Overview

Dataset statistics

Number of variables7
Number of observations106
Missing cells4
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory61.2 B

Variable types

Numeric4
Text2
DateTime1

Dataset

Description전라남도 내 특정 도서(도서명, 주소, 면적(제곱미터), 지정년도, 섬, 섬 위치 등) 지정에 관한 데이터입니다.
Author전라남도
URLhttps://www.data.go.kr/data/15069151/fileData.do

Alerts

위도 has 2 (1.9%) missing valuesMissing
경도 has 2 (1.9%) missing valuesMissing
지정번호 has unique valuesUnique
면적(제곱미터) has unique valuesUnique
지번 has unique valuesUnique

Reproduction

Analysis started2023-08-16 00:40:56.179648
Analysis finished2023-08-16 00:40:58.918199
Duration2.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지정번호
Real number (ℝ)

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.5
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-16T09:40:59.029407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.25
Q127.25
median53.5
Q379.75
95-th percentile100.75
Maximum106
Range105
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation30.743563
Coefficient of variation (CV)0.57464604
Kurtosis-1.2
Mean53.5
Median Absolute Deviation (MAD)26.5
Skewness0
Sum5671
Variance945.16667
MonotonicityStrictly increasing
2023-08-16T09:40:59.233088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
81 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
73 1
 
0.9%
72 1
 
0.9%
Other values (96) 96
90.6%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%
98 1
0.9%
97 1
0.9%

도서명
Text

Distinct102
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size980.0 B
2023-08-16T09:40:59.638495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.2924528
Min length2

Characters and Unicode

Total characters349
Distinct characters117
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

Unique99 ?
Unique (%)93.4%

Sample

1st row병풍도
2nd row행금도
3rd row탄항도
4th row납태기도(서대기도)
5th row백야도
ValueCountFrequency (%)
도 5
 
4.5%
송도 3
 
2.7%
가덕도 2
 
1.8%
구도 2
 
1.8%
육각도 1
 
0.9%
외엽산도 1
 
0.9%
진지외도 1
 
0.9%
다라도 1
 
0.9%
저도 1
 
0.9%
해2도 1
 
0.9%
Other values (94) 94
83.9%
2023-08-16T09:41:00.275705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 82
23.5%
섬 25
 
7.2%
소 10
 
2.9%
대 10
 
2.9%
기 9
 
2.6%
매 8
 
2.3%
6
 
1.7%
고 6
 
1.7%
중 5
 
1.4%
갈 5
 
1.4%
Other values (107) 183
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 331
94.8%
Space Separator 6
 
1.7%
Close Punctuation 4
 
1.1%
Open Punctuation 4
 
1.1%
Decimal Number 4
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 82
24.8%
섬 25
 
7.6%
소 10
 
3.0%
대 10
 
3.0%
기 9
 
2.7%
매 8
 
2.4%
고 6
 
1.8%
중 5
 
1.5%
갈 5
 
1.5%
여 4
 
1.2%
Other values (102) 167
50.5%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 331
94.8%
Common 18
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 82
24.8%
섬 25
 
7.6%
소 10
 
3.0%
대 10
 
3.0%
기 9
 
2.7%
매 8
 
2.4%
고 6
 
1.8%
중 5
 
1.5%
갈 5
 
1.5%
여 4
 
1.2%
Other values (102) 167
50.5%
Common
ValueCountFrequency (%)
6
33.3%
) 4
22.2%
( 4
22.2%
2 2
 
11.1%
1 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 331
94.8%
ASCII 18
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 82
24.8%
섬 25
 
7.6%
소 10
 
3.0%
대 10
 
3.0%
기 9
 
2.7%
매 8
 
2.4%
고 6
 
1.8%
중 5
 
1.5%
갈 5
 
1.5%
여 4
 
1.2%
Other values (102) 167
50.5%
ASCII
ValueCountFrequency (%)
6
33.3%
) 4
22.2%
( 4
22.2%
2 2
 
11.1%
1 2
 
11.1%

면적(제곱미터)
Real number (ℝ)

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53714.462
Minimum334
Maximum560530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-16T09:41:00.509859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile1912.5
Q111965.75
median31815
Q363232.25
95-th percentile191974.5
Maximum560530
Range560196
Interquartile range (IQR)51266.5

