Overview

Dataset statistics

Number of variables9
Number of observations68
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory76.9 B

Variable types

Categorical2
Text2
Numeric3
Boolean2

Dataset

Description제주특별자치도 서귀포시 관내 이용가능 오름에 관련한 데이터로 읍면동,오름명, 소재지, 위도, 경도, 주차장, 화장실, 표고(m) 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3082952/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
읍면동 is highly overall correlated with 경도High correlation
주차장 is highly overall correlated with 화장실High correlation
화장실 is highly overall correlated with 주차장High correlation
오름명 has unique valuesUnique
표고(m) has 2 (2.9%) zerosZeros

Reproduction

Analysis started2023-12-12 21:43:22.041348
Analysis finished2023-12-12 21:43:23.560991
Duration1.52 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size676.0 B
성산읍
15 
표선면
14 
남원읍
12 
안덕면
11 
대정읍
4 
Other values (6)
12 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)2.9%

Sample

1st row대정읍
2nd row대정읍
3rd row대정읍
4th row대정읍
5th row남원읍

Common Values

ValueCountFrequency (%)
성산읍 15
22.1%
표선면 14
20.6%
남원읍 12
17.6%
안덕면 11
16.2%
대정읍 4
 
5.9%
서홍동 3
 
4.4%
중문동 3
 
4.4%
효돈동 2
 
2.9%
영천동 2
 
2.9%
동홍동 1
 
1.5%

Length

2023-12-13T06:43:23.635545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성산읍 15
22.1%
표선면 14
20.6%
남원읍 12
17.6%
안덕면 11
16.2%
대정읍 4
 
5.9%
서홍동 3
 
4.4%
중문동 3
 
4.4%
효돈동 2
 
2.9%
영천동 2
 
2.9%
동홍동 1
 
1.5%

오름명
Text

UNIQUE 

Distinct68
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-13T06:43:23.871016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.5588235
Min length2

Characters and Unicode

Total characters310
Distinct characters124
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)100.0%

Sample

1st row송악산(절울이)
2nd row섯알오름
3rd row가시오름
4th row녹남봉
5th row물영아리
ValueCountFrequency (%)
송악산(절울이 1
 
1.5%
따라비오름 1
 
1.5%
광해악(넙게오름 1
 
1.5%
논오름 1
 
1.5%
금산 1
 
1.5%
용머리 1
 
1.5%
왕이메 1
 
1.5%
섯알오름 1
 
1.5%
달산봉 1
 
1.5%
민오름 1
 
1.5%
Other values (58) 58
85.3%
2023-12-13T06:43:24.247729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
오 32
 
10.3%
름 32
 
10.3%
산 14
 
4.5%
( 12
 
3.9%
봉 12
 
3.9%
) 12
 
3.9%
악 8
 
2.6%
이 8
 
2.6%
리 6
 
1.9%
미 6
 
1.9%
Other values (114) 168
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 284
91.6%
Open Punctuation 12
 
3.9%
Close Punctuation 12
 
3.9%
Dash Punctuation 1
 
0.3%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
오 32
 
11.3%
름 32
 
11.3%
산 14
 
4.9%
봉 12
 
4.2%
악 8
 
2.8%
이 8
 
2.8%
리 6
 
2.1%
미 6
 
2.1%
대 5
 
1.8%
남 5
 
1.8%
Other values (110) 156
54.9%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 284
91.6%
Common 26
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
오 32
 
11.3%
름 32
 
11.3%
산 14
 
4.9%
봉 12
 
4.2%
악 8
 
2.8%
이 8
 
2.8%
리 6
 
2.1%
미 6
 
2.1%
대 5
 
1.8%
남 5
 
1.8%
Other values (110) 156
54.9%
Common
ValueCountFrequency (%)
( 12
46.2%
) 12
46.2%
- 1
 
3.8%
1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 284
91.6%
ASCII 26
 
8.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
오 32
 
11.3%
름 32
 
11.3%
산 14
 
4.9%
봉 12
 
4.2%
악 8
 
2.8%
이 8
 
2.8%
리 6
 
2.1%
미 6
 
2.1%
대 5
 
1.8%
남 5
 
1.8%
Other values (110) 156
54.9%
ASCII
ValueCountFrequency (%)
( 12
46.2%
) 12
46.2%
- 1
 
3.8%
1
 
3.8%

소재지
Text

Distinct63
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size676.0 B
2023-12-13T06:43:24.483915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length24.485294
Min length20

