Overview

Dataset statistics

Number of variables6
Number of observations85
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory52.6 B

Variable types

Numeric3
Categorical2
Text1

Dataset

Description국립공원공단 내 설치된 자동우량경보시설 중 우량국의 위치정보 데이터 입니다.데이터에는 관리 사무소 명 우량국 국소명, 위도, 경도 값을 제공합니다.
Author국립공원공단
URLhttps://www.data.go.kr/data/15124545/fileData.do

Alerts

국종 has constant value ""Constant
연번 is highly overall correlated with 사무소High correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
사무소 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
국소명 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:49:17.400886
Analysis finished2023-12-12 20:49:18.685845
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T05:49:18.784076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q122
median43
Q364
95-th percentile80.8
Maximum85
Range84
Interquartile range (IQR)42

Descriptive statistics

Standard deviation24.681302
Coefficient of variation (CV)0.57398377
Kurtosis-1.2
Mean43
Median Absolute Deviation (MAD)21
Skewness0
Sum3655
Variance609.16667
MonotonicityStrictly increasing
2023-12-13T05:49:18.987614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
55 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
Other values (75) 75
88.2%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
85 1
1.2%
84 1
1.2%
83 1
1.2%
82 1
1.2%
81 1
1.2%
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%

사무소
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size812.0 B
지리산전남
15 
월악산
9 
설악산
8 
치악산
6 
지리산전북
5 
Other values (16)
42 

Length

Max length5
Median length3
Mean length3.7411765
Min length3

Unique

Unique3 ?
Unique (%)3.5%

Sample

1st row지리산경남
2nd row지리산경남
3rd row지리산경남
4th row지리산전북
5th row지리산전북

Common Values

ValueCountFrequency (%)
지리산전남 15
17.6%
월악산 9
10.6%
설악산 8
 
9.4%
치악산 6
 
7.1%
지리산전북 5
 
5.9%
주왕산 4
 
4.7%
덕유산 4
 
4.7%
소백산 4
 
4.7%
태백산 4
 
4.7%
속리산 4
 
4.7%
Other values (11) 22
25.9%

Length

2023-12-13T05:49:19.193747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지리산전남 15
17.6%
월악산 9
10.6%
설악산 8
 
9.4%
치악산 6
 
7.1%
지리산전북 5
 
5.9%
주왕산 4
 
4.7%
덕유산 4
 
4.7%
소백산 4
 
4.7%
태백산 4
 
4.7%
속리산 4
 
4.7%
Other values (11) 22
25.9%

국소명
Text

UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
2023-12-13T05:49:19.563398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.2352941
Min length2

Characters and Unicode

Total characters275
Distinct characters117
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

Unique85 ?
Unique (%)100.0%

Sample

1st row조개골
2nd row작은새재골
3rd row선녀탕
4th row뱀사골
5th row심원
ValueCountFrequency (%)
조개골 1
 
1.2%
안성1 1
 
1.2%
청벽대 1
 
1.2%
미륵 1
 
1.2%
만수 1
 
1.2%
골미 1
 
1.2%
덕주 1
 
1.2%
성남탐방로 1
 
1.2%
상원사 1
 
1.2%
질아치 1
 
1.2%
Other values (75) 75
88.2%
2023-12-13T05:49:20.180733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
봉 15
 
5.5%
사 11
 
4.0%
골 10
 
3.6%
수 10
 
3.6%
1 8
 
2.9%
문 8
 
2.9%
원 8
 
2.9%
2 8
 
2.9%
산 7
 
2.5%
선 6
 
2.2%
Other values (107) 184
66.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
93.1%
Decimal Number 19
 
6.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
봉 15
 
5.9%
사 11
 
4.3%
골 10
 
3.9%
수 10
 
3.9%
문 8
 
3.1%
원 8
 
3.1%
산 7
 
2.7%
선 6
 
2.3%
대 5
 
2.0%
동 5
 
2.0%
Other values (103) 171
66.8%
Decimal Number
ValueCountFrequency (%)
1 8
42.1%
2 8
42.1%
3 2
 
10.5%
4 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
93.1%
Common 19
 
6.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
봉 15
 
5.9%
사 11
 
4.3%
골 10
 
3.9%
수 10
 
3.9%
문 8
 
3.1%
원 8
 
3.1%
산 7
 
2.7%
선 6
 
2.3%
대 5
 
2.0%
동 5
 
2.0%
Other values (103) 171
66.8%
Common
ValueCountFrequency (%)
1 8
42.1%
2 8
42.1%
3 2
 
10.5%
4 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
93.1%
ASCII 19
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
봉 15
 
5.9%
사 11
 
4.3%
골 10
 
3.9%
수 10
 
3.9%
문 8
 
3.1%
원 8
 
3.1%
산 7
 
2.7%
선 6
 
2.3%
대 5
 
2.0%
동 5
 
2.0%
Other values (103) 171
66.8%
ASCII
ValueCountFrequency (%)
1 8
42.1%
2 8
42.1%
3 2
 
10.5%
4 1
 
5.3%

국종
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size812.0 B
우량국
85 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
우량국 85
100.0%

