Overview

Dataset statistics

Number of variables21
Number of observations50
Missing cells254
Missing cells (%)24.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.1 KiB
Average record size in memory186.6 B

Variable types

Categorical10
Text3
Numeric8

Dataset

Description부산광역시 사하구 가로수 현황에 대한 데이터로 가로수 식재 노선별 도로명, 시점, 종점, 식재거리, 수종별 나무수 등 정보를 제공합니다
Author부산광역시 사하구
URLhttps://www.data.go.kr/data/3079307/fileData.do

Alerts

기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
회화나무 is highly imbalanced (75.8%)Imbalance
칠엽수 is highly imbalanced (85.9%)Imbalance
팽나무 is highly imbalanced (78.9%)Imbalance
튤립나무 is highly imbalanced (85.9%)Imbalance
후박나무 is highly imbalanced (72.9%)Imbalance
해송 is highly imbalanced (78.9%)Imbalance
개잎갈나무 is highly imbalanced (85.9%)Imbalance
녹나무 is highly imbalanced (85.9%)Imbalance
왕벚나무 has 24 (48.0%) missing valuesMissing
은행나무 has 28 (56.0%) missing valuesMissing
느티나무 has 42 (84.0%) missing valuesMissing
양버즘 has 33 (66.0%) missing valuesMissing
이팝나무 has 43 (86.0%) missing valuesMissing
먼나무 has 42 (84.0%) missing valuesMissing
가시나무 has 42 (84.0%) missing valuesMissing
도로명주소 has unique valuesUnique
양버즘 has 1 (2.0%) zerosZeros
가시나무 has 1 (2.0%) zerosZeros

Reproduction

Analysis started2024-01-14 13:46:14.885476
Analysis finished2024-01-14 13:46:15.343337
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
부산광역시 사하구
50 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 사하구 50
100.0%

Length

2024-01-14T22:46:15.464238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:15.633908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 50
50.0%
사하구 50
50.0%

도로명주소
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-01-14T22:46:15.941245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21.5
Mean length18.02
Min length13

Characters and Unicode

Total characters901
Distinct characters74
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row부산광역시 사하구 강변대로
2nd row부산광역시 사하구 감천로
3rd row부산광역시 사하구 감천항로
4th row부산광역시 사하구 감천항로 291번길
5th row부산광역시 사하구 구평로
ValueCountFrequency (%)
부산광역시 50
28.6%
사하구 50
28.6%
다대로 7
 
4.0%
하신번영로 6
 
3.4%
다산로 4
 
2.3%
신산로 2
 
1.1%
을숙도대로 2
 
1.1%
장평로 2
 
1.1%
감천항로 2
 
1.1%
다송로 2
 
1.1%
Other values (48) 48
27.4%
2024-01-14T22:46:16.496608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
199
22.1%
하 58
 
6.4%
산 57
 
6.3%
구 52
 
5.8%
부 51
 
5.7%
사 51
 
5.7%
광 50
 
5.5%
역 50
 
5.5%
시 50
 
5.5%
로 49
 
5.4%
Other values (64) 234
26.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 637
70.7%
Space Separator 199
 
22.1%
Decimal Number 64
 
7.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
하 58
9.1%
산 57
8.9%
구 52
 
8.2%
부 51
 
8.0%
사 51
 
8.0%
광 50
 
7.8%
역 50
 
7.8%
시 50
 
7.8%
로 49
 
7.7%
번 28
 
4.4%
Other values (52) 141
22.1%
Decimal Number
ValueCountFrequency (%)
1 14
21.9%
2 11
17.2%
3 8
12.5%
0 8
12.5%
7 8
12.5%
9 5
 
7.8%
8 4
 
6.2%
6 3
 
4.7%
4 2
 
3.1%
5 1
 
1.6%
Space Separator
ValueCountFrequency (%)
199
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 637
70.7%
Common 264
29.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
하 58
9.1%
산 57
8.9%
구 52
 
