Overview

Dataset statistics

Number of variables21
Number of observations30
Missing cells76
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory188.4 B

Variable types

Text3
Numeric7
Categorical9
Unsupported1
DateTime1

Dataset

Description2023년 1월 1일 기준 부산광역시 남구 가로수 현황 노선에 따른 구간, 식재거리, 수목별 식재 본수, 수종별 현황 등 자료 제공
URLhttps://www.data.go.kr/data/15047953/fileData.do

Alerts

구군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
이팝나무 is highly imbalanced (73.5%)Imbalance
메타세콰이아 is highly imbalanced (78.9%)Imbalance
후박나무 is highly imbalanced (73.5%)Imbalance
먼나무 is highly imbalanced (68.6%)Imbalance
해송 is highly imbalanced (78.9%)Imbalance
가시나무 is highly imbalanced (73.5%)Imbalance
녹나무 is highly imbalanced (73.5%)Imbalance
기타나무 is highly imbalanced (68.6%)Imbalance
왕벚나무 has 17 (56.7%) missing valuesMissing
은행나무 has 20 (66.7%) missing valuesMissing
느티나무 has 9 (30.0%) missing valuesMissing
팽나무 has 30 (100.0%) missing valuesMissing
위치명 has unique valuesUnique
팽나무 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 21:29:56.928343
Analysis finished2023-12-12 21:29:57.197948
Duration0.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위치명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T06:29:57.321541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length14.2
Min length12

Characters and Unicode

Total characters426
Distinct characters71
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

Unique30 ?
Unique (%)100.0%

Sample

1st row부산광역시 남구 북항로
2nd row부산광역시 남구 분포로
3rd row부산광역시 남구 석포로
4th row부산광역시 남구 수영로
5th row부산광역시 남구 수영로325번길
ValueCountFrequency (%)
부산광역시 30
32.6%
남구 30
32.6%
이기대공원로 1
 
1.1%
lg메트로 1
 
1.1%
내 1
 
1.1%
도로 1
 
1.1%
백운포로 1
 
1.1%
유엔로 1
 
1.1%
유엔평화로 1
 
1.1%
전포대로 1
 
1.1%
Other values (24) 24
26.1%
2023-12-13T06:29:57.662539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63
14.8%
남 33
 
7.7%
부 31
 
7.3%
산 31
 
7.3%
구 31
 
7.3%
로 30
 
7.0%
시 30
 
7.0%
역 30
 
7.0%
광 30
 
7.0%
대 8
 
1.9%
Other values (61) 109
25.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
79.8%
Space Separator 63
 
14.8%
Decimal Number 21
 
4.9%
Uppercase Letter 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 33
9.7%
부 31
 
9.1%
산 31
 
9.1%
구 31
 
9.1%
로 30
 
8.8%
시 30
 
8.8%
역 30
 
8.8%
광 30
 
8.8%
대 8
 
2.4%
길 8
 
2.4%
Other values (49) 78
22.9%
Decimal Number
ValueCountFrequency (%)
1 4
19.0%
3 4
19.0%
9 3
14.3%
4 3
14.3%
0 2
9.5%
2 2
9.5%
5 1
 
4.8%
7 1
 
4.8%
8 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
L 1
50.0%
Space Separator
ValueCountFrequency (%)
63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
79.8%
Common 84
 
19.7%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 33
9.7%
부 31
 
9.1%
산 31
 
9.1%
구 31
 
9.1%
로 30
 
8.8%
시 30
 
8.8%
역 30
 
8.8%
광 30
 
8.8%
대 8
 
2.4%
길 8
 
2.4%
Other values (49) 78
22.9%
Common
ValueCountFrequency (%)
63
75.0%
1 4
 
4.8%
3 4
 
4.8%
9 3
 
3.6%
4 3
 
3.6%
0 2
 
2.4%
2 2
 
2.4%
5 1
 
1.2%
7 1
 
1.2%
8 1
 
1.2%
Latin
ValueCountFrequency (%)
G 1
50.0%
L 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
79.8%
ASCII 86
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63
73.3%
1 4
 
