Overview

Dataset statistics

Number of variables4
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory39.9 B

Variable types

Numeric2
Text1
Categorical1

Dataset

Description가뭄 분석 정보 제공을 위한 북한의 GTS 관측소코드, 관측소명, 표고 등 북한 GTS 관측소 시설 제원정보 데이터 항목을 제공합니다.
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049838/fileData.do

Alerts

관측소 코드 has unique valuesUnique
관측소 명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:35:00.699745
Analysis finished2023-12-12 20:35:01.438025
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소 코드
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47041.481
Minimum47003
Maximum47075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:35:01.499376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47003
5-th percentile47005.9
Q147023.5
median47041
Q347060.5
95-th percentile47069.7
Maximum47075
Range72
Interquartile range (IQR)37

Descriptive statistics

Standard deviation22.183449
Coefficient of variation (CV)0.00047157208
Kurtosis-1.2185411
Mean47041.481
Median Absolute Deviation (MAD)19
Skewness-0.20368301
Sum1270120
Variance492.10541
MonotonicityStrictly increasing
2023-12-13T05:35:01.712755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
47003 1
 
3.7%
47005 1
 
3.7%
47075 1
 
3.7%
47070 1
 
3.7%
47069 1
 
3.7%
47068 1
 
3.7%
47067 1
 
3.7%
47065 1
 
3.7%
47061 1
 
3.7%
47060 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
47003 1
3.7%
47005 1
3.7%
47008 1
3.7%
47014 1
3.7%
47016 1
3.7%
47020 1
3.7%
47022 1
3.7%
47025 1
3.7%
47028 1
3.7%
47031 1
3.7%
ValueCountFrequency (%)
47075 1
3.7%
47070 1
3.7%
47069 1
3.7%
47068 1
3.7%
47067 1
3.7%
47065 1
3.7%
47061 1
3.7%
47060 1
3.7%
47058 1
3.7%
47055 1
3.7%

관측소 명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T05:35:01.965195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1111111
Min length2

Characters and Unicode

Total characters57
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row선봉
2nd row삼지연
3rd row청진
4th row중강
5th row혜산
ValueCountFrequency (%)
선봉 1
 
3.7%
신포 1
 
3.7%
개성 1
 
3.7%
해주 1
 
3.7%
용연 1
 
3.7%
신계 1
 
3.7%
사리원 1
 
3.7%
장전 1
 
3.7%
남포 1
 
3.7%
평양 1
 
3.7%
Other values (17) 17
63.0%
2023-12-13T05:35:02.343161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 3
 
5.3%
신 3
 
5.3%
강 3
 
5.3%
산 3
 
5.3%
계 2
 
3.5%
포 2
 
3.5%
성 2
 
3.5%
양 2
 
3.5%
장 2
 
3.5%
원 2
 
3.5%
Other values (29) 33
57.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3
 
5.3%
신 3
 
5.3%
강 3
 
5.3%
산 3
 
5.3%
계 2
 
3.5%
포 2
 
3.5%
성 2
 
3.5%
양 2
 
3.5%
장 2
 
3.5%
원 2
 
3.5%
Other values (29) 33
57.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3
 
5.3%
신 3
 
5.3%
강 3
 
5.3%
산 3
 
5.3%
계 2
 
3.5%
포 2
 
3.5%
성 2
 
3.5%
양 2
 
3.5%
장 2
 
3.5%
원 2
 
3.5%
Other values (29) 33
57.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 3
 
5.3%
신 3
 
5.3%
강 3
 
5.3%
산 3
 
5.3%
계 2
 
3.5%
포 2
 
3.5%
성 2
 
3.5%
양 2
 
3.5%
장 2
 
3.5%
원 2
 
3.5%
Other values (29) 33
57.9%

관측개시일
Categorical

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
1981
14 
1973
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1973
2nd row1981
3rd row1973
4th row1973
5th row1973

Common Values

ValueCountFrequency (%)
1981 14
51.9%
1973 13
48.1%

Length

2023-12-13T05:35:02.492655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:35:02.605920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1981 14
51.9%
1973 13
48.1%

표고
Real number (ℝ)

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.77778
Minimum3
Maximum1386
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:35:02.694738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5.6
Q135.5
median70
Q3292.5
95-th percentile1168.5
Maximum1386
Range1383
Interquartile range (IQR)257

Descriptive statistics

Standard deviation388.20091
Coefficient of variation (CV)1.5794793
Kurtosis3.2533765
Mean245.77778
Median Absolute Deviation (MAD)47
Skewness2.058832
Sum6636
Variance150699.95
MonotonicityNot monotonic
2023-12-13T05:35:02.806315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
38 2
 
7.4%
19 1
 
3.7%
371 1
 
3.7%
70 1
 
3.7%
81 1
 
3.7%
5 1
 
3.7%
100 1
 
3.7%
52 1
 
3.7%
35 1
 
3.7%
47 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
3 1
3.7%
5 1
3.7%
7 1
3.7%
19 1
3.7%
23 1
3.7%
27 1
3.7%
35 1
3.7%
36 1
3.7%
38 2
7.4%
43 1
3.7%
ValueCountFrequency (%)
1386 1
3.7%
1206 1
3.7%
1081 1
3.7%
714 1
3.7%
371 1
3.7%
332 1
3.7%
306 1
3.7%
279 1
3.7%
155 1
3.7%
100 1
3.7%

Interactions

2023-12-13T05:35:01.084332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:35:00.826548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:35:01.183325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:35:00.965305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:35:02.913472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소 코드관측소 명관측개시일표고
관측소 코드1.0001.0000.0000.556
관측소 명1.0001.0001.0001.000
관측개시일0.0001.0001.0000.148
표고0.5561.0000.1481.000
2023-12-13T05:35:03.030763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소 코드표고관측개시일
관측소 코드1.000-0.1960.000
표고-0.1961.0000.107
관측개시일0.0000.1071.000

Missing values

2023-12-13T05:35:01.316634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:35:01.404563image/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

관측소 코드관측소 명관측개시일표고
047003선봉19733
147005삼지연19811386
247008청진197343
347014중강1973332
447016혜산1973714
547020강계1973306
647022풍산19811206
747025김책197323
847028수풍198183
947031장진19811081
관측소 코드관측소 명관측개시일표고
1747055원산197336
1847058평양197338
1947060남포198147
2047061장전198135
2147065사리원197352
2247067신계1981100
2347068용연19815
2447069해주197381
2547070개성197370
2647075평강1981371