8.2%
부 51
 
8.0%
사 51
 
8.0%
광 50
 
7.8%
역 50
 
7.8%
시 50
 
7.8%
로 49
 
7.7%
번 28
 
4.4%
Other values (52) 141
22.1%
Common
ValueCountFrequency (%)
199
75.4%
1 14
 
5.3%
2 11
 
4.2%
3 8
 
3.0%
0 8
 
3.0%
7 8
 
3.0%
9 5
 
1.9%
8 4
 
1.5%
6 3
 
1.1%
4 2
 
0.8%
Other values (2) 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 637
70.7%
ASCII 264
29.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
199
75.4%
1 14
 
5.3%
2 11
 
4.2%
3 8
 
3.0%
0 8
 
3.0%
7 8
 
3.0%
9 5
 
1.9%
8 4
 
1.5%
6 3
 
1.1%
4 2
 
0.8%
Other values (2) 2
 
0.8%
Hangul
ValueCountFrequency (%)
하 58
9.1%
산 57
8.9%
구 52
 
8.2%
부 51
 
8.0%
사 51
 
8.0%
광 50
 
7.8%
역 50
 
7.8%
시 50
 
7.8%
로 49
 
7.7%
번 28
 
4.4%
Other values (52) 141
22.1%

시점
Text

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-01-14T22:46:16.844825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.24
Min length3

Characters and Unicode

Total characters312
Distinct characters118
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)80.0%

Sample

1st row사상구경계
2nd row감천삼거리
3rd row화력발전소
4th row삼표산업
5th row구평동 보호수
ValueCountFrequency (%)
사상구경계 2
 
3.4%
구평고개사거리 2
 
3.4%
하단오거리 2
 
3.4%
당리 2
 
3.4%
동원아파트 2
 
3.4%
을숙도대교 2
 
3.4%
다대로교차로 2
 
3.4%
하남중학교 1
 
1.7%
신평역9번출구 1
 
1.7%
사하초교 1
 
1.7%
Other values (41) 41
70.7%
2024-01-14T22:46:17.320579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
4.8%
리 11
 
3.5%
아 11
 
3.5%
사 10
 
3.2%
교 10
 
3.2%
트 10
 
3.2%
거 9
 
2.9%
구 8
 
2.6%
파 8
 
2.6%
대 8
 
2.6%
Other values (108) 212
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 293
93.9%
Space Separator 15
 
4.8%
Uppercase Letter 2
 
0.6%
Decimal Number 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 11
 
3.8%
아 11
 
3.8%
사 10
 
3.4%
교 10
 
3.4%
트 10
 
3.4%
거 9
 
3.1%
구 8
 
2.7%
파 8
 
2.7%
대 8
 
2.7%
평 7
 
2.4%
Other values (103) 201
68.6%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
K 1
50.0%
Decimal Number
ValueCountFrequency (%)
9 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 293
93.9%
Common 17
 
5.4%
Latin 2
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 11
 
3.8%
아 11
 
3.8%
사 10
 
3.4%
교 10
 
3.4%
트 10
 
3.4%
거 9
 
3.1%
구 8
 
2.7%
파 8
 
2.7%
대 8
 
2.7%
평 7
 
2.4%
Other values (103) 201
68.6%
Common
ValueCountFrequency (%)
15
88.2%
9 1
 
5.9%
2 1
 
5.9%
Latin
ValueCountFrequency (%)
S 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 293
93.9%
ASCII 19
 
6.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
78.9%
S 1
 
5.3%
K 1
 
5.3%
9 1
 
5.3%
2 1
 
5.3%
Hangul
ValueCountFrequency (%)
리 11
 
3.8%
아 11
 
3.8%
사 10
 
3.4%
교 10
 
3.4%
트 10
 
3.4%
거 9
 
3.1%
구 8
 
2.7%
파 8
 
2.7%
대 8
 
2.7%
평 7
 
2.4%
Other values (103) 201
68.6%

종점
Text

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-01-14T22:46:17.645277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.54
Min length3