4.7%
3 4
 
4.7%
9 3
 
3.5%
4 3
 
3.5%
0 2
 
2.3%
2 2
 
2.3%
G 1
 
1.2%
L 1
 
1.2%
5 1
 
1.2%
Other values (2) 2
 
2.3%
Hangul
ValueCountFrequency (%)
남 33
9.7%
부 31
 
9.1%
산 31
 
9.1%
구 31
 
9.1%
로 30
 
8.8%
시 30
 
8.8%
역 30
 
8.8%
광 30
 
8.8%
대 8
 
2.4%
길 8
 
2.4%
Other values (49) 78
22.9%

위도
Real number (ℝ)

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.130943
Minimum35.107848
Maximum35.150968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:29:57.797283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.107848
5-th percentile35.108937
Q135.125445
median35.132263
Q335.137781
95-th percentile35.148454
Maximum35.150968
Range0.0431198
Interquartile range (IQR)0.012336275

Descriptive statistics

Standard deviation0.011616394
Coefficient of variation (CV)0.00033065989
Kurtosis-0.1953614
Mean35.130943
Median Absolute Deviation (MAD)0.0063794
Skewness-0.44590828
Sum1053.9283
Variance0.0001349406
MonotonicityNot monotonic
2023-12-13T06:29:57.926801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
35.1374733 2
 
6.7%
35.1509676 2
 
6.7%
35.1256321 2
 
6.7%
35.1078478 2
 
6.7%
35.110269 1
 
3.3%
35.1326633 1
 
3.3%
35.138214 1
 
3.3%
35.1224975 1
 
3.3%
35.12875 1
 
3.3%
35.1446469 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
35.1078478 2
6.7%
35.110269 1
3.3%
35.1116339 1
3.3%
35.1187264 1
3.3%
35.1224975 1
3.3%
35.1245608 1
3.3%
35.1253822 1
3.3%
35.1256321 2
6.7%
35.1261348 1
3.3%
35.12875 1
3.3%
ValueCountFrequency (%)
35.1509676 2
6.7%
35.1453817 1
3.3%
35.1446469 1
3.3%
35.1407797 1
3.3%
35.1382222 1
3.3%
35.138214 1
3.3%
35.1378835 1
3.3%
35.1374733 2
6.7%
35.1372612 1
3.3%
35.1372058 1
3.3%

경도
Real number (ℝ)

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.09099
Minimum129.06454
Maximum129.11349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:29:58.045300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.06454
5-th percentile129.06633
Q1129.08064
median129.09204
Q3129.10234
95-th percentile129.11205
Maximum129.11349
Range0.048946
Interquartile range (IQR)0.02170525

Descriptive statistics

Standard deviation0.015702614
Coefficient of variation (CV)0.00012163989
Kurtosis-1.0412896
Mean129.09099
Median Absolute Deviation (MAD)0.0112815
Skewness-0.28201837
Sum3872.7298
Variance0.00024657208
MonotonicityNot monotonic
2023-12-13T06:29:58.171806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
129.108076 2
 
6.7%
129.091161 2
 
6.7%
129.08786 2
 
6.7%
129.111646 2
 
6.7%
129.09223 1
 
3.3%
129.109668 1
 
3.3%
129.0833397 1
 
3.3%
129.093611 1
 
3.3%
129.101913 1
 
3.3%
129.066099 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
129.06454 1
3.3%
129.066099 1
3.3%
129.066617 1
3.3%
129.067146 1
3.3%
129.067449 1
3.3%
129.069536 1
3.3%
129.077996 1
3.3%
129.079924 1
3.3%
129.082781 1
3.3%
129.0833397 1
3.3%
ValueCountFrequency (%)
129.113486 1
3.3%
129.112378 1
3.3%
129.111646 2
6.7%
129.109668 1
3.3%
129.108076 2
6.7%
129.102487 1
3.3%
129.101913 1
3.3%
129.101075 1
3.3%
129.099828 1
3.3%
129.096084 1
3.3%

구간시점
Text

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T06:29:58.352395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.0666667
Min length3

Characters and Unicode

Total characters182
Distinct characters91
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

Unique24 ?
Unique (%)80.0%

Sample

1st row현대오일뱅크
2nd row분포교
3rd row유엔교차로
4th row문현교차로
5th row경성대부경대역2번출구
ValueCountFrequency (%)
성모병원앞 2
 
