Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory124.0 B

Variable types

Categorical7
Text3
Numeric4

Dataset

Description도시숲가로수관리시스템에서 관리하는 시군구별 / 구간별 가로수 현황에 대한 데이터입니다. 해당 가로수 수목에 대한 흉고직경과 위치정보가 포함되어있습니다.
URLhttps://www.data.go.kr/data/15120900/fileData.do

Alerts

가로내녹지유형명 has constant value ""Constant
도로변녹지유형명 has constant value ""Constant
기후대구분명 has constant value ""Constant
입지명 has constant value ""Constant
좌표계코드 has constant value ""Constant
지역X좌표 is highly overall correlated with 시군구명High correlation
지역Y좌표 is highly overall correlated with 시군구명High correlation
시군구별가로수번호 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 지역X좌표 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 23:12:45.947203
Analysis finished2023-12-12 23:12:49.841278
Duration3.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
충청북도 흥덕구
2021 
충청북도 청주시 서원구
1166 
경상북도 안동시
1118 
충청북도 청주시 상당구
1108 
충청북도 청주시 청원구
1038 
Other values (19)
3549 

Length

Max length12
Median length11
Mean length9.8174
Min length7

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row충청북도 청주시 서원구
2nd row대구광역시 달성군
3rd row충청북도 청주시 상당구
4th row강원특별자치도 춘천시
5th row충청북도 흥덕구

Common Values

ValueCountFrequency (%)
충청북도 흥덕구 2021
20.2%
충청북도 청주시 서원구 1166
11.7%
경상북도 안동시 1118
11.2%
충청북도 청주시 상당구 1108
11.1%
충청북도 청주시 청원구 1038
10.4%
강원특별자치도 춘천시 597
 
6.0%
대구광역시 달성군 561
 
5.6%
전라남도 나주시 433
 
4.3%
경기도 평택시 410
 
4.1%
경기도 부천시 404
 
4.0%
Other values (14) 1144
11.4%

Length

2023-12-13T08:12:49.917644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충청북도 5333
22.2%
청주시 3312
13.8%
흥덕구 2021
 
8.4%
경기도 1560
 
6.5%
서원구 1166
 
4.8%
경상북도 1118
 
4.6%
안동시 1118
 
4.6%
상당구 1108
 
4.6%
청원구 1038
 
4.3%
수원시 743
 
3.1%
Other values (24) 5538
23.0%

도로구간명
Text

Distinct803
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:12:50.210969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.2057
Min length3

Characters and Unicode

Total characters42057
Distinct characters243
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique264 ?
Unique (%)2.6%

Sample

1st row모충로
2nd row비슬로468길
3rd row호미로
4th row한치로
5th row직지대로
ValueCountFrequency (%)
2순환로 494
 
4.9%
1순환로 456
 
4.6%
직지대로 184
 
1.8%
강남로 183
 
1.8%
무심서로 183
 
1.8%
가로수로 176
 
1.8%
논공로 162
 
1.6%
경인로 115
 
1.1%
경동로 112
 
1.1%
경북대로 111
 
1.1%
Other values (793) 7824
78.2%
2023-12-13T08:12:50.738475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 9444
22.5%
길 2275
 
5.4%
1 1646
 
3.9%
번 1350
 
3.2%
2 1238
 
2.9%
환 1218
 
2.9%
순 1218
 
2.9%
대 1027
 
2.4%
서 581
 
1.4%
수 529
 
1.3%
Other values (233) 21531
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36382
86.5%
Decimal Number 5675
 
13.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 9444
26.0%
길 2275
 
6.3%
번 1350
 
3.7%
환 1218
 
3.3%
순 1218
 
3.3%
대 1027
 
2.8%
서 581
 
1.6%
수 529
 
1.5%
경 509
 
1.4%
산 507
 
1.4%
Other values (223) 17724
48.7%
Decimal Number
ValueCountFrequency (%)
1 1646
29.0%
2 1238
21.8%
3 519
 