Characters and Unicode

Total characters327
Distinct characters149
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)92.0%

Sample

1st row을숙도대교
2nd row고신대병원
3rd row구평방파제
4th row감천항 부두
5th row쌍용양회
ValueCountFrequency (%)
65호광장 2
 
3.3%
아파트 2
 
3.3%
롯데캐슬아파트 2
 
3.3%
서구경계 2
 
3.3%
중앙섬유 1
 
1.6%
럭키무지개타운 1
 
1.6%
감천국민체육센터 1
 
1.6%
을숙도대교 1
 
1.6%
장림홈플러스 1
 
1.6%
감천항로 1
 
1.6%
Other values (47) 47
77.0%
2024-01-14T22:46:18.197807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
5.5%
트 12
 
3.7%
아 11
 
3.4%
파 10
 
3.1%
구 9
 
2.8%
대 9
 
2.8%
리 7
 
2.1%
신 6
 
1.8%
장 6
 
1.8%
평 6
 
1.8%
Other values (139) 233
71.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 295
90.2%
Space Separator 18
 
5.5%
Decimal Number 9
 
2.8%
Uppercase Letter 5
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 12
 
4.1%
아 11
 
3.7%
파 10
 
3.4%
구 9
 
3.1%
대 9
 
3.1%
리 7
 
2.4%
신 6
 
2.0%
장 6
 
2.0%
평 6
 
2.0%
부 5
 
1.7%
Other values (128) 214
72.5%
Decimal Number
ValueCountFrequency (%)
6 4
44.4%
5 2
22.2%
2 1
 
11.1%
4 1
 
11.1%
3 1
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
E 1
20.0%
H 1
20.0%
L 1
20.0%
N 1
20.0%
G 1
20.0%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 295
90.2%
Common 27
 
8.3%
Latin 5
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 12
 
4.1%
아 11
 
3.7%
파 10
 
3.4%
구 9
 
3.1%
대 9
 
3.1%
리 7
 
2.4%
신 6
 
2.0%
장 6
 
2.0%
평 6
 
2.0%
부 5
 
1.7%
Other values (128) 214
72.5%
Common
ValueCountFrequency (%)
18
66.7%
6 4
 
14.8%
5 2
 
7.4%
2 1
 
3.7%
4 1
 
3.7%
3 1
 
3.7%
Latin
ValueCountFrequency (%)
E 1
20.0%
H 1
20.0%
L 1
20.0%
N 1
20.0%
G 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 295
90.2%
ASCII 32
 
9.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
56.2%
6 4
 
12.5%
5 2
 
6.2%
E 1
 
3.1%
H 1
 
3.1%
L 1
 
3.1%
N 1
 
3.1%
G 1
 
3.1%
2 1
 
3.1%
4 1
 
3.1%
Hangul
ValueCountFrequency (%)
트 12
 
4.1%
아 11
 
3.7%
파 10
 
3.4%
구 9
 
3.1%
대 9
 
3.1%
리 7
 
2.4%
신 6
 
2.0%
장 6
 
2.0%
평 6
 
2.0%
부 5
 
1.7%
Other values (128) 214
72.5%

식재거리(m)
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1673.58
Minimum70
Maximum11604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-01-14T22:46:18.423010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile189
Q1338
median903.5
Q32371
95-th percentile5024.65
Maximum11604
Range11534
Interquartile range (IQR)2033

Descriptive statistics

Standard deviation2059.7107
Coefficient of variation (CV)1.2307214
Kurtosis10.223983
Mean1673.58
Median Absolute Deviation (MAD)653.5
Skewness2.7198667
Sum83679
Variance4242408
MonotonicityNot monotonic
2024-01-14T22:46:18.652187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
200 2
 