6.2%
문현교차로 2
 
6.2%
경동메르빌아파트 2
 
6.2%
lg메트로시티 1
 
3.1%
한국은행 1
 
3.1%
광안대교입구 1
 
3.1%
용당하늘채아파트 1
 
3.1%
대연고등학교 1
 
3.1%
문현혁신지구 1
 
3.1%
문현태영데시앙아파트 1
 
3.1%
Other values (19) 19
59.4%
2023-12-13T06:29:58.664532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 10
 
5.5%
트 7
 
3.8%
교 7
 
3.8%
경 6
 
3.3%
현 6
 
3.3%
아 6
 
3.3%
파 6
 
3.3%
원 5
 
2.7%
로 5
 
2.7%
성 4
 
2.2%
Other values (81) 120
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 175
96.2%
Decimal Number 3
 
1.6%
Space Separator 2
 
1.1%
Uppercase Letter 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 10
 
5.7%
트 7
 
4.0%
교 7
 
4.0%
경 6
 
3.4%
현 6
 
3.4%
아 6
 
3.4%
파 6
 
3.4%
원 5
 
2.9%
로 5
 
2.9%
성 4
 
2.3%
Other values (75) 113
64.6%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
4 1
33.3%
9 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 175
96.2%
Common 5
 
2.7%
Latin 2
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 10
 
5.7%
트 7
 
4.0%
교 7
 
4.0%
경 6
 
3.4%
현 6
 
3.4%
아 6
 
3.4%
파 6
 
3.4%
원 5
 
2.9%
로 5
 
2.9%
성 4
 
2.3%
Other values (75) 113
64.6%
Common
ValueCountFrequency (%)
2
40.0%
2 1
20.0%
4 1
20.0%
9 1
20.0%
Latin
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 175
96.2%
ASCII 7
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 10
 
5.7%
트 7
 
4.0%
교 7
 
4.0%
경 6
 
3.4%
현 6
 
3.4%
아 6
 
3.4%
파 6
 
3.4%
원 5
 
2.9%
로 5
 
2.9%
성 4
 
2.3%
Other values (75) 113
64.6%
ASCII
ValueCountFrequency (%)
2
28.6%
2 1
14.3%
4 1
14.3%
9 1
14.3%
L 1
14.3%
G 1
14.3%

구간종점
Text

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T06:29:58.849407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length5.7333333
Min length3

Characters and Unicode

Total characters172
Distinct characters91
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)83.3%

Sample

1st row감만시민부두
2nd row용호지구대
3rd row감만홈플러스
4th row대남교차로
5th row맥도날드
ValueCountFrequency (%)
성모병원앞 3
 
9.1%
일원 2
 
6.1%
수목원일원 2
 
6.1%
lg메트로시티 1
 
3.0%
감만시민부두 1
 
3.0%
기술보증기금 1
 
3.0%
장백아파트 1
 
3.0%
황령터널입구 1
 
3.0%
용당하늘채아파트 1
 
3.0%
문전역 1
 
3.0%
Other values (19) 19
57.6%
2023-12-13T06:29:59.134863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 12
 
7.0%
트 8
 
4.7%
아 7
 
4.1%
파 7
 
4.1%
일 6
 
3.5%
구 6
 
3.5%
앞 4
 
2.3%
대 4
 
2.3%
문 4
 
2.3%
성 3
 
1.7%
Other values (81) 111
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 165
95.9%
Uppercase Letter 4
 
2.3%
Space Separator 3
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 12
 
7.3%
트 8
 
4.8%
아 7
 
4.2%
파 7
 
4.2%
일 6
 
3.6%
구 6
 
3.6%
앞 4
 
2.4%
대 4
 
2.4%
문 4
 
2.4%
성 3
 
1.8%
Other values (76) 104
63.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
25.0%
S 1
25.0%
L 1
25.0%
G 1
25.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 165
95.9%
Latin 4
 
2.3%
Common 3
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 12
 
7.3%
트 8
 
4.8%
아 7
 
4.2%
파 7
 
4.2%
일 6
 
3.6%
구 6
 
3.6%
앞 4
 
2.4%
대 4
 
2.4%
문 4
 
2.4%
성 3
 
1.8%
Other values (76) 104
63.0%
Latin
ValueCountFrequency (%)
K 1
25.0%
S 1
25.0%
L 1
25.0%
G 1
25.0%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 165
95.9%
ASCII 7
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 12
 