9.1%
4 406
 
7.2%
0 394
 
6.9%
5 325
 
5.7%
6 309
 
5.4%
9 303
 
5.3%
7 276
 
4.9%
8 259
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36382
86.5%
Common 5675
 
13.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 9444
26.0%
길 2275
 
6.3%
번 1350
 
3.7%
환 1218
 
3.3%
순 1218
 
3.3%
대 1027
 
2.8%
서 581
 
1.6%
수 529
 
1.5%
경 509
 
1.4%
산 507
 
1.4%
Other values (223) 17724
48.7%
Common
ValueCountFrequency (%)
1 1646
29.0%
2 1238
21.8%
3 519
 
9.1%
4 406
 
7.2%
0 394
 
6.9%
5 325
 
5.7%
6 309
 
5.4%
9 303
 
5.3%
7 276
 
4.9%
8 259
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36382
86.5%
ASCII 5675
 
13.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 9444
26.0%
길 2275
 
6.3%
번 1350
 
3.7%
환 1218
 
3.3%
순 1218
 
3.3%
대 1027
 
2.8%
서 581
 
1.6%
수 529
 
1.5%
경 509
 
1.4%
산 507
 
1.4%
Other values (223) 17724
48.7%
ASCII
ValueCountFrequency (%)
1 1646
29.0%
2 1238
21.8%
3 519
 
9.1%
4 406
 
7.2%
0 394
 
6.9%
5 325
 
5.7%
6 309
 
5.4%
9 303
 
5.3%
7 276
 
4.9%
8 259
 
4.6%

구간시점명
Text

Distinct707
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:12:51.142008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length10.3136
Min length3

Characters and Unicode

Total characters103136
Distinct characters189
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique203 ?
Unique (%)2.0%

Sample

1st row개신동 458-1
2nd row비슬로468길
3rd row용암동 792
4th row남산면 방곡리 139-6도
5th row정봉동 30-1
ValueCountFrequency (%)
충청북도 670
 
2.9%
청주시 670
 
2.9%
율량동 654
 
2.8%
내덕동 558
 
2.4%
166-1 494
 
2.1%
복대동 477
 
2.1%
565-24 462
 
2.0%
봉명동 327
 
1.4%
가경동 297
 
1.3%
흥덕구 282
 
1.2%
Other values (947) 18276
78.9%
2023-12-13T08:12:51.667196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13167
 
12.8%
동 8108
 
7.9%
1 7851
 
7.6%
- 7095
 
6.9%
2 4686
 
4.5%
6 4497
 
4.4%
5 4073
 
3.9%
3 3915
 
3.8%
0 3157
 
3.1%
4 2975
 
2.9%
Other values (179) 43612
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44991
43.6%
Decimal Number 37685
36.5%
Space Separator 13167
 
12.8%
Dash Punctuation 7095
 
6.9%
Open Punctuation 99
 
0.1%
Close Punctuation 99
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8108
 
18.0%
청 1719
 
3.8%
도 1699
 
3.8%
리 1474
 
3.3%
구 1301
 
2.9%
봉 959
 
2.1%
덕 872
 
1.9%
충 866
 
1.9%
내 851
 
1.9%
북 835
 
1.9%
Other values (165) 26307
58.5%
Decimal Number
ValueCountFrequency (%)
1 7851
20.8%
2 4686
12.4%
6 4497
11.9%
5 4073
10.8%
3 3915
10.4%
0 3157
8.4%
4 2975
 
7.9%
8 2262
 
6.0%
7 2227
 
5.9%
9 2042
 
5.4%
Space Separator
ValueCountFrequency (%)
13167
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7095
100.0%
Open Punctuation
ValueCountFrequency (%)
( 99
100.0%
Close Punctuation
ValueCountFrequency (%)
) 99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58145
56.4%
Hangul 44991
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8108
 
