Overview

Dataset statistics

Number of variables14
Number of observations25
Missing cells19
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory120.3 B

Variable types

Text5
Numeric1
Categorical8

Dataset

Description경상남도 창녕군 재난대응용 배수펌프장 시설현황에 대한 데이터를 포함하고 있습니다.(배수장명칭, 배수장위치, 준공연도, 보강연도, 펌프마력, 펌프대수, 유수지용량, 수문수, 수혜지역, 방류하천명, 관리기관명, 관리부서, 운영기간)
URLhttps://www.data.go.kr/data/15021461/fileData.do

Alerts

관리기관명 has constant value ""Constant
유수지용량(세제곱미터) is highly overall correlated with 준공연도 and 5 other fieldsHigh correlation
방류하천명 is highly overall correlated with 유수지용량(세제곱미터)High correlation
펌프대수 is highly overall correlated with 준공연도 and 2 other fieldsHigh correlation
운영기간 is highly overall correlated with 유수지용량(세제곱미터)High correlation
수문(기) is highly overall correlated with 펌프대수 and 1 other fieldsHigh correlation
관리부서 is highly overall correlated with 유수지용량(세제곱미터)High correlation
준공연도 is highly overall correlated with 펌프대수 and 1 other fieldsHigh correlation
보강연도 is highly imbalanced (75.8%)Imbalance
유수지용량(세제곱미터) is highly imbalanced (69.6%)Imbalance
펌프마력(HP) has 19 (76.0%) missing valuesMissing
배수장명칭 has unique valuesUnique
배수장위치 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:26:27.353288
Analysis finished2023-12-12 09:26:28.697103
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

배수장명칭
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T18:26:28.800080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.24
Min length6

Characters and Unicode

Total characters156
Distinct characters41
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

Unique25 ?
Unique (%)100.0%

Sample

1st row창아지 배수장
2nd row영아지 배수장
3rd row양정 배수장
4th row상리 배수장
5th row우만 배수장
ValueCountFrequency (%)
배수장 24
49.0%
창아지 1
 
2.0%
대성 1
 
2.0%
신덕 1
 
2.0%
어릿골배수장 1
 
2.0%
상남 1
 
2.0%
남지 1
 
2.0%
비봉1 1
 
2.0%
노리 1
 
2.0%
청암 1
 
2.0%
Other values (16) 16
32.7%
2023-12-12T18:26:29.153486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
배 25
16.0%
수 25
16.0%
장 25
16.0%
24
15.4%
지 3
 
1.9%
봉 3
 
1.9%
리 3
 
1.9%
아 3
 
1.9%
신 2
 
1.3%
성 2
 
1.3%
Other values (31) 41
26.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128
82.1%
Space Separator 24
 
15.4%
Decimal Number 4
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
배 25
19.5%
수 25
19.5%
장 25
19.5%
지 3
 
2.3%
봉 3
 
2.3%
리 3
 
2.3%
아 3
 
2.3%
신 2
 
1.6%
성 2
 
1.6%
대 2
 
1.6%
Other values (28) 35
27.3%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128
82.1%
Common 28
 
17.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
배 25
19.5%
수 25
19.5%
장 25
19.5%
지 3
 
2.3%
봉 3
 
2.3%
리 3
 
2.3%
아 3
 
2.3%
신 2
 
1.6%
성 2
 
1.6%
대 2
 
1.6%
Other values (28) 35
27.3%
Common
ValueCountFrequency (%)
24
85.7%
2 2
 
7.1%
1 2
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128
82.1%
ASCII 28
 
17.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
배 25
19.5%
수 25
19.5%
장 25
19.5%
지 3
 
2.3%
봉 3
 
2.3%
리 3
 
2.3%
아 3
 
2.3%
신 2
 
1.6%
성 2
 
1.6%
대 2
 
1.6%
Other values (28) 35
27.3%
ASCII
ValueCountFrequency (%)
24
85.7%
2 2
 
7.1%
1 2
 
7.1%

배수장위치
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T18:26:29.711329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length15.76
Min length13

Characters and Unicode

Total characters394
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row창녕군 남지읍 아지리 940-1
2nd row창녕군 남지읍 신전리 938-3
3rd row창녕군 이방면 거남리 483-1
4th row창녕군 이방면 상리 435-1
5th row창녕군 이방면 안리 1263-5
ValueCountFrequency (%)
창녕군 25
25.0%
이방면 6
 