7.3%
트 8
 
4.8%
아 7
 
4.2%
파 7
 
4.2%
일 6
 
3.6%
구 6
 
3.6%
앞 4
 
2.4%
대 4
 
2.4%
문 4
 
2.4%
성 3
 
1.8%
Other values (76) 104
63.0%
ASCII
ValueCountFrequency (%)
3
42.9%
K 1
 
14.3%
S 1
 
14.3%
L 1
 
14.3%
G 1
 
14.3%

식재거리(km)
Real number (ℝ)

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1496.2667
Minimum23
Maximum4600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:29:59.246600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile122.5
Q1389.25
median1355
Q32112.5
95-th percentile4185
Maximum4600
Range4577
Interquartile range (IQR)1723.25

Descriptive statistics

Standard deviation1297.1503
Coefficient of variation (CV)0.86692454
Kurtosis0.37992189
Mean1496.2667
Median Absolute Deviation (MAD)945.5
Skewness1.0457003
Sum44888
Variance1682598.9
MonotonicityNot monotonic
2023-12-13T06:29:59.367556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1400 2
 
6.7%
2500 2
 
6.7%
1500 2
 
6.7%
2150 1
 
3.3%
1350 1
 
3.3%
23 1
 
3.3%
800 1
 
3.3%
2000 1
 
3.3%
150 1
 
3.3%
340 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
23 1
3.3%
100 1
3.3%
150 1
3.3%
200 1
3.3%
286 1
3.3%
300 1
3.3%
340 1
3.3%
369 1
3.3%
450 1
3.3%
500 1
3.3%
ValueCountFrequency (%)
4600 1
3.3%
4500 1
3.3%
3800 1
3.3%
3500 1
3.3%
2750 1
3.3%
2500 2
6.7%
2150 1
3.3%
2000 1
3.3%
1600 1
3.3%
1500 2
6.7%

총합계
Real number (ℝ)

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.23333
Minimum6
Maximum901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:29:59.464196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile23
Q164.75
median134
Q3289.25
95-th percentile616.7
Maximum901
Range895
Interquartile range (IQR)224.5

Descriptive statistics

Standard deviation223.2402
Coefficient of variation (CV)1.0182767
Kurtosis1.7829544
Mean219.23333
Median Absolute Deviation (MAD)96.5
Skewness1.477898
Sum6577
Variance49836.185
MonotonicityNot monotonic
2023-12-13T06:29:59.566592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
23 2
 
6.7%
25 2
 
6.7%
901 1
 
3.3%
150 1
 
3.3%
64 1
 
3.3%
237 1
 
3.3%
67 1
 
3.3%
44 1
 
3.3%
6 1
 
3.3%
92 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
6 1
3.3%
23 2
6.7%
25 2
6.7%
44 1
3.3%
61 1
3.3%
64 1
3.3%
67 1
3.3%
77 1
3.3%
79 1
3.3%
92 1
3.3%
ValueCountFrequency (%)
901 1
3.3%
632 1
3.3%
598 1
3.3%
576 1
3.3%
466 1
3.3%
414 1
3.3%
375 1
3.3%
291 1
3.3%
284 1
3.3%
237 1
3.3%

왕벚나무
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)92.3%
Missing17
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean153.61538
Minimum4
Maximum537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:29:59.650048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.4
Q123
median92
Q3157
95-th percentile526.8
Maximum537
Range533
Interquartile range (IQR)134

Descriptive statistics

Standard deviation183.376
Coefficient of variation (CV)1.1937346
Kurtosis1.1542455
Mean153.61538
Median Absolute Deviation (MAD)69
Skewness1.5198724
Sum1997
Variance33626.756
MonotonicityNot monotonic
2023-12-13T06:29:59.733675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
23 2
 
6.7%
50 1
 
3.3%
59 1
 
3.3%
92 1
 
3.3%
13 1
 
3.3%
4 1
 
3.3%
98 1
 
3.3%
130 1
 
3.3%
520 1
 
3.3%
157 1
 
3.3%
Other values (2) 2
 
6.7%
(Missing) 17
56.7%
ValueCountFrequency (%)
4 1
3.3%
13 1
3.3%
23 2
6.7%
50 1
3.3%
59 1
3.3%
92 1
3.3%
98 1
3.3%
130 1
3.3%
157 1
3.3%
291 1
3.3%
ValueCountFrequency (%)
537 1
3.3%
520 1
3.3%
291 1
3.3%
157 1
3.3%
130 1
3.3%
98 1
3.3%
92 1
3.3%
59 1
3.3%
50 1
3.3%
23 2
6.7%

