Overview

Dataset statistics

Number of variables7
Number of observations127
Missing cells103
Missing cells (%)11.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 KiB
Average record size in memory59.0 B

Variable types

Categorical2
Text3
Numeric2

Dataset

Description국립공원에 설치되어 있는 120여개의 기후변화측정망 현황에 대한 데이터입니다. 공원명, 일련번호, 위치, 좌표, 고도, 작동여부에 대한 데이터로 csv형식입니다.
Author국립공원공단
URLhttps://www.data.go.kr/data/15003425/fileData.do

Alerts

작동여부 has constant value ""Constant
좌표(X) is highly overall correlated with 공원명High correlation
좌표(Y) is highly overall correlated with 공원명High correlation
공원명 is highly overall correlated with 좌표(X) and 1 other fieldsHigh correlation
좌표(X) has 2 (1.6%) missing valuesMissing
좌표(Y) has 2 (1.6%) missing valuesMissing
고도 has 99 (78.0%) missing valuesMissing
일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:27:50.303329
Analysis finished2023-12-12 17:27:51.447871
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공원명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
월악산
15 
설악산
10 
덕유산
10 
가야산
8 
치악산
8 
Other values (21)
76 

Length

Max length8
Median length3
Mean length3.519685
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row지리산북부
2nd row지리산북부
3rd row지리산북부
4th row계룡산
5th row계룡산

Common Values

ValueCountFrequency (%)
월악산 15
 
11.8%
설악산 10
 
7.9%
덕유산 10
 
7.9%
가야산 8
 
6.3%
치악산 8
 
6.3%
경주 7
 
5.5%
북한산 6
 
4.7%
계룡산 5
 
3.9%
한려해상 5
 
3.9%
속리산 4
 
3.1%
Other values (16) 49
38.6%

Length

2023-12-13T02:27:51.820052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
월악산 15
 
11.8%
설악산 10
 
7.9%
덕유산 10
 
7.9%
가야산 8
 
6.3%
치악산 8
 
6.3%
경주 7
 
5.5%
북한산 6
 
4.7%
계룡산 5
 
3.9%
한려해상 5
 
3.9%
주왕산 4
 
3.1%
Other values (16) 49
38.6%

일련번호
Text

UNIQUE 

Distinct127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T02:27:52.204686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1023622
Min length4

Characters and Unicode

Total characters521
Distinct characters48
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)100.0%

Sample

1st row지북-1
2nd row지북-2
3rd row지북-3
4th row계룡-5
5th row계룡-4
ValueCountFrequency (%)
지북-1 1
 
0.8%
북도-1 1
 
0.8%
북한-3 1
 
0.8%
북한-5 1
 
0.8%
북한-6 1
 
0.8%
북한-4 1
 
0.8%
월악-1 1
 
0.8%
월악-2 1
 
0.8%
월악-3 1
 
0.8%
월악-4 1
 
0.8%
Other values (117) 117
92.1%
2023-12-13T02:27:52.762236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 127
24.4%
1 35
 
6.7%
악 35
 
6.7%
2 24
 
4.6%
3 22
 
4.2%
4 19
 
3.6%
월 19
 
3.6%
북 15
 
2.9%
한 13
 
2.5%
주 11
 
2.1%
Other values (38) 201
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 258
49.5%
Decimal Number 136
26.1%
Dash Punctuation 127
24.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
악 35
 
13.6%
월 19
 
7.4%
북 15
 
5.8%
한 13
 
5.0%
주 11
 
4.3%
유 10
 
3.9%
설 10
 
3.9%
덕 10
 
3.9%
야 8
 
3.1%
치 8
 
3.1%
Other values (27) 119
46.1%
Decimal Number
ValueCountFrequency (%)
1 35
25.7%
2 24
17.6%
3 22
16.2%
4 19
14.0%
5 10
 
7.4%
6 8
 
5.9%
7 6
 
4.4%
8 5
 
3.7%
9 4
 
2.9%
0 3
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 263
50.5%
Hangul 258
49.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
악 35
 