18.0%
청 1719
 
3.8%
도 1699
 
3.8%
리 1474
 
3.3%
구 1301
 
2.9%
봉 959
 
2.1%
덕 872
 
1.9%
충 866
 
1.9%
내 851
 
1.9%
북 835
 
1.9%
Other values (165) 26307
58.5%
Common
ValueCountFrequency (%)
13167
22.6%
1 7851
13.5%
- 7095
12.2%
2 4686
 
8.1%
6 4497
 
7.7%
5 4073
 
7.0%
3 3915
 
6.7%
0 3157
 
5.4%
4 2975
 
5.1%
8 2262
 
3.9%
Other values (4) 4467
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58145
56.4%
Hangul 44991
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13167
22.6%
1 7851
13.5%
- 7095
12.2%
2 4686
 
8.1%
6 4497
 
7.7%
5 4073
 
7.0%
3 3915
 
6.7%
0 3157
 
5.4%
4 2975
 
5.1%
8 2262
 
3.9%
Other values (4) 4467
 
7.7%
Hangul
ValueCountFrequency (%)
동 8108
 
18.0%
청 1719
 
3.8%
도 1699
 
3.8%
리 1474
 
3.3%
구 1301
 
2.9%
봉 959
 
2.1%
덕 872
 
1.9%
충 866
 
1.9%
내 851
 
1.9%
북 835
 
1.9%
Other values (165) 26307
58.5%

구간종점명
Text

Distinct725
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T08:12:52.052573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length10.6483
Min length3

Characters and Unicode

Total characters106483
Distinct characters191
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique219 ?
Unique (%)2.2%

Sample

1st row남주동 636-3
2nd row옥포면 기세리 212-3
3rd row용암동 230-5
4th row남산면 방곡리 116-4대
5th row정봉동 32-1
ValueCountFrequency (%)
충청북도 670
 
2.8%
청주시 670
 
2.8%
내덕동 596
 
2.5%
복대동 498
 
2.1%
지북동 495
 
2.0%
366-5 494
 
2.0%
573-2 456
 
1.9%
봉명동 277
 
1.1%
율량동 273
 
1.1%
가경동 254
 
1.1%
Other values (997) 19498
80.6%
2023-12-13T08:12:52.568692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14181
 
13.3%
동 8171
 
7.7%
- 7248
 
6.8%
1 6638
 
6.2%
2 5756
 
5.4%
3 4688
 
4.4%
5 4413
 
4.1%
6 3454
 
3.2%
4 3441
 
3.2%
9 2607
 
2.4%
Other values (181) 45886
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46383
43.6%
Decimal Number 38483
36.1%
Space Separator 14181
 
13.3%
Dash Punctuation 7248
 
6.8%
Open Punctuation 94
 
0.1%
Close Punctuation 94
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8171
 
17.6%
리 2109
 
4.5%
청 1611
 
3.5%
도 1520
 
3.3%
북 1511
 
3.3%
구 1438
 
3.1%
대 1120
 
2.4%
면 1016
 
2.2%
정 884
 
1.9%
시 858
 
1.8%
Other values (167) 26145
56.4%
Decimal Number
ValueCountFrequency (%)
1 6638
17.2%
2 5756
15.0%
3 4688
12.2%
5 4413
11.5%
6 3454
9.0%
4 3441
8.9%
9 2607
 
6.8%
7 2568
 
6.7%
8 2496
 
6.5%
0 2422
 
6.3%
Space Separator
ValueCountFrequency (%)
14181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7248
100.0%
Open Punctuation
ValueCountFrequency (%)
( 94
100.0%
Close Punctuation
ValueCountFrequency (%)
) 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60100
56.4%
Hangul 46383
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8171
 