6.0%
남지 5
 
5.0%
부곡 3
 
3.0%
안리 2
 
2.0%
길곡 2
 
2.0%
남지읍 2
 
2.0%
세진 2
 
2.0%
유어 2
 
2.0%
이방 2
 
2.0%
Other values (48) 49
49.0%
2023-12-12T18:26:30.113698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76
19.3%
창 25
 
6.3%
군 25
 
6.3%
녕 25
 
6.3%
- 22
 
5.6%
3 21
 
5.3%
리 16
 
4.1%
1 15
 
3.8%
9 10
 
2.5%
4 10
 
2.5%
Other values (45) 149
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 197
50.0%
Decimal Number 99
25.1%
Space Separator 76
 
19.3%
Dash Punctuation 22
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 25
12.7%
군 25
12.7%
녕 25
12.7%
리 16
 
8.1%
면 9
 
4.6%
지 9
 
4.6%
곡 8
 
4.1%
이 8
 
4.1%
방 8
 
4.1%
남 8
 
4.1%
Other values (33) 56
28.4%
Decimal Number
ValueCountFrequency (%)
3 21
21.2%
1 15
15.2%
9 10
10.1%
4 10
10.1%
2 9
9.1%
5 8
 
8.1%
7 7
 
7.1%
6 7
 
7.1%
8 6
 
6.1%
0 6
 
6.1%
Space Separator
ValueCountFrequency (%)
76
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 197
50.0%
Hangul 197
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 25
12.7%
군 25
12.7%
녕 25
12.7%
리 16
 
8.1%
면 9
 
4.6%
지 9
 
4.6%
곡 8
 
4.1%
이 8
 
4.1%
방 8
 
4.1%
남 8
 
4.1%
Other values (33) 56
28.4%
Common
ValueCountFrequency (%)
76
38.6%
- 22
 
11.2%
3 21
 
10.7%
1 15
 
7.6%
9 10
 
5.1%
4 10
 
5.1%
2 9
 
4.6%
5 8
 
4.1%
7 7
 
3.6%
6 7
 
3.6%
Other values (2) 12
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197
50.0%
Hangul 197
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76
38.6%
- 22
 
11.2%
3 21
 
10.7%
1 15
 
7.6%
9 10
 
5.1%
4 10
 
5.1%
2 9
 
4.6%
5 8
 
4.1%
7 7
 
3.6%
6 7
 
3.6%
Other values (2) 12
 
6.1%
Hangul
ValueCountFrequency (%)
창 25
12.7%
군 25
12.7%
녕 25
12.7%
리 16
 
8.1%
면 9
 
4.6%
지 9
 
4.6%
곡 8
 
4.1%
이 8
 
4.1%
방 8
 
4.1%
남 8
 
4.1%
Other values (33) 56
28.4%

준공연도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.48
Minimum1984
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T18:26:30.287638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile1988.2
Q12003
median2004
Q32008
95-th percentile2013.8
Maximum2021
Range37
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.2367065
Coefficient of variation (CV)0.0041091487
Kurtosis1.3254915
Mean2004.48
Median Absolute Deviation (MAD)3
Skewness-0.76407062
Sum50112
Variance67.843333
MonotonicityNot monotonic
2023-12-12T18:26:30.450114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2004 5
20.0%
2006 3
12.0%
2003 2
 
8.0%
2001 2
 
8.0%
2012 2
 
8.0%
2013 2
 
8.0%
2005 1
 
4.0%
1984 1
 
4.0%
1988 1
 
4.0%
1989 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
1984 1
 
4.0%
1988 1
 
4.0%
1989 1
 
4.0%
2000 1
 
4.0%
2001 2
 
8.0%
2003 2
 
8.0%
2004 5
20.0%
2005 1
 
4.0%
2006 3
12.0%
2007 1
 
4.0%
ValueCountFrequency (%)
2021 1
 
4.0%
2014 1
 
4.0%
2013 2
 
8.0%
2012 2
 
8.0%
2008 1
 
4.0%
2007 1
 
4.0%
2006 3
12.0%
2005 1
 
4.0%
2004 5
20.0%
2003 2
 
8.0%

보강연도
Categorical

IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
24 
2017
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 24
96.0%
2017 1
 