은행나무
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)100.0%
Missing20
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean188.2
Minimum39
Maximum413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:29:59.809976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile39.9
Q166
median127
Q3332.25
95-th percentile411.65
Maximum413
Range374
Interquartile range (IQR)266.25

Descriptive statistics

Standard deviation155.87944
Coefficient of variation (CV)0.82826483
Kurtosis-1.5317745
Mean188.2
Median Absolute Deviation (MAD)87
Skewness0.62372788
Sum1882
Variance24298.4
MonotonicityNot monotonic
2023-12-13T06:29:59.891797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
413 1
 
3.3%
79 1
 
3.3%
370 1
 
3.3%
175 1
 
3.3%
72 1
 
3.3%
410 1
 
3.3%
41 1
 
3.3%
39 1
 
3.3%
219 1
 
3.3%
64 1
 
3.3%
(Missing) 20
66.7%
ValueCountFrequency (%)
39 1
3.3%
41 1
3.3%
64 1
3.3%
72 1
3.3%
79 1
3.3%
175 1
3.3%
219 1
3.3%
370 1
3.3%
410 1
3.3%
413 1
3.3%
ValueCountFrequency (%)
413 1
3.3%
410 1
3.3%
370 1
3.3%
219 1
3.3%
175 1
3.3%
79 1
3.3%
72 1
3.3%
64 1
3.3%
41 1
3.3%
39 1
3.3%

느티나무
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)90.5%
Missing9
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean45.904762
Minimum1
Maximum218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T06:29:59.982837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q117
median28
Q353
95-th percentile133
Maximum218
Range217
Interquartile range (IQR)36

Descriptive statistics

Standard deviation52.222509
Coefficient of variation (CV)1.1376273
Kurtosis5.2608087
Mean45.904762
Median Absolute Deviation (MAD)23
Skewness2.1292139
Sum964
Variance2727.1905
MonotonicityNot monotonic
2023-12-13T06:30:00.076799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 2
 
6.7%
25 2
 
6.7%
4 1
 
3.3%
18 1
 
3.3%
28 1
 
3.3%
44 1
 
3.3%
6 1
 
3.3%
51 1
 
3.3%
61 1
 
3.3%
133 1
 
3.3%
Other values (9) 9
30.0%
(Missing) 9
30.0%
ValueCountFrequency (%)
1 2
6.7%
3 1
3.3%
4 1
3.3%
6 1
3.3%
17 1
3.3%
18 1
3.3%
19 1
3.3%
25 2
6.7%
28 1
3.3%
36 1
3.3%
ValueCountFrequency (%)
218 1
3.3%
133 1
3.3%
103 1
3.3%
76 1
3.3%
61 1
3.3%
53 1
3.3%
51 1
3.3%
44 1
3.3%
42 1
3.3%
36 1
3.3%

이팝나무
Categorical

IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
28 
162
 
1
208
 
1

Length

Max length4
Median length4
Mean length3.9333333
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row162
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 28
93.3%
162 1
 
3.3%
208 1
 
3.3%

Length

2023-12-13T06:30:00.168835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:00.247056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
93.3%
162 1
 
3.3%
208 1
 
3.3%

메타세콰이아
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
29 
45
 
1

Length

Max length4
Median length4
Mean length3.9333333
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
96.7%
45 1
 
3.3%

Length

2023-12-13T06:30:00.350349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:00.436935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
96.7%
45 1
 
3.3%

팽나무
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

후박나무
Categorical

IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
28 
27
 
1
72
 
1

Length

Max length4
Median length4
Mean length3.8666667
Min length2

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 28
93.3%
27 1
 
3.3%
72 1
 
3.3%

Length

2023-12-13T06:30:00.526987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:00.624850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
93.3%
27 1
 
3.3%
72 1
 
3.3%

먼나무
Categorical

IMBALANCE 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
27 
43
 
1
28
 
1
39
 
1

Length

Max length4
Median length4
Mean length3.8
Min length2

Unique

Unique3 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 27
90.0%
43 1
 
3.3%
28 1
 
3.3%
39 1
 
3.3%

Length

2023-12-13T06:30:00.715498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:00.805634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
90.0%
43 1
 