17.6%
리 2109
 
4.5%
청 1611
 
3.5%
도 1520
 
3.3%
북 1511
 
3.3%
구 1438
 
3.1%
대 1120
 
2.4%
면 1016
 
2.2%
정 884
 
1.9%
시 858
 
1.8%
Other values (167) 26145
56.4%
Common
ValueCountFrequency (%)
14181
23.6%
- 7248
12.1%
1 6638
11.0%
2 5756
9.6%
3 4688
 
7.8%
5 4413
 
7.3%
6 3454
 
5.7%
4 3441
 
5.7%
9 2607
 
4.3%
7 2568
 
4.3%
Other values (4) 5106
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60100
56.4%
Hangul 46383
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14181
23.6%
- 7248
12.1%
1 6638
11.0%
2 5756
9.6%
3 4688
 
7.8%
5 4413
 
7.3%
6 3454
 
5.7%
4 3441
 
5.7%
9 2607
 
4.3%
7 2568
 
4.3%
Other values (4) 5106
 
8.5%
Hangul
ValueCountFrequency (%)
동 8171
 
17.6%
리 2109
 
4.5%
청 1611
 
3.5%
도 1520
 
3.3%
북 1511
 
3.3%
구 1438
 
3.1%
대 1120
 
2.4%
면 1016
 
2.2%
정 884
 
1.9%
시 858
 
1.8%
Other values (167) 26145
56.4%

수종명
Categorical

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은행나무
2998 
이팝나무
1308 
벚나무
1183 
느티나무
1035 
버즘나무
993 
Other values (25)
2483 

Length

Max length8
Median length4
Mean length3.9783
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row은행나무
2nd row왕벚나무
3rd row이팝나무
4th row은행나무
5th row버즘나무

Common Values

ValueCountFrequency (%)
은행나무 2998
30.0%
이팝나무 1308
13.1%
벚나무 1183
 
11.8%
느티나무 1035
 
10.3%
버즘나무 993
 
9.9%
미분류 453
 
4.5%
메타세쿼이아 401
 
4.0%
양버즘나무 304
 
3.0%
플라타너스 259
 
2.6%
단풍나무 257
 
2.6%
Other values (20) 809
 
8.1%

Length

2023-12-13T08:12:52.767905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은행나무 2998
30.0%
이팝나무 1308
13.1%
벚나무 1183
 
11.8%
느티나무 1035
 
10.3%
버즘나무 993
 
9.9%
미분류 453
 
4.5%
메타세쿼이아 401
 
4.0%
양버즘나무 304
 
3.0%
플라타너스 259
 
2.6%
단풍나무 257
 
2.6%
Other values (20) 809
 
8.1%

수목흉고직경
Real number (ℝ)

Distinct80
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.67944
Minimum2.3
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:12:52.924088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile7
Q110
median15
Q325
95-th percentile50
Maximum120
Range117.7
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.809175
Coefficient of variation (CV)0.70170567
Kurtosis5.017463
Mean19.67944
Median Absolute Deviation (MAD)5
Skewness2.0057041
Sum196794.4
Variance190.6933
MonotonicityNot monotonic
2023-12-13T08:12:53.089475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 1990
19.9%
20.0 1018
 
10.2%
15.0 952
 
9.5%
12.0 674
 
6.7%
30.0 450
 
4.5%
8.0 382
 
3.8%
7.0 366
 
3.7%
14.0 300
 
3.0%
25.0 279
 
2.8%
11.0 271
 
2.7%
Other values (70) 3318
33.2%
ValueCountFrequency (%)
2.3 1
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
3.0 9
 
0.1%
4.0 10
 
0.1%
5.0 211
2.1%
6.0 45
 
0.4%
7.0 366
3.7%
8.0 382
3.8%
9.0 87
 
0.9%
ValueCountFrequency (%)
120.0 1
 
< 0.1%
100.0 13
0.1%
90.0 15
0.1%
80.0 22
0.2%
79.0 1
 
< 0.1%
78.0 5
 
0.1%
75.0 18
0.2%
74.0 2
 
< 0.1%
73.0 2
 
< 0.1%
72.0 7
 
0.1%

가로내녹지유형명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 10000
100.0%

Length

2023-12-13T08:12:53.238361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:12:53.347018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 10000
100.0%