Descriptive statistics

Standard deviation74368.678
Coefficient of variation (CV)1.3845187
Kurtosis20.773897
Mean53714.462
Median Absolute Deviation (MAD)21769
Skewness3.8198001
Sum5693733
Variance5.5307002 × 109
MonotonicityNot monotonic
2023-08-16T09:41:01.052068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
560530 1
 
0.9%
4661 1
 
0.9%
3868 1
 
0.9%
10589 1
 
0.9%
39067 1
 
0.9%
19342 1
 
0.9%
1738 1
 
0.9%
1450 1
 
0.9%
23901 1
 
0.9%
26505 1
 
0.9%
Other values (96) 96
90.6%
ValueCountFrequency (%)
334 1
0.9%
706 1
0.9%
1450 1
0.9%
1587 1
0.9%
1738 1
0.9%
1889 1
0.9%
1983 1
0.9%
2010 1
0.9%
3074 1
0.9%
3175 1
0.9%
ValueCountFrequency (%)
560530 1
0.9%
265190 1
0.9%
248432 1
0.9%
242876 1
0.9%
205091 1
0.9%
197156 1
0.9%
176430 1
0.9%
164107 1
0.9%
149157 1
0.9%
129832 1
0.9%

지번
Text

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size980.0 B
2023-08-16T09:41:01.638587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length21
Mean length23.858491
Min length17

Characters and Unicode

Total characters2529
Distinct characters133
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)100.0%

Sample

1st row전라남도 진도군 조도면 동거차도리 산16, 산17
2nd row전라남도 진도군 조도면 독거도리 산101, 산102
3rd row전라남도 진도군 조도면 독거도리 산103
4th row전라남도 진도군 조도면 독거도리 산107
5th row전라남도 진도군 조도면 여미리 산209
ValueCountFrequency (%)
전라남도 106
 
18.3%
완도군 34
 
5.9%
신안군 30
 
5.2%
진도군 15
 
2.6%
여수시 14
 
2.4%
금일읍 13
 
2.2%
조도면 8
 
1.4%
삼산면 6
 
1.0%
생일면 6
 
1.0%
고흥군 6
 
1.0%
Other values (246) 340
58.8%
2023-08-16T09:41:02.491578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
472
18.7%
도 197
 
7.8%
산 160
 
6.3%
남 110
 
4.3%
전 107
 
4.2%
라 106
 
4.2%
리 104
 
4.1%
1 98
 
3.9%
군 92
 
3.6%
면 81
 
3.2%
Other values (123) 1002
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1529
60.5%
Space Separator 472
 
18.7%
Decimal Number 442
 
17.5%
Other Punctuation 49
 
1.9%
Dash Punctuation 31
 
1.2%
Math Symbol 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 197
 
12.9%
산 160
 
10.5%
남 110
 
7.2%
전 107
 
7.0%
라 106
 
6.9%
리 104
 
6.8%
군 92
 
6.0%
면 81
 
5.3%
신 38
 
2.5%
완 34
 
2.2%
Other values (108) 500
32.7%
Decimal Number
ValueCountFrequency (%)
1 98
22.2%
2 65
14.7%
3 53
12.0%
0 44
10.0%
4 36
 
8.1%
5 36
 
8.1%
7 31
 
7.0%
6 30
 
6.8%
9 29
 
6.6%
8 20
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 47
95.9%
. 2
 
4.1%
Space Separator
ValueCountFrequency (%)
472
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Math Symbol
ValueCountFrequency (%)
～ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1529
60.5%
Common 1000
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 197
 
12.9%
산 160
 
10.5%
남 110
 
7.2%
전 107
 
7.0%
라 106
 
6.9%
리 104
 
6.8%
군 92
 
6.0%
면 81
 
5.3%
신 38
 
2.5%
완 34
 
2.2%
Other values (108) 500
32.7%
Common
ValueCountFrequency (%)
472
47.2%
1 98
 
9.8%
2 65
 
6.5%
3 53
 
5.3%
, 47
 
4.7%
0 44
 
4.4%
4 36
 
3.6%
5 36
 
3.6%
7 31
 
3.1%
- 31
 
3.1%
Other values (5) 87
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1529
60.5%
ASCII 994
39.3%
None 6
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
472
47.5%
1 98
 