13.6%
월 19
 
7.4%
북 15
 
5.8%
한 13
 
5.0%
주 11
 
4.3%
유 10
 
3.9%
설 10
 
3.9%
덕 10
 
3.9%
야 8
 
3.1%
치 8
 
3.1%
Other values (27) 119
46.1%
Common
ValueCountFrequency (%)
- 127
48.3%
1 35
 
13.3%
2 24
 
9.1%
3 22
 
8.4%
4 19
 
7.2%
5 10
 
3.8%
6 8
 
3.0%
7 6
 
2.3%
8 5
 
1.9%
9 4
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263
50.5%
Hangul 258
49.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 127
48.3%
1 35
 
13.3%
2 24
 
9.1%
3 22
 
8.4%
4 19
 
7.2%
5 10
 
3.8%
6 8
 
3.0%
7 6
 
2.3%
8 5
 
1.9%
9 4
 
1.5%
Hangul
ValueCountFrequency (%)
악 35
 
13.6%
월 19
 
7.4%
북 15
 
5.8%
한 13
 
5.0%
주 11
 
4.3%
유 10
 
3.9%
설 10
 
3.9%
덕 10
 
3.9%
야 8
 
3.1%
치 8
 
3.1%
Other values (27) 119
46.1%

위치
Text

Distinct122
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T02:27:53.083495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length8.1417323
Min length3

Characters and Unicode

Total characters1034
Distinct characters212
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)92.9%

Sample

1st row뱀사골계곡(고정조사구1) - 요룡대
2nd row뱀사골계곡(고정조사구7) - 간장소
3rd row뱀사골계곡(고정조사구10) - 막차
4th row수통골화산계곡(특별보호구)
5th row동학계곡
ValueCountFrequency (%)
영봉 5
 
2.6%
격자 4
 
2.1%
고정조사구 4
 
2.1%
조사구 4
 
2.1%
하단 3
 
1.6%
3
 
1.6%
토함산지구 3
 
1.6%
상록수림 2
 
1.0%
탐방로 2
 
1.0%
1지점 2
 
1.0%
Other values (152) 161
83.4%
2023-12-13T02:27:53.548071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66
 
6.4%
구 51
 
4.9%
( 33
 
3.2%
) 33
 
3.2%
지 30
 
2.9%
봉 27
 
2.6%
사 27
 
2.6%
조 20
 
1.9%
정 19
 
1.8%
계 18
 
1.7%
Other values (202) 710
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 838
81.0%
Space Separator 66
 
6.4%
Decimal Number 47
 
4.5%
Open Punctuation 33
 
3.2%
Close Punctuation 33
 
3.2%
Dash Punctuation 9
 
0.9%
Math Symbol 3
 
0.3%
Other Punctuation 3
 
0.3%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 51
 
6.1%
지 30
 
3.6%
봉 27
 
3.2%
사 27
 
3.2%
조 20
 
2.4%
정 19
 
2.3%
계 18
 
2.1%
고 17
 
2.0%
산 17
 
2.0%
곡 17
 
2.0%
Other values (185) 595
71.0%
Decimal Number
ValueCountFrequency (%)
1 16
34.0%
2 7
14.9%
0 7
14.9%
4 4
 
8.5%
7 3
 
6.4%
8 3
 
6.4%
3 2
 
4.3%
5 2
 
4.3%
9 2
 
4.3%
6 1
 
2.1%
Space Separator
ValueCountFrequency (%)
66
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 838
81.0%
Common 194
 
18.8%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 51
 
6.1%
지 30
 
3.6%
봉 27
 
3.2%
사 27
 
3.2%
조 20
 
2.4%
정 19
 
2.3%
계 18
 
2.1%
고 17
 
2.0%
산 17
 
2.0%
곡 17
 
2.0%
Other values (185) 595
71.0%
Common
ValueCountFrequency (%)
66
34.0%
( 33
17.0%
) 33
17.0%
1 16
 
8.2%
- 9
 
4.6%
2 7
 
3.6%
0 7
 
3.6%
4 4
 
2.1%
~ 3
 
1.5%
/ 3
 
1.5%
Other values (6) 13
 
6.7%
Latin
ValueCountFrequency (%)
m 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 838
81.0%
ASCII 196
 
19.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66
33.7%
( 33
16.8%
) 33
16.8%
1 16
 