도로변녹지유형명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 10000
100.0%

Length

2023-12-13T08:12:53.442241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:12:53.524026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 10000
100.0%

기후대구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 10000
100.0%

Length

2023-12-13T08:12:53.610265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:12:53.696427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 10000
100.0%

입지명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 10000
100.0%

Length

2023-12-13T08:12:53.789564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:12:53.890887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 10000
100.0%

지역X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct9987
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251245.51
Minimum173910.6
Maximum376775.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:12:53.987540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum173910.6
5-th percentile180454.09
Q1237256.79
median242881.16
Q3246312.17
95-th percentile354333.08
Maximum376775.14
Range202864.54
Interquartile range (IQR)9055.3738

Descriptive statistics

Standard deviation49318.178
Coefficient of variation (CV)0.19629477
Kurtosis0.13607369
Mean251245.51
Median Absolute Deviation (MAD)4842.8912
Skewness0.93527488
Sum2.5124551 × 109
Variance2.4322827 × 109
MonotonicityNot monotonic
2023-12-13T08:12:54.153238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180571.6 6
 
0.1%
181026.7 3
 
< 0.1%
207219.006 2
 
< 0.1%
180257.1 2
 
< 0.1%
183104.821 2
 
< 0.1%
181042.2 2
 
< 0.1%
180571.7 2
 
< 0.1%
181346.7 2
 
< 0.1%
241774.892 1
 
< 0.1%
239359.6853 1
 
< 0.1%
Other values (9977) 9977
99.8%
ValueCountFrequency (%)
173910.6 1
< 0.1%
173947.3 1
< 0.1%
173949.2 1
< 0.1%
173949.4 1
< 0.1%
173952.5 1
< 0.1%
173953.2 1
< 0.1%
173953.8 1
< 0.1%
173962.6 1
< 0.1%
173973.4 1
< 0.1%
173983.9 1
< 0.1%
ValueCountFrequency (%)
376775.1379 1
< 0.1%
376747.1702 1
< 0.1%
376715.4718 1
< 0.1%
376682.5259 1
< 0.1%
376599.1629 1
< 0.1%
376578.5799 1
< 0.1%
376567.3703 1
< 0.1%
360939.3846 1
< 0.1%
360923.3013 1
< 0.1%
360902.6896 1
< 0.1%

지역Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct9948
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452164.4
Minimum267950.6
Maximum622035.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:12:54.303280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum267950.6
5-th percentile348302.48
Q1444242.4
median448413.42
Q3452969.76
95-th percentile581690.29
Maximum622035.78
Range354085.18
Interquartile range (IQR)8727.365

Descriptive statistics

Standard deviation65884.915
Coefficient of variation (CV)0.14571009
Kurtosis1.5941077
Mean452164.4
Median Absolute Deviation (MAD)4336.9107
Skewness-0.41550561
Sum4.521644 × 109
Variance4.340822 × 109
MonotonicityNot monotonic
2023-12-13T08:12:54.479685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
268608.1 7
 
0.1%
268391.9 7
 
0.1%
268391.8 6
 
0.1%
268399.7 6
 
0.1%
268399.6 4
 
< 0.1%
268399.5 4
 
< 0.1%
268611.2 4
 
< 0.1%
268632.2 4
 
< 0.1%
268157.2 4
 
< 0.1%
268175.3 3
 
< 0.1%
Other values (9938) 9951
99.5%
ValueCountFrequency (%)
267950.6 1
< 0.1%
267960.4 1
< 0.1%
267964.1 1
< 0.1%
267973.8 1
< 0.1%
267978.2 1
< 0.1%
267980.1 1
< 0.1%
267984.2 1
< 0.1%
267985.2 1
< 0.1%
268000.0 1
< 0.1%
268015.7 1
< 0.1%
ValueCountFrequency (%)
622035.7825 1
< 0.1%
622014.0134 1
< 0.1%
622006.0397 1
< 0.1%
622004.3995 1
< 0.1%
621981.2632 1
< 0.1%
621950.1271 1
< 0.1%
621940.2904 1
< 0.1%
621934.049 1
< 0.1%
621886.3351 1
< 0.1%
621883.6177 1
< 0.1%

