Overview

Dataset statistics

Number of variables3
Number of observations850
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.7 KiB
Average record size in memory26.2 B

Variable types

Numeric2
Text1

Dataset

Description제방월류, 제방파제, 내수침수 등 홍수시나리오에 따라 예상되는 최대 범람지역을 표시하는 홍수위험지도에 대하여 수자원단위지도의 표준유역을 표현
Author환경부 한강홍수통제소
URLhttps://www.data.go.kr/data/15123324/fileData.do

Alerts

표준유역코드 is highly overall correlated with 중권역코드High correlation
중권역코드 is highly overall correlated with 표준유역코드High correlation
표준유역코드 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:52:44.241967
Analysis finished2023-12-11 23:52:44.888016
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

표준유역코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236577.32
Minimum100101
Maximum600405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2023-12-12T08:52:44.963484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100101
5-th percentile100313.45
Q1102201.25
median201554.5
Q3301377
95-th percentile510101.55
Maximum600405
Range500304
Interquartile range (IQR)199175.75

Descriptive statistics

Standard deviation132570.7
Coefficient of variation (CV)0.56036944
Kurtosis0.016057783
Mean236577.32
Median Absolute Deviation (MAD)99648.5
Skewness0.90103938
Sum2.0109072 × 108
Variance1.7574991 × 1010
MonotonicityStrictly increasing
2023-12-12T08:52:45.096434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100101 1
 
0.1%
250407 1
 
0.1%
250409 1
 
0.1%
300101 1
 
0.1%
300102 1
 
0.1%
300103 1
 
0.1%
300104 1
 
0.1%
300105 1
 
0.1%
300106 1
 
0.1%
300107 1
 
0.1%
Other values (840) 840
98.8%
ValueCountFrequency (%)
100101 1
0.1%
100102 1
0.1%
100103 1
0.1%
100104 1
0.1%
100105 1
0.1%
100106 1
0.1%
100107 1
0.1%
100108 1
0.1%
100109 1
0.1%
100110 1
0.1%
ValueCountFrequency (%)
600405 1
0.1%
600404 1
0.1%
600403 1
0.1%
600402 1
0.1%
600401 1
0.1%
600304 1
0.1%
600303 1
0.1%
600302 1
0.1%
600301 1
0.1%
600204 1
0.1%

표준유역명
Text

Distinct820
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2023-12-12T08:52:45.379715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.9905882
Min length2

Characters and Unicode

Total characters3392
Distinct characters237
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique800 ?
Unique (%)94.1%

Sample

1st row광동댐
2nd row광동댐하류
3rd row임계천
4th row골지천중류
5th row도암댐
ValueCountFrequency (%)
남천 5
 
0.6%
동천 4
 
0.5%
북천 4
 
0.5%
남대천 3
 
0.4%
광천 3
 
0.4%
한천 3
 
0.4%
석교천 2
 
0.2%
금천 2
 
0.2%
화양천 2
 
0.2%
사천천 2
 
0.2%
Other values (810) 820
96.5%
2023-12-12T08:52:45.904490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 608
 
17.9%
류 288
 
8.5%
하 124
 
3.7%
상 102
 
3.0%
강 102
 
3.0%
댐 95
 
2.8%
수 92
 
2.7%
위 79
 
2.3%
표 71
 
2.1%
합 69
 
2.0%
Other values (227) 1762
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3390
99.9%
Decimal Number 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 608
 
17.9%
류 288
 
8.5%
하 124
 
3.7%
상 102
 
3.0%
강 102
 
3.0%
댐 95
 
2.8%
수 92
 
2.7%
위 79
 
2.3%
표 71
 
2.1%
합 69
 
2.0%
Other values (225) 1760
51.9%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3390
99.9%
Common 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 608
 
17.9%
류 288
 
8.5%
하 124
 
3.7%
상 102
 
3.0%
강 102
 
3.0%
댐 95
 
2.8%
수 92
 
2.7%
위 79
 
2.3%
표 71
 
2.1%
합 69
 
2.0%
Other values (225) 1760
51.9%
Common
ValueCountFrequency (%)
2 1
50.0%
· 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3390
99.9%
ASCII 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 608
 
17.9%
류 288
 
8.5%
하 124
 
3.7%
상 102
 
3.0%
강 102
 
3.0%
댐 95
 
2.8%
수 92
 
2.7%
위 79
 
2.3%
표 71
 
2.1%
합 69
 
2.0%
Other values (225) 1760
51.9%
ASCII
ValueCountFrequency (%)
2 1
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

중권역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2365.7176
Minimum1001
Maximum6004
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2023-12-12T08:52:46.109751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1003
Q11022
median2015.5
Q33013.75
95-th percentile5101
Maximum6004
Range5003
Interquartile range (IQR)1991.75

Descriptive statistics

Standard deviation1325.7181
Coefficient of variation (CV)0.5603873
Kurtosis0.016056697
Mean2365.7176
Median Absolute Deviation (MAD)996.5
Skewness0.90103739
Sum2010860
Variance1757528.6
MonotonicityIncreasing
2023-12-12T08:52:46.250735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1003 20
 
2.4%
2012 19
 
2.2%
1007 19
 
2.2%
1101 18
 
2.1%
1001 17
 
2.0%
2004 17
 
2.0%
2002 16
 
1.9%
1018 16
 
1.9%
3101 16
 
1.9%
1022 15
 
1.8%
Other values (107) 677
79.6%
ValueCountFrequency (%)
1001 17
2.0%
1002 13
1.5%
1003 20
2.4%
1004 14
1.6%
1005 5
 
0.6%
1006 10
1.2%
1007 19
2.2%
1008 13
1.5%
1009 4
 
0.5%
1010 12
1.4%
ValueCountFrequency (%)
6004 5
0.6%
6003 4
0.5%
6002 4
0.5%
6001 3
 
0.4%
5303 1
 
0.1%
5302 9
1.1%
5301 4
0.5%
5202 6
0.7%
5201 4
0.5%
5101 4
0.5%

Interactions

2023-12-12T08:52:44.567082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:52:44.387569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:52:44.652161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:52:44.477808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:52:46.332829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준유역코드중권역코드
표준유역코드1.0001.000
중권역코드1.0001.000
2023-12-12T08:52:46.404446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준유역코드중권역코드
표준유역코드1.0001.000
중권역코드1.0001.000

Missing values

2023-12-12T08:52:44.780690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:52:44.857705image/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

표준유역코드표준유역명중권역코드
0100101광동댐1001
1100102광동댐하류1001
2100103임계천1001
3100104골지천중류1001
4100105도암댐1001
5100106송천1001
6100107골지천하류1001
7100108오대천상류1001
8100109오대천하류1001
9100110어천상류1001
표준유역코드표준유역명중권역코드
840600204화북천6002
841600301창고천6003
842600302예래천6003
843600303도순천6003
844600304신례천6003
845600401조천읍6004
846600402구좌읍6004
847600403성산읍6004
848600404천미천6004
849600405종남천6004