Characters and Unicode

Total characters1665
Distinct characters73
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

Unique58 ?
Unique (%)85.3%

Sample

1st row제주특별자치도 서귀포시 대정읍 상모리 산 2
2nd row제주특별자치도 서귀포시 대정읍 상모리 1618
3rd row제주특별자치도 서귀포시 대정읍 동일리 1209
4th row제주특별자치도 서귀포시 대정읍 신도리 1304
5th row제주특별자치도 서귀포시 남원읍 수망리 산 189
ValueCountFrequency (%)
제주특별자치도 68
18.8%
서귀포시 68
18.8%
산 33
 
9.1%
성산읍 15
 
4.2%
표선면 14
 
3.9%
남원읍 12
 
3.3%
안덕면 11
 
3.0%
1 5
 
1.4%
2-1 5
 
1.4%
가시리 5
 
1.4%
Other values (91) 125
34.6%
2023-12-13T06:43:24.824522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
293
 
17.6%
시 75
 
4.5%
서 73
 
4.4%
포 69
 
4.1%
도 69
 
4.1%
제 68
 
4.1%
주 68
 
4.1%
귀 68
 
4.1%
치 68
 
4.1%
자 68
 
4.1%
Other values (63) 746
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1153
69.2%
Space Separator 293
 
17.6%
Decimal Number 200
 
12.0%
Dash Punctuation 19
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 75
 
6.5%
서 73
 
6.3%
포 69
 
6.0%
도 69
 
6.0%
제 68
 
5.9%
주 68
 
5.9%
귀 68
 
5.9%
치 68
 
5.9%
자 68
 
5.9%
별 68
 
5.9%
Other values (51) 459
39.8%
Decimal Number
ValueCountFrequency (%)
1 54
27.0%
2 33
16.5%
3 21
 
10.5%
4 18
 
9.0%
9 15
 
7.5%
6 13
 
6.5%
5 12
 
6.0%
7 12
 
6.0%
0 11
 
5.5%
8 11
 
5.5%
Space Separator
ValueCountFrequency (%)
293
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1153
69.2%
Common 512
30.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 75
 
6.5%
서 73
 
6.3%
포 69
 
6.0%
도 69
 
6.0%
제 68
 
5.9%
주 68
 
5.9%
귀 68
 
5.9%
치 68
 
5.9%
자 68
 
5.9%
별 68
 
5.9%
Other values (51) 459
39.8%
Common
ValueCountFrequency (%)
293
57.2%
1 54
 
10.5%
2 33
 
6.4%
3 21
 
4.1%
- 19
 
3.7%
4 18
 
3.5%
9 15
 
2.9%
6 13
 
2.5%
5 12
 
2.3%
7 12
 
2.3%
Other values (2) 22
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1153
69.2%
ASCII 512
30.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
293
57.2%
1 54
 
10.5%
2 33
 
6.4%
3 21
 
4.1%
- 19
 
3.7%
4 18
 
3.5%
9 15
 
2.9%
6 13
 
2.5%
5 12
 
2.3%
7 12
 
2.3%
Other values (2) 22
 
4.3%
Hangul
ValueCountFrequency (%)
시 75
 
6.5%
서 73
 
6.3%
포 69
 
6.0%
도 69
 
6.0%
제 68
 
5.9%
주 68
 
5.9%
귀 68
 
5.9%
치 68
 
5.9%
자 68
 
5.9%
별 68
 
5.9%
Other values (51) 459
39.8%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.34259
Minimum33.199409
Maximum33.47865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T06:43:25.239129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.199409
5-th percentile33.237736
Q133.285285
median33.344964
Q333.386685
95-th percentile33.457759
Maximum33.47865
Range0.2792407
Interquartile range (IQR)0.10140095

Descriptive statistics

Standard deviation0.070739897
Coefficient of variation (CV)0.0021216077
Kurtosis-0.75400484
Mean33.34259
Median Absolute Deviation (MAD)0.04636805
Skewness-0.019565893
Sum2267.2961
Variance0.0050041331
MonotonicityNot monotonic
2023-12-13T06:43:25.362469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3708091 6
 