좌표계코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
EPSG:5186
10000 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEPSG:5186
2nd rowEPSG:5186
3rd rowEPSG:5186
4th rowEPSG:5186
5th rowEPSG:5186

Common Values

ValueCountFrequency (%)
EPSG:5186 10000
100.0%

Length

2023-12-13T08:12:54.627228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:12:54.719172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
epsg:5186 10000
100.0%

시군구별가로수번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9810
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50545.285
Minimum11
Maximum213997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T08:12:54.838743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile6108.75
Q119218.75
median37826.5
Q354971.75
95-th percentile147404.65
Maximum213997
Range213986
Interquartile range (IQR)35753

Descriptive statistics

Standard deviation44724.674
Coefficient of variation (CV)0.88484364
Kurtosis0.78326095
Mean50545.285
Median Absolute Deviation (MAD)17827.5
Skewness1.3398856
Sum5.0545285 × 108
Variance2.0002965 × 109
MonotonicityNot monotonic
2023-12-13T08:12:54.966927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23167 3
 
< 0.1%
52584 2
 
< 0.1%
6049 2
 
< 0.1%
23318 2
 
< 0.1%
13591 2
 
< 0.1%
23390 2
 
< 0.1%
16151 2
 
< 0.1%
10386 2
 
< 0.1%
24332 2
 
< 0.1%
28197 2
 
< 0.1%
Other values (9800) 9979
99.8%
ValueCountFrequency (%)
11 1
< 0.1%
16 1
< 0.1%
21 1
< 0.1%
27 1
< 0.1%
35 1
< 0.1%
44 1
< 0.1%
55 1
< 0.1%
478 1
< 0.1%
479 1
< 0.1%
481 1
< 0.1%
ValueCountFrequency (%)
213997 1
< 0.1%
213995 1
< 0.1%
213992 1
< 0.1%
184562 1
< 0.1%
184550 1
< 0.1%
184535 1
< 0.1%
184497 1
< 0.1%
184475 1
< 0.1%
184390 1
< 0.1%
184375 1
< 0.1%

Interactions

2023-12-13T08:12:49.009442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:47.318647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:47.753871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:48.219073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:49.129957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:47.436705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:47.861079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:48.341334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:49.286752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:47.559294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:47.989324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:48.472963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:49.406072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:47.655783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:48.106024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:12:48.608102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:12:55.038056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명수종명수목흉고직경지역X좌표지역Y좌표시군구별가로수번호
시군구명1.0000.8190.5040.9890.9840.955
수종명0.8191.0000.6080.7820.7590.783
수목흉고직경0.5040.6081.0000.3530.3230.371
지역X좌표0.9890.7820.3531.0000.8930.729
지역Y좌표0.9840.7590.3230.8931.0000.731
시군구별가로수번호0.9550.7830.3710.7290.7311.000
2023-12-13T08:12:55.126636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수종명시군구명
수종명1.0000.334
시군구명0.3341.000
2023-12-13T08:12:55.197500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수목흉고직경지역X좌표지역Y좌표시군구별가로수번호시군구명수종명
수목흉고직경1.000-0.052-0.1040.0120.2100.238
지역X좌표-0.0521.000-0.270-0.3270.8400.448
지역Y좌표-0.104-0.2701.0000.2990.9010.402
시군구별가로수번호0.012-0.3270.2991.0000.7730.373
시군구명0.2100.8400.9010.7731.0000.334
수종명0.2380.4480.4020.3730.3341.000

Missing values

2023-12-13T08:12:49.559928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:12:49.755597image/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.