Length

2023-12-13T05:49:20.348012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:49:20.446958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우량국 85
100.0%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.3265
Minimum34.759444
Maximum38.177481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T05:49:20.581815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.759444
5-th percentile35.152701
Q135.314722
median36.388142
Q337.076576
95-th percentile38.107605
Maximum38.177481
Range3.418037
Interquartile range (IQR)1.761854

Descriptive statistics

Standard deviation1.0175778
Coefficient of variation (CV)0.028011996
Kurtosis-1.2481887
Mean36.3265
Median Absolute Deviation (MAD)0.996841
Skewness0.30289825
Sum3087.7525
Variance1.0354645
MonotonicityNot monotonic
2023-12-13T05:49:20.713501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.370908 1
 
1.2%
37.3045 1
 
1.2%
36.849578 1
 
1.2%
36.824325 1
 
1.2%
36.833828 1
 
1.2%
36.860661 1
 
1.2%
36.861761 1
 
1.2%
37.294167 1
 
1.2%
37.303333 1
 
1.2%
37.309774 1
 
1.2%
Other values (75) 75
88.2%
ValueCountFrequency (%)
34.759444 1
1.2%
34.761388 1
1.2%
35.114184 1
1.2%
35.115605 1
1.2%
35.124487 1
1.2%
35.265556 1
1.2%
35.267366 1
1.2%
35.269063 1
1.2%
35.273255 1
1.2%
35.274444 1
1.2%
ValueCountFrequency (%)
38.177481 1
1.2%
38.15555 1
1.2%
38.146656 1
1.2%
38.126593 1
1.2%
38.109641 1
1.2%
38.099459 1
1.2%
38.089724 1
1.2%
38.076144 1
1.2%
37.780883 1
1.2%
37.770121 1
1.2%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct85
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.89756
Minimum126.5155
Maximum129.20306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T05:49:20.873516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5155
5-th percentile126.82886
Q1127.52831
median127.918
Q3128.39404
95-th percentile128.94312
Maximum129.20306
Range2.687564
Interquartile range (IQR)0.865731

Descriptive statistics

Standard deviation0.62979628
Coefficient of variation (CV)0.0049242243
Kurtosis-0.24364477
Mean127.89756
Median Absolute Deviation (MAD)0.395968
Skewness-0.031676592
Sum10871.293
Variance0.39664335
MonotonicityNot monotonic
2023-12-13T05:49:21.316473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.752596 1
 
1.2%
128.037415 1
 
1.2%
128.185365 1
 
1.2%
128.091353 1
 
1.2%
128.099078 1
 
1.2%
128.056311 1
 
1.2%
128.099703 1
 
1.2%
128.080278 1
 
1.2%
128.054167 1
 
1.2%
128.026299 1
 
1.2%
Other values (75) 75
88.2%
ValueCountFrequency (%)
126.5155 1
1.2%
126.573932 1
1.2%
126.693611 1
1.2%
126.698055 1
1.2%
126.825531 1
1.2%
126.842169 1
1.2%
126.887694 1
1.2%
126.992243 1
1.2%
126.997874 1
1.2%
127.002616 1
1.2%
ValueCountFrequency (%)
129.203064 1
1.2%
129.199808 1
1.2%
129.194811 1
1.2%
129.191217 1
1.2%
128.944066 1
1.2%
128.939346 1
1.2%
128.932648 1
1.2%
128.702259 1
1.2%
128.6403 1
1.2%
128.5707 1
1.2%

Interactions

2023-12-13T05:49:18.242910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:17.628878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:17.923306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:18.327817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:17.719444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:18.030245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:18.417378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:17.826282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:18.149286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:49:21.409911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사무소국소명위도경도
연번1.0000.9641.0000.9230.750
사무소0.9641.0001.0000.9950.964
국소명1.0001.0001.0001.0001.000
위도0.9230.9951.0001.0000.847
경도0.7500.9641.0000.8471.000
2023-12-13T05:49:21.495739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도사무소
연번1.0000.3230.2730.745
위도0.3231.0000.7270.891
경도0.2730.7271.0000.750
사무소0.7450.8910.7501.000

Missing values

2023-12-13T05:49:18.538447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:49:18.643495image/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

연번사무소국소명국종위도경도
01지리산경남조개골우량국35.370908127.752596
12지리산경남작은새재골우량국35.297763127.666583
23지리산경남선녀탕우량국35.369158127.698212
34지리산전북뱀사골우량국35.3077127.5861
45지리산전북심원우량국35.363885127.522037
56지리산전북덕동우량국35.3641127.5736
67지리산전북와운우량국35.3586127.5875
78지리산전북달궁우량국35.3452127.5511
89지리산전남화엄사1우량국35.269063127.506402
910지리산전남화엄사2우량국35.287561127.52831
연번사무소국소명국종위도경도
7576월출산바람재우량국34.759444126.698055
7677월출산구정봉우량국34.761388126.693611
7778변산반도내변산우량국35.632703126.573932
7879무등산무등산중봉우량국35.124487126.992243
7980무등산장불재우량국35.115605126.997874
8081무등산동부도원우량국35.114184127.002616
8182태백산문수봉1우량국37.090454128.939346
8283태백산문수봉2우량국37.076576128.932648
8384태백산부쇠봉우량국37.092139128.004541
8485태백산소문수봉우량국37.095066128.944066