8.8%
33.3616666 6
 
8.8%
33.342895 5
 
7.4%
33.4080291 2
 
2.9%
33.4786495 2
 
2.9%
33.4377473 2
 
2.9%
33.1994088 1
 
1.5%
33.2360818 1
 
1.5%
33.2340447 1
 
1.5%
33.340233 1
 
1.5%
Other values (41) 41
60.3%
ValueCountFrequency (%)
33.1994088 1
1.5%
33.2042537 1
1.5%
33.2340447 1
1.5%
33.2360818 1
1.5%
33.240809 1
1.5%
33.2442794 1
1.5%
33.2469709 1
1.5%
33.2514303 1
1.5%
33.2533626 1
1.5%
33.2537823 1
1.5%
ValueCountFrequency (%)
33.4786495 2
2.9%
33.4656758 1
1.5%
33.4584473 1
1.5%
33.456482 1
1.5%
33.4398545 1
1.5%
33.4377473 2
2.9%
33.4355716 1
1.5%
33.4349078 1
1.5%
33.4310244 1
1.5%
33.4135945 1
1.5%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.62148
Minimum126.19548
Maximum126.94253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T06:43:25.481440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.19548
5-th percentile126.28835
Q1126.45362
median126.64941
Q3126.81039
95-th percentile126.88406
Maximum126.94253
Range0.7470521
Interquartile range (IQR)0.3567748

Descriptive statistics

Standard deviation0.20691235
Coefficient of variation (CV)0.0016341015
Kurtosis-1.1098525
Mean126.62148
Median Absolute Deviation (MAD)0.17056475
Skewness-0.36952761
Sum8610.2606
Variance0.042812721
MonotonicityNot monotonic
2023-12-13T06:43:25.609609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6935196 6
 
8.8%
126.5291666 6
 
8.8%
126.6494133 5
 
7.4%
126.8271111 2
 
2.9%
126.8840616 2
 
2.9%
126.7917034 2
 
2.9%
126.2907797 1
 
1.5%
126.2870485 1
 
1.5%
126.3141969 1
 
1.5%
126.374785 1
 
1.5%
Other values (41) 41
60.3%
ValueCountFrequency (%)
126.1954827 1
1.5%
126.2490546 1
1.5%
126.281676 1
1.5%
126.2870485 1
1.5%
126.2907797 1
1.5%
126.3046682 1
1.5%
126.3141969 1
1.5%
126.3162332 1
1.5%
126.3206311 1
1.5%
126.3313681 1
1.5%
ValueCountFrequency (%)
126.9425348 1
1.5%
126.9196473 1
1.5%
126.8991383 1
1.5%
126.8840616 2
2.9%
126.8727771 1
1.5%
126.8567401 1
1.5%
126.8541774 1
1.5%
126.8467892 1
1.5%
126.842924 1
1.5%
126.836251 1
1.5%

주차장
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size200.0 B
True
37 
False
31 
ValueCountFrequency (%)
True 37
54.4%
False 31
45.6%
2023-12-13T06:43:25.704837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

화장실
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size200.0 B
False
41 
True
27 
ValueCountFrequency (%)
False 41
60.3%
True 27
39.7%
2023-12-13T06:43:25.777285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

표고(m)
Real number (ℝ)

ZEROS 

Distinct65
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.49706
Minimum0
Maximum760.1
Zeros2
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size744.0 B
2023-12-13T06:43:25.872657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile52.595
Q1137.15
median195.35
Q3342.675
95-th percentile597.86
Maximum760.1
Range760.1
Interquartile range (IQR)205.525

Descriptive statistics

Standard deviation183.75607
Coefficient of variation (CV)0.70540555
Kurtosis0.57655123
Mean260.49706
Median Absolute Deviation (MAD)90.1
Skewness1.0901852
Sum17713.8
Variance33766.294
MonotonicityNot monotonic
2023-12-13T06:43:26.006914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2
 
2.9%
158.0 2
 
2.9%
342.0 2
 
2.9%
104.0 1
 
1.5%
87.5 1
 
1.5%
188.2 1
 
1.5%
356.9 1
 
1.5%
326.4 1
 
1.5%
136.7 1
 
1.5%
136.5 1
 
1.5%
Other values (55) 55
80.9%
ValueCountFrequency (%)
0.0 2
2.9%
40.7 1
1.5%
48.5 1
1.5%
60.2 1
1.5%
63.5 1
1.5%
87.5 1
1.5%
94.8 1
1.5%
100.4 1
1.5%
101.2 1
1.5%
104.0 1
1.5%
ValueCountFrequency (%)
760.1 1
1.5%
757.8 1
1.5%
742.9 1
1.5%
613.4 1
1.5%
569.0 1
1.5%
567.5 1
1.5%
539.0 1
1.5%
523.0 1
1.5%
508.0 1
1.5%
491.9 1
1.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size676.0 B
2022-05-10
68 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-05-10
2nd row2022-05-10
3rd row2022-05-10
4th row2022-05-10
5th row2022-05-10