Sample

시군구명도로구간명구간시점명구간종점명수종명수목흉고직경가로내녹지유형명도로변녹지유형명기후대구분명입지명지역X좌표지역Y좌표좌표계코드시군구별가로수번호
32974충청북도 청주시 서원구모충로개신동 458-1남주동 636-3은행나무9.0기타기타기타기타241774.892447506.907EPSG:518628927
70421대구광역시 달성군비슬로468길비슬로468길옥포면 기세리 212-3왕벚나무28.0기타기타기타기타333422.0856355513.4091EPSG:5186126605
24604충청북도 청주시 상당구호미로용암동 792용암동 230-5이팝나무7.0기타기타기타기타246113.77447673.0811EPSG:518635272
11585강원특별자치도 춘천시한치로남산면 방곡리 139-6도남산면 방곡리 116-4대은행나무30.0기타기타기타기타261751.11579627.31EPSG:51861197
24249충청북도 흥덕구직지대로정봉동 30-1정봉동 32-1버즘나무34.0기타기타기타기타237104.3856449891.4689EPSG:518647980
37935경상북도 안동시강남로수상동 530-3정상동 92벚나무25.0기타기타기타기타355964.0683440894.2159EPSG:518614621
15108충청북도 청주시 서원구무심서로평촌동 130-1원평동 75-1메타세쿼이아32.0기타기타기타기타243291.8317447774.8296EPSG:518628862
12742충청북도 청주시 상당구1순환로내덕동 565-24내덕동 573-2이팝나무7.0기타기타기타기타246528.2313447875.9183EPSG:518640253
61360경기도 부천시계남로상동 525-7중동 1097-2백합나무21.0기타기타기타기타180231.513545265.395EPSG:5186123304
49762충청북도 흥덕구짐대로60번길복대동 3159복대동 3153은행나무12.0기타기타기타기타238320.339448161.06EPSG:518619231
시군구명도로구간명구간시점명구간종점명수종명수목흉고직경가로내녹지유형명도로변녹지유형명기후대구분명입지명지역X좌표지역Y좌표좌표계코드시군구별가로수번호
45193충청북도 흥덕구가로수로1312번길복대동 1898복대동 1898미분류22.0기타기타기타기타239211.658448262.567EPSG:518618626
69548대구광역시 달성군사문진로사문진로화원읍 천내리 156-3은행나무18.0기타기타기타기타335035.3493357472.4392EPSG:5186124371
46562충청북도 청주시 청원구직지대로832번길우암동 1207우암동 1202-1버즘나무24.0기타기타기타기타243254.7295450032.8206EPSG:518642717
28692충청북도 청주시 서원구내수동로복대동 969복대동 1915미분류20.0기타기타기타기타241034.003448461.383EPSG:518622692
30381충청북도 청주시 서원구수곡로산남동 274-3산남동 315-8느티나무27.0기타기타기타기타242349.949446500.183EPSG:518627051
33074충청북도 흥덕구죽천로복대동 3101복대동 230-2느티나무7.0기타기타기타기타239129.5656448905.9873EPSG:518647094
55858경기도 수원시 장안구경수대로호계동 859-61대석수동 704-1잡은행나무10.0기타기타기타기타198948.5524149.982EPSG:5186182966
33780충청북도 흥덕구월명로봉명동 2197봉명동 25-1은행나무12.0기타기타기타기타240596.4386449769.2292EPSG:518637775
24367충청북도 청주시 청원구율봉로사천동 630-87율량동 673-8이팝나무12.0기타기타기타기타244640.742452127.622EPSG:518658613
1925충청북도 청주시 상당구산성로충청북도 청주시 상당구 탑동 327-5충청북도 청주시 상당구 낭성면 관정리 142-13이팝나무7.0기타기타기타기타245725.0453448927.7342EPSG:518640581