Overview

Dataset statistics

Number of variables7
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory64.5 B

Variable types

Numeric3
Text3
Categorical1

Dataset

Description전라남도_영광군_배수펌프장 현황 자료입니다. 내용으로는 배수장, 위치, 준공년도, 설치목적, 규모, 계약전력 등의 항목을 제공합니다.
Author전라남도 영광군
URLhttps://www.data.go.kr/data/15021454/fileData.do

Alerts

설치목적 has constant value ""Constant
연번 is highly overall correlated with 준공년도High correlation
준공년도 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:27:01.253441
Analysis finished2023-12-12 17:27:02.838685
Duration1.59 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:27:02.908541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2023-12-13T02:27:03.044692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

배수장
Text

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T02:27:03.235325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.5416667
Min length2

Characters and Unicode

Total characters61
Distinct characters35
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

Unique22 ?
Unique (%)91.7%

Sample

1st row장호
2nd row구수
3rd row창포
4th row장산
5th row칠성동
ValueCountFrequency (%)
2 3
 
10.3%
장산 3
 
10.3%
양평 2
 
6.9%
월계 2
 
6.9%
와룡 2
 
6.9%
1 2
 
6.9%
구평 1
 
3.4%
신월 1
 
3.4%
와전 1
 
3.4%
칠성동 1
 
3.4%
Other values (11) 11
37.9%
2023-12-13T02:27:03.589965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
8.2%
평 4
 
6.6%
장 4
 
6.6%
2 3
 
4.9%
산 3
 
4.9%
와 3
 
4.9%
동 3
 
4.9%
월 3
 
4.9%
계 2
 
3.3%
1 2
 
3.3%
Other values (25) 29
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51
83.6%
Space Separator 5
 
8.2%
Decimal Number 5
 
8.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
평 4
 
7.8%
장 4
 
7.8%
산 3
 
5.9%
와 3
 
5.9%
동 3
 
5.9%
월 3
 
5.9%
계 2
 
3.9%
룡 2
 
3.9%
전 2
 
3.9%
양 2
 
3.9%
Other values (22) 23
45.1%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51
83.6%
Common 10
 
16.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
평 4
 
7.8%
장 4
 
7.8%
산 3
 
5.9%
와 3
 
5.9%
동 3
 
5.9%
월 3
 
5.9%
계 2
 
3.9%
룡 2
 
3.9%
전 2
 
3.9%
양 2
 
3.9%
Other values (22) 23
45.1%
Common
ValueCountFrequency (%)
5
50.0%
2 3
30.0%
1 2
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51
83.6%
ASCII 10
 
16.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5
50.0%
2 3
30.0%
1 2
 
20.0%
Hangul
ValueCountFrequency (%)
평 4
 
7.8%
장 4
 
7.8%
산 3
 
5.9%
와 3
 
5.9%
동 3
 
5.9%
월 3
 
5.9%
계 2
 
3.9%
룡 2
 
3.9%
전 2
 
3.9%
양 2
 
3.9%
Other values (22) 23
45.1%

위치
Text

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T02:27:03.760205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters168
Distinct characters35
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

Unique7 ?
Unique (%)29.2%

Sample

1st row군서면 만금리
2nd row백수읍 구수리
3rd row영광읍 송림리
4th row백수읍 장산리
5th row영광읍 덕호리
ValueCountFrequency (%)
영광읍 11
22.9%
법성면 6
12.5%
양평리 4
 
8.3%
백수읍 4
 
8.3%
와룡리 4
 
8.3%
장산리 3
 
6.2%
덕호리 2
 
4.2%
월산리 2
 
4.2%
신장리 2
 
4.2%
염산면 2
 
4.2%
Other values (8) 8
16.7%
2023-12-13T02:27:04.053261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 24
14.3%
24
14.3%
읍 15
 
8.9%
영 11
 
6.5%
광 11
 
6.5%
면 9
 
5.4%
산 7
 
4.2%
성 6
 
3.6%
법 6
 
3.6%
수 5
 
3.0%
Other values (25) 50
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
85.7%
Space Separator 24
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 24
16.7%
읍 15
 
10.4%
영 11
 
7.6%
광 11
 
7.6%
면 9
 
6.2%
산 7
 
4.9%
성 6
 
4.2%
법 6
 
4.2%
수 5
 
3.5%
장 5
 
3.5%
Other values (24) 45
31.2%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
85.7%
Common 24
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 24
16.7%
읍 15
 
10.4%
영 11
 
7.6%
광 11
 
7.6%
면 9
 
6.2%
산 7
 
4.9%
성 6
 
4.2%
법 6
 
4.2%
수 5
 
3.5%
장 5
 
3.5%
Other values (24) 45
31.2%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
85.7%
ASCII 24
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 24
16.7%
읍 15
 