Common Values

ValueCountFrequency (%)
2022-05-10 68
100.0%

Length

2023-12-13T06:43:26.162623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:43:26.254980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-05-10 68
100.0%

Interactions

2023-12-13T06:43:23.073229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:22.457144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:22.777504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:23.179044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:22.551514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:22.867521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:23.263325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:22.673929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:43:22.964514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:43:26.335183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동오름명소재지위도경도주차장화장실표고(m)
읍면동1.0001.0001.0000.7400.9050.1340.3520.624
오름명1.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0000.0000.0000.823
위도0.7401.0001.0001.0000.8740.3410.0000.000
경도0.9051.0001.0000.8741.0000.4130.4880.618
주차장0.1341.0000.0000.3410.4131.0000.7940.356
화장실0.3521.0000.0000.0000.4880.7941.0000.339
표고(m)0.6241.0000.8230.0000.6180.3560.3391.000
2023-12-13T06:43:26.453105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
화장실읍면동주차장
화장실1.0000.3100.584
읍면동0.3101.0000.109
주차장0.5840.1091.000
2023-12-13T06:43:26.533780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도표고(m)읍면동주차장화장실
위도1.0000.8380.1530.4240.2410.000
경도0.8381.000-0.0840.6800.2950.349
표고(m)0.153-0.0841.0000.3180.2520.240
읍면동0.4240.6800.3181.0000.1090.310
주차장0.2410.2950.2520.1091.0000.584
화장실0.0000.3490.2400.3100.5841.000

Missing values

2023-12-13T06:43:23.367164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:43:23.504286image/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

읍면동오름명소재지위도경도주차장화장실표고(m)데이터기준일자
0대정읍송악산(절울이)제주특별자치도 서귀포시 대정읍 상모리 산 233.199409126.29078YY104.02022-05-10
1대정읍섯알오름제주특별자치도 서귀포시 대정읍 상모리 161833.204254126.281676YY40.72022-05-10
2대정읍가시오름제주특별자치도 서귀포시 대정읍 동일리 120933.253363126.249055YN106.52022-05-10
3대정읍녹남봉제주특별자치도 서귀포시 대정읍 신도리 130433.279815126.195483NN100.42022-05-10
4남원읍물영아리제주특별자치도 서귀포시 남원읍 수망리 산 18933.370809126.69352YN508.02022-05-10
5남원읍머체오름제주특별자치도 서귀포시 남원읍 한남리 산 2-133.342895126.649413YY425.82022-05-10
6남원읍이승이제주특별자치도 서귀포시 남원읍 신례리 산 2-133.342895126.649413YY539.02022-05-10
7남원읍사려니오름제주특별자치도 서귀포시 남원읍 한남리 산 2-133.342895126.649413NN523.02022-05-10
8남원읍민오름제주특별자치도 서귀포시 남원읍 수망리 15833.350883126.698013YN446.82022-05-10
9남원읍운지오름제주특별자치도 서귀포시 남원읍 남원2리 760-233.320538126.745155NN113.52022-05-10
읍면동오름명소재지위도경도주차장화장실표고(m)데이터기준일자
58영천동칡오름제주특별자치도 서귀포시 상효동 산 19333.361667126.529167NN271.02022-05-10
59영천동영천오름제주특별자치도 서귀포시 상효동 산 12333.361667126.529167YN227.02022-05-10
60동홍동미악산(솔오름)제주특별자치도 서귀포시 동홍동 산 733.301759126.557339YY567.52022-05-10
61서홍동삼매봉제주특별자치도 서귀포시 서홍동 809-133.253938126.559592YY153.62022-05-10
62서홍동하논-보로미제주특별자치도 서귀포시 호근동 14933.253782126.542263YN143.42022-05-10
63서홍동시오름제주특별자치도 서귀포시 서홍동 산 133.361667126.529167NN757.82022-05-10
64대륜동고근산제주특별자치도 서귀포시 서호동 1286-133.266364126.512309YY396.22022-05-10
65중문동거린사슴제주특별자치도 서귀포시 대포동 산 2-133.307313126.454127YY742.92022-05-10
66중문동베릿내오름제주특별자치도 서귀포시 중문동 371233.25143126.434824YY101.22022-05-10
67중문동법정악제주특별자치도 서귀포시 하원동 산 133.258346126.4521YY760.12022-05-10