8.2%
- 9
 
4.6%
2 7
 
3.6%
0 7
 
3.6%
4 4
 
2.0%
~ 3
 
1.5%
/ 3
 
1.5%
Other values (7) 15
 
7.7%
Hangul
ValueCountFrequency (%)
구 51
 
6.1%
지 30
 
3.6%
봉 27
 
3.2%
사 27
 
3.2%
조 20
 
2.4%
정 19
 
2.3%
계 18
 
2.1%
고 17
 
2.0%
산 17
 
2.0%
곡 17
 
2.0%
Other values (185) 595
71.0%

좌표(X)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct123
Distinct (%)98.4%
Missing2
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean127.80299
Minimum125.19363
Maximum129.64973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T02:27:53.737833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.19363
5-th percentile126.49088
Q1127.03773
median127.98583
Q3128.28873
95-th percentile129.19319
Maximum129.64973
Range4.4561
Interquartile range (IQR)1.251

Descriptive statistics

Standard deviation0.81301016
Coefficient of variation (CV)0.0063614332
Kurtosis0.15519374
Mean127.80299
Median Absolute Deviation (MAD)0.4647667
Skewness-0.32056593
Sum15975.373
Variance0.66098553
MonotonicityNot monotonic
2023-12-13T02:27:53.893353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1531 2
 
1.6%
128.4486333 2
 
1.6%
128.0538 1
 
0.8%
128.2606 1
 
0.8%
126.9616 1
 
0.8%
126.9641 1
 
0.8%
127.0014 1
 
0.8%
126.9555 1
 
0.8%
126.9995333 1
 
0.8%
128.0795667 1
 
0.8%
Other values (113) 113
89.0%
(Missing) 2
 
1.6%
ValueCountFrequency (%)
125.1936333 1
0.8%
125.8255667 1
0.8%
126.2124 1
0.8%
126.2927333 1
0.8%
126.3477333 1
0.8%
126.3778667 1
0.8%
126.4668667 1
0.8%
126.5869333 1
0.8%
126.5992333 1
0.8%
126.6493333 1
0.8%
ValueCountFrequency (%)
129.6497333 1
0.8%
129.3993333 1
0.8%
129.3543 1
0.8%
129.3391333 1
0.8%
129.2302667 1
0.8%
129.2286667 1
0.8%
129.1946333 1
0.8%
129.1874333 1
0.8%
129.1531 2
1.6%
129.0716667 1
0.8%

좌표(Y)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct120
Distinct (%)96.0%
Missing2
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean36.301734
Minimum34.018267
Maximum38.173867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T02:27:54.116077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.018267
5-th percentile34.743267
Q135.625767
median36.3599
Q336.934433
95-th percentile38.11466
Maximum38.173867
Range4.1556
Interquartile range (IQR)1.3086667

Descriptive statistics

Standard deviation1.029436
Coefficient of variation (CV)0.028357763
Kurtosis-0.80005502
Mean36.301734
Median Absolute Deviation (MAD)0.61446667
Skewness0.013453748
Sum4537.7168
Variance1.0597385
MonotonicityNot monotonic
2023-12-13T02:27:54.323571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.39413333 2
 
1.6%
36.88256666 2
 
1.6%
36.87956666 2
 
1.6%
34.7542 2
 
1.6%
35.82246666 2
 
1.6%
37.29253333 1
 
0.8%
36.98473333 1
 
0.8%
37.6166 1
 
0.8%
37.64783333 1
 
0.8%
37.64323333 1
 
0.8%
Other values (110) 110
86.6%
(Missing) 2
 
1.6%
ValueCountFrequency (%)
34.01826666 1
0.8%
34.24873333 1
0.8%
34.47136666 1
0.8%
34.59573333 1
0.8%
34.6131 1
0.8%
34.68613333 1
0.8%
34.74053333 1
0.8%
34.7542 2
1.6%
34.7666 1
0.8%
34.76706666 1
0.8%
ValueCountFrequency (%)
38.17386666 1
0.8%
38.16726666 1
0.8%
38.1584 1
0.8%
38.1552 1
0.8%
38.12616666 1
0.8%
38.12116666 1
0.8%
38.11916666 1
0.8%
38.09663333 1
0.8%
38.08943333 1
0.8%
38.07166666 1
0.8%