3.3%
28 1
 
3.3%
39 1
 
3.3%

해송
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
29 
127
 
1

Length

Max length4
Median length4
Mean length3.9666667
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
96.7%
127 1
 
3.3%

Length

2023-12-13T06:30:00.889449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:00.966981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
96.7%
127 1
 
3.3%

가시나무
Categorical

IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
28 
154
 
1
172
 
1

Length

Max length4
Median length4
Mean length3.9333333
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row154
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 28
93.3%
154 1
 
3.3%
172 1
 
3.3%

Length

2023-12-13T06:30:01.059433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:01.141138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
93.3%
154 1
 
3.3%
172 1
 
3.3%

녹나무
Categorical

IMBALANCE 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
28 
10
 
1
33
 
1

Length

Max length4
Median length4
Mean length3.8666667
Min length2

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 28
93.3%
10 1
 
3.3%
33 1
 
3.3%

Length

2023-12-13T06:30:01.229563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:01.323926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
93.3%
10 1
 
3.3%
33 1
 
3.3%

기타나무
Categorical

IMBALANCE 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
27 
493
 
1
42
 
1
79
 
1

Length

Max length4
Median length4
Mean length3.8333333
Min length2

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row493
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 27
90.0%
493 1
 
3.3%
42 1
 
3.3%
79 1
 
3.3%

Length

2023-12-13T06:30:01.411037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:01.504242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
90.0%
493 1
 
3.3%
42 1
 
3.3%
79 1
 
3.3%

구군명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
부산광역시 남구
30 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 남구 30
100.0%

Length

2023-12-13T06:30:01.596261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:30:01.674149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 30
50.0%
남구 30
50.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-04-01 00:00:00
Maximum2023-04-01 00:00:00
2023-12-13T06:30:01.739449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:30:01.830148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Sample

위치명위도경도구간시점구간종점식재거리(km)총합계왕벚나무은행나무느티나무이팝나무메타세콰이아팽나무후박나무먼나무해송가시나무녹나무기타나무구군명데이터기준일자
0부산광역시 남구 북항로35.110269129.09223현대오일뱅크감만시민부두215090150<NA>42162<NA><NA><NA><NA><NA>154<NA>493부산광역시 남구2023-04-01
1부산광역시 남구 분포로35.125382129.112378분포교용호지구대150013559<NA>76<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
2부산광역시 남구 석포로35.129896129.091878유엔교차로감만홈플러스1400218<NA><NA>218<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
3부산광역시 남구 수영로35.137261129.069536문현교차로대남교차로3500466<NA>41353<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
4부산광역시 남구 수영로325번길35.138222129.101075경성대부경대역2번출구맥도날드900103<NA><NA>103<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
5부산광역시 남구 신선로35.137473129.10807649호광장오륙도아파트460063292791208<NA><NA>2743<NA>17210<NA>부산광역시 남구2023-04-01
6부산광역시 남구 신선로447번길35.126135129.096084수목원평화공원 일원4507713<NA>36<NA><NA><NA><NA>28<NA><NA><NA><NA>부산광역시 남구2023-04-01
7부산광역시 남구 우암로35.111634129.082781감만현대아파트문현교차로380037543701<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
8부산광역시 남구 황령대로319번가길35.150968129.091161경동메르빌아파트대우그린아파트10009898<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
9부산광역시 남구 남구청1길35.125632129.08786남구청일원남구청일원20025<NA><NA>25<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
위치명위도경도구간시점구간종점식재거리(km)총합계왕벚나무은행나무느티나무이팝나무메타세콰이아팽나무후박나무먼나무해송가시나무녹나무기타나무구군명데이터기준일자
20부산광역시 남구 유엔평화로35.134886129.092208대연역수목원일원1500133<NA><NA>133<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
21부산광역시 남구 이기대공원로35.124561129.113486이기대어귀삼거리성모병원앞4500598537<NA>61<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
22부산광역시 남구 전포대로35.137884129.067449문현교차로부산진구경계140092<NA>4151<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
23부산광역시 남구 전포대로40번길35.14078129.067146문현태영데시앙아파트문현여중1006<NA><NA>6<NA><NA><NA><NA><NA><NA><NA><NA><NA>부산광역시 남구2023-04-01
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