4.0%
3912 1
 
2.0%
70 1
 
2.0%
696 1
 
2.0%
760 1
 
2.0%
2040 1
 
2.0%
676 1
 
2.0%
260 1
 
2.0%
990 1
 
2.0%
1291 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
70 1
2.0%
140 1
2.0%
180 1
2.0%
200 2
4.0%
220 1
2.0%
240 1
2.0%
260 1
2.0%
300 1
2.0%
307 1
2.0%
317 1
2.0%
ValueCountFrequency (%)
11604 1
2.0%
5856 1
2.0%
5350 1
2.0%
4627 1
2.0%
4330 1
2.0%
3912 1
2.0%
3524 1
2.0%
3146 1
2.0%
3072 1
2.0%
2869 1
2.0%

왕벚나무
Real number (ℝ)

MISSING 

Distinct25
Distinct (%)96.2%
Missing24
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean154.96154
Minimum4
Maximum799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-01-14T22:46:18.923860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.5
Q126.5
median56
Q3232
95-th percentile564
Maximum799
Range795
Interquartile range (IQR)205.5

Descriptive statistics

Standard deviation205.72418
Coefficient of variation (CV)1.3275822
Kurtosis3.2294463
Mean154.96154
Median Absolute Deviation (MAD)40.5
Skewness1.8997752
Sum4029
Variance42322.438
MonotonicityNot monotonic
2024-01-14T22:46:19.140944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
564 2
 
4.0%
4 1
 
2.0%
33 1
 
2.0%
290 1
 
2.0%
16 1
 
2.0%
54 1
 
2.0%
320 1
 
2.0%
92 1
 
2.0%
208 1
 
2.0%
87 1
 
2.0%
Other values (15) 15
30.0%
(Missing) 24
48.0%
ValueCountFrequency (%)
4 1
2.0%
5 1
2.0%
15 1
2.0%
16 1
2.0%
21 1
2.0%
25 1
2.0%
26 1
2.0%
28 1
2.0%
29 1
2.0%
33 1
2.0%
ValueCountFrequency (%)
799 1
2.0%
564 2
4.0%
320 1
2.0%
290 1
2.0%
254 1
2.0%
240 1
2.0%
208 1
2.0%
128 1
2.0%
99 1
2.0%
92 1
2.0%

은행나무
Real number (ℝ)

MISSING 

Distinct21
Distinct (%)95.5%
Missing28
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean100.36364
Minimum2
Maximum657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-01-14T22:46:19.295643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.6
Q131
median63.5
Q3105.25
95-th percentile200.25
Maximum657
Range655
Interquartile range (IQR)74.25

Descriptive statistics

Standard deviation137.3274
Coefficient of variation (CV)1.3682983
Kurtosis13.743263
Mean100.36364
Median Absolute Deviation (MAD)39.5
Skewness3.4333891
Sum2208
Variance18858.814
MonotonicityNot monotonic
2024-01-14T22:46:19.463024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 2
 
4.0%
657 1
 
2.0%
186 1
 
2.0%
97 1
 
2.0%
14 1
 
2.0%
61 1
 
2.0%
66 1
 
2.0%
83 1
 
2.0%
111 1
 
2.0%
15 1
 
2.0%
Other values (11) 11
 
22.0%
(Missing) 28
56.0%
ValueCountFrequency (%)
2 2
4.0%
14 1
2.0%
15 1
2.0%
24 1
2.0%
29 1
2.0%
37 1
2.0%
38 1
2.0%
42 1
2.0%
50 1
2.0%
61 1
2.0%
ValueCountFrequency (%)
657 1
2.0%
201 1
2.0%
186 1
2.0%
182 1
2.0%
111 1
2.0%
106 1
2.0%
103 1
2.0%
102 1
2.0%
97 1
2.0%
83 1
2.0%

느티나무
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)87.5%
Missing42
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean46.125
Minimum2
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-01-14T22:46:19.670790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.45
Q118.75
median35
Q357.5
95-th percentile114.9
Maximum124
Range122
Interquartile range (IQR)38.75