9.9%
2 65
 
6.5%
3 53
 
5.3%
, 47
 
4.7%
0 44
 
4.4%
4 36
 
3.6%
5 36
 
3.6%
7 31
 
3.1%
- 31
 
3.1%
Other values (4) 81
 
8.1%
Hangul
ValueCountFrequency (%)
도 197
 
12.9%
산 160
 
10.5%
남 110
 
7.2%
전 107
 
7.0%
라 106
 
6.9%
리 104
 
6.8%
군 92
 
6.0%
면 81
 
5.3%
신 38
 
2.5%
완 34
 
2.2%
Other values (108) 500
32.7%
None
ValueCountFrequency (%)
～ 6
100.0%

지정년도
Date

Distinct13
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size980.0 B
Minimum2000-09-05 00:00:00
Maximum2019-12-23 00:00:00
2023-08-16T09:41:02.686392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:41:02.877682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

위도
Real number (ℝ)

MISSING 

Distinct103
Distinct (%)99.0%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean126.61894
Minimum125.09502
Maximum127.76017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-16T09:41:03.122106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.09502
5-th percentile125.893
Q1126.12527
median126.45907
Q3127.09095
95-th percentile127.57881
Maximum127.76017
Range2.6651492
Interquartile range (IQR)0.96567557

Descriptive statistics

Standard deviation0.62291822
Coefficient of variation (CV)0.004919629
Kurtosis-0.73148087
Mean126.61894
Median Absolute Deviation (MAD)0.45450615
Skewness-0.086156864
Sum13168.37
Variance0.38802711
MonotonicityNot monotonic
2023-08-16T09:41:03.431972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.4774722 2
 
1.9%
127.6028218 1
 
0.9%
127.647584 1
 
0.9%
125.3146225 1
 
0.9%
126.0131818 1
 
0.9%
126.1978729 1
 
0.9%
127.4218645 1
 
0.9%
127.4279979 1
 
0.9%
127.6577649 1
 
0.9%
127.5308665 1
 
0.9%
Other values (93) 93
87.7%
(Missing) 2
 
1.9%
ValueCountFrequency (%)
125.0950191 1
0.9%
125.2994522 1
0.9%
125.2994971 1
0.9%
125.3001132 1
0.9%
125.3146225 1
0.9%
125.8848291 1
0.9%
125.9392881 1
0.9%
125.9555634 1
0.9%
126.0042559 1
0.9%
126.0048736 1
0.9%
ValueCountFrequency (%)
127.7601683 1
0.9%
127.6577649 1
0.9%
127.647584 1
0.9%
127.6290329 1
0.9%
127.6028218 1
0.9%
127.5820954 1
0.9%
127.5601935 1
0.9%
127.5424851 1
0.9%
127.5308665 1
0.9%
127.508207 1
0.9%

경도
Real number (ℝ)

MISSING 

Distinct103
Distinct (%)99.0%
Missing2
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean34.523937
Minimum34.114035
Maximum35.322271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-08-16T09:41:03.658478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.114035
5-th percentile34.19066
Q134.282611
median34.423704
Q334.7455
95-th percentile35.074714
Maximum35.322271
Range1.208236
Interquartile range (IQR)0.46288823

Descriptive statistics

Standard deviation0.29971971
Coefficient of variation (CV)0.0086815043
Kurtosis-0.41609018
Mean34.523937
Median Absolute Deviation (MAD)0.19049752
Skewness0.78954457
Sum3590.4895
Variance0.089831907
MonotonicityNot monotonic
2023-08-16T09:41:04.003464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.67116259 2
 