10.4%
영 11
 
7.6%
광 11
 
7.6%
면 9
 
6.2%
산 7
 
4.9%
성 6
 
4.2%
법 6
 
4.2%
수 5
 
3.5%
장 5
 
3.5%
Other values (24) 45
31.2%
ASCII
ValueCountFrequency (%)
24
100.0%

준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.9583
Minimum1996
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:27:04.210675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile1997
Q12000
median2001
Q32006
95-th percentile2016
Maximum2021
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.0862952
Coefficient of variation (CV)0.003536149
Kurtosis0.34415313
Mean2003.9583
Median Absolute Deviation (MAD)1.5
Skewness1.2571232
Sum48095
Variance50.21558
MonotonicityNot monotonic
2023-12-13T02:27:04.331440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2001 6
25.0%
2000 3
12.5%
2002 3
12.5%
2016 3
12.5%
1997 2
 
8.3%
1999 2
 
8.3%
2006 2
 
8.3%
1996 1
 
4.2%
2014 1
 
4.2%
2021 1
 
4.2%
ValueCountFrequency (%)
1996 1
 
4.2%
1997 2
 
8.3%
1999 2
 
8.3%
2000 3
12.5%
2001 6
25.0%
2002 3
12.5%
2006 2
 
8.3%
2014 1
 
4.2%
2016 3
12.5%
2021 1
 
4.2%
ValueCountFrequency (%)
2021 1
 
4.2%
2016 3
12.5%
2014 1
 
4.2%
2006 2
 
8.3%
2002 3
12.5%
2001 6
25.0%
2000 3
12.5%
1999 2
 
8.3%
1997 2
 
8.3%
1996 1
 
4.2%

설치목적
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
농경지 침수예방
24 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농경지 침수예방
2nd row농경지 침수예방
3rd row농경지 침수예방
4th row농경지 침수예방
5th row농경지 침수예방

Common Values

ValueCountFrequency (%)
농경지 침수예방 24
100.0%

Length

2023-12-13T02:27:04.458676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:27:04.560387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농경지 24
50.0%
침수예방 24
50.0%

규모
Text

Distinct14
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T02:27:04.740087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length12
Mean length13.083333
Min length12

Characters and Unicode

Total characters314
Distinct characters16
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)41.7%

Sample

1st row60HP×500㎜×2대
2nd row60HP×250㎜×2대
3rd row50HP×300㎜×2대
4th row40HP×400㎜×2대
5th row40HP×400㎜×2대
ValueCountFrequency (%)
40hp×400㎜×2대 7
25.9%
20 3
11.1%
30,hp×350㎜ 3
11.1%
50hp×400㎜×2대 2
 
7.4%
100hp×500㎜×2대 2
 
7.4%
60hp×500㎜×2대 1
 
3.7%
60hp×250㎜×2대 1
 
3.7%
50hp×300㎜×2대 1
 
3.7%
20hp×250㎜×1대 1
 
3.7%
30hp×300㎜×2대 1
 
3.7%
Other values (5) 5
18.5%
2023-12-13T02:27:05.085845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 76
24.2%
× 47
15.0%
2 27
 
8.6%
H 25
 
8.0%
P 25
 
8.0%
㎜ 25
 
8.0%
대 22
 
7.0%
4 16
 
5.1%
5 14
 
4.5%
3 12
 
3.8%
Other values (6) 25
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159
50.6%
Uppercase Letter 50
 
15.9%
Math Symbol 47
 
15.0%
Other Symbol 25
 
8.0%
Other Letter 22
 
7.0%
Other Punctuation 8
 
2.5%
Space Separator 3
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76
47.8%
2 27
 
17.0%
4 16
 
10.1%
5 14
 
8.8%
3 12
 
7.5%
1 10
 
6.3%
6 2
 
1.3%
9 1
 
0.6%
7 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
H 25
50.0%
P 25
50.0%
Math Symbol
ValueCountFrequency (%)
× 47
100.0%
Other Symbol
ValueCountFrequency (%)
㎜ 25
100.0%
Other Letter
ValueCountFrequency (%)
대 22
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 242
77.1%
Latin 50
 
15.9%
Hangul 22
 
7.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76
31.4%
× 47
19.4%
2 27
 
11.2%
㎜ 25
 
10.3%
4 16
 
6.6%
5 14
 
5.8%
3 12
 
5.0%
1 10
 
4.1%
, 8
 
3.3%
3
 
1.2%
Other values (3) 4
 
1.7%
Latin
ValueCountFrequency (%)
H 25
50.0%
P 25
50.0%
Hangul
ValueCountFrequency (%)
대 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220
70.1%
None 47
 