Descriptive statistics

Standard deviation42.953255
Coefficient of variation (CV)0.93123589
Kurtosis0.14419012
Mean46.125
Median Absolute Deviation (MAD)19.5
Skewness1.1007902
Sum369
Variance1844.9821
MonotonicityNot monotonic
2024-01-14T22:46:19.816938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
35 2
 
4.0%
22 1
 
2.0%
124 1
 
2.0%
9 1
 
2.0%
98 1
 
2.0%
44 1
 
2.0%
2 1
 
2.0%
(Missing) 42
84.0%
ValueCountFrequency (%)
2 1
2.0%
9 1
2.0%
22 1
2.0%
35 2
4.0%
44 1
2.0%
98 1
2.0%
124 1
2.0%
ValueCountFrequency (%)
124 1
2.0%
98 1
2.0%
44 1
2.0%
35 2
4.0%
22 1
2.0%
9 1
2.0%
2 1
2.0%

양버즘
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)94.1%
Missing33
Missing (%)66.0%
Infinite0
Infinite (%)0.0%
Mean93.352941
Minimum0
Maximum464
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-01-14T22:46:19.999045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q133
median65
Q3111
95-th percentile258.4
Maximum464
Range464
Interquartile range (IQR)78

Descriptive statistics

Standard deviation109.24396
Coefficient of variation (CV)1.1702251
Kurtosis8.6689324
Mean93.352941
Median Absolute Deviation (MAD)39
Skewness2.6907227
Sum1587
Variance11934.243
MonotonicityNot monotonic
2024-01-14T22:46:20.193392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
65 2
 
4.0%
207 1
 
2.0%
52 1
 
2.0%
49 1
 
2.0%
0 1
 
2.0%
111 1
 
2.0%
120 1
 
2.0%
8 1
 
2.0%
104 1
 
2.0%
33 1
 
2.0%
Other values (6) 6
 
12.0%
(Missing) 33
66.0%
ValueCountFrequency (%)
0 1
2.0%
3 1
2.0%
8 1
2.0%
30 1
2.0%
33 1
2.0%
49 1
2.0%
52 1
2.0%
54 1
2.0%
65 2
4.0%
100 1
2.0%
ValueCountFrequency (%)
464 1
2.0%
207 1
2.0%
122 1
2.0%
120 1
2.0%
111 1
2.0%
104 1
2.0%
100 1
2.0%
65 2
4.0%
54 1
2.0%
52 1
2.0%

이팝나무
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing43
Missing (%)86.0%
Infinite0
Infinite (%)0.0%
Mean108.57143
Minimum19
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-01-14T22:46:20.352560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile19.3
Q120.5
median30
Q3195
95-th percentile266.2
Maximum280
Range261
Interquartile range (IQR)174.5

Descriptive statistics

Standard deviation113.34293
Coefficient of variation (CV)1.043948
Kurtosis-1.613635
Mean108.57143
Median Absolute Deviation (MAD)11
Skewness0.72877448
Sum760
Variance12846.619
MonotonicityNot monotonic
2024-01-14T22:46:20.515779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
156 1
 
2.0%
20 1
 
2.0%
19 1
 
2.0%
234 1
 
2.0%
21 1
 
2.0%
280 1
 
2.0%
30 1
 
2.0%
(Missing) 43
86.0%
ValueCountFrequency (%)
19 1
2.0%
20 1
2.0%
21 1
2.0%
30 1
2.0%
156 1
2.0%
234 1
2.0%
280 1
2.0%
ValueCountFrequency (%)
280 1
2.0%
234 1
2.0%
156 1
2.0%
30 1
2.0%
21 1
2.0%
20 1
2.0%
19 1
2.0%

회화나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
48 
7
 
2

Length

Max length4
Median length4
Mean length3.88
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 48
96.0%
7 2
 
4.0%

Length

2024-01-14T22:46:20.656101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:20.803180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 48
96.0%
7 2
 
4.0%

칠엽수
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
49 
3
 
1

Length

Max length4
Median length4
Mean length3.94
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 49
98.0%
3 1
 