고도
Text

MISSING 

Distinct25
Distinct (%)89.3%
Missing99
Missing (%)78.0%
Memory size1.1 KiB
2023-12-13T02:27:54.543714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.25
Min length1

Characters and Unicode

Total characters91
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)82.1%

Sample

1st row585
2nd row880
3rd row1153
4th row0
5th row5
ValueCountFrequency (%)
0 3
 
10.7%
747m 2
 
7.1%
909m 1
 
3.6%
259m 1
 
3.6%
30 1
 
3.6%
229 1
 
3.6%
836 1
 
3.6%
1085 1
 
3.6%
406 1
 
3.6%
157m 1
 
3.6%
Other values (15) 15
53.6%
2023-12-13T02:27:54.893128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 15
16.5%
5 12
13.2%
0 11
12.1%
4 9
9.9%
8 8
8.8%
2 8
8.8%
3 7
7.7%
7 6
 
6.6%
1 6
 
6.6%
9 6
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76
83.5%
Lowercase Letter 15
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12
15.8%
0 11
14.5%
4 9
11.8%
8 8
10.5%
2 8
10.5%
3 7
9.2%
7 6
7.9%
1 6
7.9%
9 6
7.9%
6 3
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
m 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76
83.5%
Latin 15
 
16.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12
15.8%
0 11
14.5%
4 9
11.8%
8 8
10.5%
2 8
10.5%
3 7
9.2%
7 6
7.9%
1 6
7.9%
9 6
7.9%
6 3
 
3.9%
Latin
ValueCountFrequency (%)
m 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 15
16.5%
5 12
13.2%
0 11
12.1%
4 9
9.9%
8 8
8.8%
2 8
8.8%
3 7
7.7%
7 6
 
6.6%
1 6
 
6.6%
9 6
 
6.6%

작동여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
작동
127 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
작동 127
100.0%

Length

2023-12-13T02:27:55.053963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:27:55.159837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
작동 127
100.0%

Interactions

2023-12-13T02:27:50.834020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:50.653277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:50.930768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:50.744620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:27:55.224028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공원명좌표(X)좌표(Y)고도
공원명1.0000.9750.9831.000
좌표(X)0.9751.0000.8721.000
좌표(Y)0.9830.8721.0000.000
고도1.0001.0000.0001.000
2023-12-13T02:27:55.323075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
좌표(X)좌표(Y)공원명
좌표(X)1.0000.3630.789
좌표(Y)0.3631.0000.827
공원명0.7890.8271.000

Missing values

2023-12-13T02:27:51.064004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:27:51.206596image/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.
2023-12-13T02:27:51.373812image/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

공원명일련번호위치좌표(X)좌표(Y)고도작동여부
0지리산북부지북-1뱀사골계곡(고정조사구1) - 요룡대127.58836135.357361585작동
1지리산북부지북-2뱀사골계곡(고정조사구7) - 간장소127.58944435.326028880작동
2지리산북부지북-3뱀사골계곡(고정조사구10) - 막차127.58716735.3111391153작동
3계룡산계룡-5수통골화산계곡(특별보호구)127.27696736.337433<NA>작동
4계룡산계룡-4동학계곡127.21756736.3523<NA>작동
5계룡산계룡-3금잔디고개하단127.210636.3646<NA>작동
6계룡산계룡-1천황봉하단<NA><NA><NA>작동
7계룡산계룡-2동월계곡127.254936.3503<NA>작동
8한려해상한려-1금산 자연관찰로127.98583334.740533<NA>작동
9한려해상한려-4금평교일원127.963634.773867<NA>작동
공원명일련번호위치좌표(X)좌표(Y)고도작동여부
117경주경주-4토함산지구129.354335.781167<NA>작동
118경주경주-3토함산지구129.33913335.822467<NA>작동
119경주경주-2남산지구129.22866735.7659<NA>작동
120경주경주-1단석산지구129.07166735.791333<NA>작동
121무등산동부무동-1도원마을127.02736735.1148406작동
122무등산동부무동-3서석대127.003335.1208671085작동
123무등산동부무동-2규봉암127.016335.118167836작동
124국립공원연구원연구-1연구원 앞127.46164235.389356229작동
125산악안전교육센터산악안전-1산악안전127.87766736.73983330작동
126산악안전교육센터산악안전-2산악안전127.559735.26946740작동