15.0%
CJK Compat 25
 
8.0%
Hangul 22
 
7.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76
34.5%
2 27
 
12.3%
H 25
 
11.4%
P 25
 
11.4%
4 16
 
7.3%
5 14
 
6.4%
3 12
 
5.5%
1 10
 
4.5%
, 8
 
3.6%
3
 
1.4%
Other values (3) 4
 
1.8%
None
ValueCountFrequency (%)
× 47
100.0%
CJK Compat
ValueCountFrequency (%)
㎜ 25
100.0%
Hangul
ValueCountFrequency (%)
대 22
100.0%

계약전력
Real number (ℝ)

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.541667
Minimum9
Maximum864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:27:05.266576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile20.8
Q141
median56.5
Q368.5
95-th percentile147.9
Maximum864
Range855
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation167.21373
Coefficient of variation (CV)1.8069021
Kurtosis22.159306
Mean92.541667
Median Absolute Deviation (MAD)13.5
Skewness4.6362129
Sum2221
Variance27960.433
MonotonicityNot monotonic
2023-12-13T02:27:05.399644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
56 3
 
12.5%
62 3
 
12.5%
34 2
 
8.3%
35 1
 
4.2%
43 1
 
4.2%
75 1
 
4.2%
150 1
 
4.2%
74 1
 
4.2%
864 1
 
4.2%
65 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
9 1
 
4.2%
19 1
 
4.2%
31 1
 
4.2%
34 2
8.3%
35 1
 
4.2%
43 1
 
4.2%
51 1
 
4.2%
52 1
 
4.2%
56 3
12.5%
57 1
 
4.2%
ValueCountFrequency (%)
864 1
 
4.2%
150 1
 
4.2%
136 1
 
4.2%
75 1
 
4.2%
74 1
 
4.2%
70 1
 
4.2%
68 1
 
4.2%
65 1
 
4.2%
62 3
12.5%
57 1
 
4.2%

Interactions

2023-12-13T02:27:02.064341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:01.509969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:01.754225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:02.166119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:01.594826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:01.850789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:02.265958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:01.678407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:27:01.939927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:27:05.485802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번배수장위치준공년도규모계약전력
연번1.0001.0000.6990.8430.3090.402
배수장1.0001.0001.0000.0000.6641.000
위치0.6991.0001.0000.4540.8220.918
준공년도0.8430.0000.4541.0000.7580.000
규모0.3090.6640.8220.7581.0001.000
계약전력0.4021.0000.9180.0001.0001.000
2023-12-13T02:27:05.625969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번준공년도계약전력
연번1.0000.9210.248
준공년도0.9211.0000.344
계약전력0.2480.3441.000

Missing values

2023-12-13T02:27:02.661042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:27:02.790169image/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

연번배수장위치준공년도설치목적규모계약전력
01장호군서면 만금리1996농경지 침수예방60HP×500㎜×2대56
12구수백수읍 구수리1997농경지 침수예방60HP×250㎜×2대57
23창포영광읍 송림리1997농경지 침수예방50HP×300㎜×2대51
34장산백수읍 장산리1999농경지 침수예방40HP×400㎜×2대62
45칠성동영광읍 덕호리1999농경지 침수예방40HP×400㎜×2대62
56월계법성면 월산리2000농경지 침수예방40HP×400㎜×2대70
67구평영광읍 와룡리2000농경지 침수예방20HP×250㎜×1대19
78신월영광읍 양평리2001농경지 침수예방30HP×300㎜×2대68
89와전영광읍 와룡리2001농경지 침수예방50HP×400㎜×2대34
910와룡 1영광읍 와룡리2001농경지 침수예방20, 30,HP×350㎜31
연번배수장위치준공년도설치목적규모계약전력
1415월계 2법성면 월산리2002농경지 침수예방40HP×400㎜×2대35
1516지안일법성면 덕흥리2000농경지 침수예방10HP×200㎜×1대9
1617장산 1백수읍 장산리2006농경지 침수예방40HP×400㎜×2대65
1718장산 2영광읍 덕호리2006농경지 침수예방40HP×400㎜×2대62
1819입암법성면 입암리2001농경지 침수예방40HP×400㎜×2대56
1920두우염산면 두우리2014농경지 침수예방200HP×1,350㎜×1대,100HP×900㎜×1대864
2021평전영광읍 양평리2016농경지 침수예방50HP×400㎜×2대74
2122미동염산면 옥실리2016농경지 침수예방100HP×500㎜×2대150
2223양평영광읍 양평리2016농경지 침수예방100HP×700㎜×2대75
2324양평영광읍 양평리2021농경지 침수예방20HP×300㎜×2대43