2.0%

Length

2024-01-14T22:46:20.976067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:21.149425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 49
98.0%
3 1
 
2.0%

팽나무
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
47 
48
 
1
576
 
1
24
 
1

Length

Max length4
Median length4
Mean length3.9
Min length2

Unique

Unique3 ?
Unique (%)6.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 47
94.0%
48 1
 
2.0%
576 1
 
2.0%
24 1
 
2.0%

Length

2024-01-14T22:46:21.370486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:21.622902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 47
94.0%
48 1
 
2.0%
576 1
 
2.0%
24 1
 
2.0%

튤립나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
49 
15
 
1

Length

Max length4
Median length4
Mean length3.96
Min length2

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 49
98.0%
15 1
 
2.0%

Length

2024-01-14T22:46:21.824537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:21.998241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 49
98.0%
15 1
 
2.0%

후박나무
Categorical

IMBALANCE 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
45 
44
 
1
40
 
1
14
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.78
Min length1

Unique

Unique5 ?
Unique (%)10.0%

Sample

1st row<NA>
2nd row<NA>
3rd row44
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 45
90.0%
44 1
 
2.0%
40 1
 
2.0%
14 1
 
2.0%
3 1
 
2.0%
54 1
 
2.0%

Length

2024-01-14T22:46:22.205216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:22.395728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 45
90.0%
44 1
 
2.0%
40 1
 
2.0%
14 1
 
2.0%
3 1
 
2.0%
54 1
 
2.0%

먼나무
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)100.0%
Missing42
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean89.25
Minimum2
Maximum192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-01-14T22:46:22.554447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.9
Q119.75
median96
Q3133.25
95-th percentile184.3
Maximum192
Range190
Interquartile range (IQR)113.5

Descriptive statistics

Standard deviation72.403137
Coefficient of variation (CV)0.81123963
Kurtosis-1.6054222
Mean89.25
Median Absolute Deviation (MAD)74.5
Skewness0.14386368
Sum714
Variance5242.2143
MonotonicityNot monotonic
2024-01-14T22:46:22.761288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
117 1
 
2.0%
2 1
 
2.0%
21 1
 
2.0%
75 1
 
2.0%
16 1
 
2.0%
170 1
 
2.0%
121 1
 
2.0%
192 1
 
2.0%
(Missing) 42
84.0%
ValueCountFrequency (%)
2 1
2.0%
16 1
2.0%
21 1
2.0%
75 1
2.0%
117 1
2.0%
121 1
2.0%
170 1
2.0%
192 1
2.0%
ValueCountFrequency (%)
192 1
2.0%
170 1
2.0%
121 1
2.0%
117 1
2.0%
75 1
2.0%
21 1
2.0%
16 1
2.0%
2 1
2.0%

해송
Categorical

IMBALANCE 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
47 
712
 
1
21
 
1
1
 
1

Length

Max length4
Median length4
Mean length3.88
Min length1

Unique

Unique3 ?
Unique (%)6.0%

Sample

1st row<NA>
2nd row<NA>
3rd row712
4th row<NA>
5th row21

Common Values

ValueCountFrequency (%)
<NA> 47
94.0%
712 1
 
2.0%
21 1
 
2.0%
1 1
 
2.0%

Length

2024-01-14T22:46:22.976400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:23.143731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 47
94.0%
712 1
 
2.0%
21 1
 
2.0%
1 1
 
2.0%

가시나무
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)100.0%
Missing42
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean75.125
Minimum0
Maximum394
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-01-14T22:46:23.285199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.95
Q119.25
median24
Q355.5
95-th percentile281.3
Maximum394
Range394
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation130.72811
Coefficient of variation (CV)1.7401413
Kurtosis7.315688
Mean75.125
Median Absolute Deviation (MAD)15.5
Skewness2.672815
Sum601
Variance17089.839
MonotonicityNot monotonic
2024-01-14T22:46:23.480913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
394 1
 