1.9%
34.5894787 1
 
0.9%
34.54482775 1
 
0.9%
34.38700917 1
 
0.9%
34.65926416 1
 
0.9%
34.56820104 1
 
0.9%
34.79874383 1
 
0.9%
34.8031269 1
 
0.9%
34.74102654 1
 
0.9%
34.26266716 1
 
0.9%
Other values (93) 93
87.7%
(Missing) 2
 
1.9%
ValueCountFrequency (%)
34.11403519 1
0.9%
34.1162604 1
0.9%
34.15263849 1
0.9%
34.15674645 1
0.9%
34.18801409 1
0.9%
34.18852889 1
0.9%
34.20273886 1
0.9%
34.21002708 1
0.9%
34.21141859 1
0.9%
34.21453219 1
0.9%
ValueCountFrequency (%)
35.32227119 1
0.9%
35.25815172 1
0.9%
35.14387043 1
0.9%
35.13565328 1
0.9%
35.13542915 1
0.9%
35.07584651 1
0.9%
35.06829634 1
0.9%
35.03950252 1
0.9%
35.01890567 1
0.9%
35.01800984 1
0.9%

Interactions

2023-08-16T09:40:57.962282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:56.528315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:57.012703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:57.443740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:58.083628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:56.653863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:57.110602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:57.529401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:58.195148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:56.778248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:57.220735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:57.668039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:58.320235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:56.895825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:57.347875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-16T09:40:57.822850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-16T09:41:04.200347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호면적(제곱미터)지정년도위도경도
지정번호1.0000.2200.9180.7150.790
면적(제곱미터)0.2201.0000.2200.5500.221
지정년도0.9180.2201.0000.8070.787
위도0.7150.5500.8071.0000.620
경도0.7900.2210.7870.6201.000
2023-08-16T09:41:04.358123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호면적(제곱미터)위도경도
지정번호1.000-0.289-0.0470.030
면적(제곱미터)-0.2891.000-0.050-0.086
위도-0.047-0.0501.000-0.304
경도0.030-0.086-0.3041.000

Missing values

2023-08-16T09:40:58.523792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-16T09:40:58.700957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-16T09:40:58.843832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지정번호도서명면적(제곱미터)지번지정년도위도경도
01병풍도560530전라남도 진도군 조도면 동거차도리 산16, 산172000-09-05125.93928834.156746
12행금도56333전라남도 진도군 조도면 독거도리 산101, 산1022000-09-05126.12626834.268352
23탄항도13326전라남도 진도군 조도면 독거도리 산1032000-09-05126.18207834.23694
34납태기도(서대기도)66967전라남도 진도군 조도면 독거도리 산1072000-09-05126.15087534.22498
45백야도62678전라남도 진도군 조도면 여미리 산2092000-09-05126.00425634.37753
56목도112330전라남도 고흥군 도화면 지죽리 산111, 산111-1, 산111-2, 산112, 산112-12000-09-05127.29475634.438313
67대항도69819전라남도 고흥군 봉래면 예내리 산1-1, 산2, 산2-1, 26-1～26-42000-09-05127.54248534.443382
78곡두도54040전라남도 고흥군 봉래면 외초리 산316, 산316-1, 산317, 산317-12000-09-05127.49414234.398846
89진섬164107전라남도 완도군 신지면 월양리 산3722002-05-01126.86568234.285857
910혈도53455전라남도 완도군 신지면 동고리 산7-42002-05-01126.91117434.33853
지정번호도서명면적(제곱미터)지번지정년도위도경도
9697중방고도12145전라남도 진도군 조도면 가사도리 산4032014-01-06126.12171734.510989
9798하방고도706전라남도 진도군 조도면 가사도리 산4042014-01-06126.12324234.511923
9899상방고도19536전라남도 진도군 조도면 가사도리 산4052014-01-06126.12594934.513614
99100솔섬1889전라남도 진도군 지산면 가학리 산1722014-01-06126.09324734.440422
100101흰여23199전라남도 완도군 금일읍 장원리 산952016-12-22127.13366334.216639
101102복생도7558전라남도 완도군 보길면 예송리 산1132016-12-22126.57617534.114035
102103육산도41355전라남도 영광군 낙월면 송이리 산4662016-12-22126.27622135.322271
103104각거도48595전라남도 영광군 낙월면 각이리 산102017-12-22126.08649635.258152
104105중결도248432전라남도 여수시 삼산면 초도리 산34-12019-12-23<NA><NA>
105106고여6336전라남도 여수시 경호동 산2402019-12-23<NA><NA>