2.0%
72 1
 
2.0%
0 1
 
2.0%
20 1
 
2.0%
50 1
 
2.0%
27 1
 
2.0%
21 1
 
2.0%
17 1
 
2.0%
(Missing) 42
84.0%
ValueCountFrequency (%)
0 1
2.0%
17 1
2.0%
20 1
2.0%
21 1
2.0%
27 1
2.0%
50 1
2.0%
72 1
2.0%
394 1
2.0%
ValueCountFrequency (%)
394 1
2.0%
72 1
2.0%
50 1
2.0%
27 1
2.0%
21 1
2.0%
20 1
2.0%
17 1
2.0%
0 1
2.0%

개잎갈나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
49 
1
 
1

Length

Max length4
Median length4
Mean length3.94
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 49
98.0%
1 1
 
2.0%

Length

2024-01-14T22:46:23.704100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:23.843889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 49
98.0%
1 1
 
2.0%

녹나무
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
49 
8
 
1

Length

Max length4
Median length4
Mean length3.94
Min length1

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 49
98.0%
8 1
 
2.0%

Length

2024-01-14T22:46:23.988027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:24.122602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 49
98.0%
8 1
 
2.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-20
50 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-20
2nd row2023-12-20
3rd row2023-12-20
4th row2023-12-20
5th row2023-12-20

Common Values

ValueCountFrequency (%)
2023-12-20 50
100.0%

Length

2024-01-14T22:46:24.262771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:46:24.710604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-20 50
100.0%

Sample

기관명도로명주소시점종점식재거리(m)왕벚나무은행나무느티나무양버즘이팝나무회화나무칠엽수팽나무튤립나무후박나무먼나무해송가시나무개잎갈나무녹나무데이터기준일자
0부산광역시 사하구부산광역시 사하구 강변대로사상구경계을숙도대교3912799<NA><NA><NA>156<NA><NA><NA><NA><NA><NA><NA>394<NA><NA>2023-12-20
1부산광역시 사하구부산광역시 사하구 감천로감천삼거리고신대병원217658182<NA>8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2023-12-20
2부산광역시 사하구부산광역시 사하구 감천항로화력발전소구평방파제5350<NA>4222<NA><NA><NA><NA><NA><NA>44117712<NA><NA><NA>2023-12-20
3부산광역시 사하구부산광역시 사하구 감천항로 291번길삼표산업감천항 부두200<NA><NA><NA><NA>20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2023-12-20
4부산광역시 사하구부산광역시 사하구 구평로구평동 보호수쌍용양회433<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>21<NA><NA><NA>2023-12-20
5부산광역시 사하구부산광역시 사하구 낙동남로하단오거리65호광장3072128<NA>124<NA><NA>7<NA>48<NA><NA><NA><NA>72<NA>82023-12-20
6부산광역시 사하구부산광역시 사하구 낙동남로1233번길낙동강문화관을숙도문화회관30734<NA>35<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2023-12-20
7부산광역시 사하구부산광역시 사하구 낙동대로사상구경계대티터널5856<NA>657<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2023-12-20
8부산광역시 사하구부산광역시 사하구 다대로을숙도대교당리동주민센터116045641869104<NA><NA><NA>576<NA>40<NA><NA>0<NA><NA>2023-12-20
9부산광역시 사하구부산광역시 사하구 다대로 170번길성일여고한신실리텍976<NA><NA><NA>122<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2023-12-20
기관명도로명주소시점종점식재거리(m)왕벚나무은행나무느티나무양버즘이팝나무회화나무칠엽수팽나무튤립나무후박나무먼나무해송가시나무개잎갈나무녹나무데이터기준일자
40부산광역시 사하구부산광역시 사하구 제석로당리 동원아파트부신일과학고교243692<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2023-12-20
41부산광역시 사하구부산광역시 사하구 하신번영로하단SK뷰아파트장림포구433032083<NA>111<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2023-12-20
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