4.0%

Length

2023-12-12T18:26:30.647907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:30.764772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
96.0%
2017 1
 
4.0%

펌프마력(HP)
Text

MISSING 

Distinct5
Distinct (%)83.3%
Missing19
Missing (%)76.0%
Memory size332.0 B
2023-12-12T18:26:30.899641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length3
Min length2

Characters and Unicode

Total characters18
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)66.7%

Sample

1st row200
2nd row40(308)
3rd row30
4th row40
5th row60
ValueCountFrequency (%)
30 2
33.3%
200 1
16.7%
40(308 1
16.7%
40 1
16.7%
60 1
16.7%
2023-12-12T18:26:31.225230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8
44.4%
3 3
 
16.7%
4 2
 
11.1%
2 1
 
5.6%
( 1
 
5.6%
8 1
 
5.6%
) 1
 
5.6%
6 1
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
88.9%
Open Punctuation 1
 
5.6%
Close Punctuation 1
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8
50.0%
3 3
 
18.8%
4 2
 
12.5%
2 1
 
6.2%
8 1
 
6.2%
6 1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8
44.4%
3 3
 
16.7%
4 2
 
11.1%
2 1
 
5.6%
( 1
 
5.6%
8 1
 
5.6%
) 1
 
5.6%
6 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8
44.4%
3 3
 
16.7%
4 2
 
11.1%
2 1
 
5.6%
( 1
 
5.6%
8 1
 
5.6%
) 1
 
5.6%
6 1
 
5.6%

펌프대수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2
17 
3
4 
4
2 
1
 
1
2(2)
 
1

Length

Max length4
Median length1
Mean length1.12
Min length1

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 17
68.0%
3 4
 
16.0%
4 2
 
8.0%
1 1
 
4.0%
2(2) 1
 
4.0%

Length

2023-12-12T18:26:31.381960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:31.551873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 17
68.0%
3 4
 
16.0%
4 2
 
8.0%
1 1
 
4.0%
2(2 1
 
4.0%

처리능력(세제곱미터,분)
Text

Distinct20
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T18:26:31.722445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.76
Min length2

Characters and Unicode

Total characters69
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)64.0%

Sample

1st row150
2nd row120
3rd row45
4th row150
5th row70
ValueCountFrequency (%)
160 3
 
12.0%
120 2
 
8.0%
150 2
 
8.0%
45 2
 
8.0%
614 1
 
4.0%
528 1
 
4.0%
23 1
 
4.0%
36 1
 
4.0%
24 1
 
4.0%
20(180 1
 
4.0%
Other values (10) 10
40.0%
2023-12-12T18:26:32.061142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
17.4%
0 12
17.4%
2 11
15.9%
5 6
8.7%
8 6
8.7%
3 6
8.7%
6 5
7.2%
4 4
 
5.8%
9 3
 
4.3%
7 2
 
2.9%
Other values (2) 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
97.1%
Open Punctuation 1
 
1.4%
Close Punctuation 1
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
17.9%
0 12
17.9%
2 11
16.4%
5 6
9.0%
8 6
9.0%
3 6
9.0%
6 5
7.5%
4 4
 
6.0%
9 3
 
4.5%
7 2
 
3.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
17.4%
0 12
17.4%
2 11
15.9%
5 6
8.7%
8 6
8.7%
3 6
8.7%
6 5
7.2%
4 4
 
5.8%
9 3
 
4.3%
7 2
 
2.9%
Other values (2) 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
17.4%
0 12
17.4%
2 11
15.9%
5 6
8.7%
8 6
8.7%
3 6
8.7%
6 5
7.2%
4 4
 
5.8%
9 3
 
4.3%
7 2
 
2.9%
Other values (2) 2
 
2.9%

유수지용량(세제곱미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
23 
30920.0
 
1
181.9
 
1

Length

Max length7
Median length4
Mean length4.16
Min length4

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 23
92.0%
30920.0 1
 
4.0%
181.9 1
 
4.0%

Length

2023-12-12T18:26:32.263152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:32.403563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 23
92.0%
30920.0 1
 
4.0%
181.9 1
 
4.0%

수문(기)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2
16 
3
4 
1
2 
6
 
1
4
 
1

Length

Max length4
Median length1
Mean length1.12
Min length1

Unique

Unique3 ?
Unique (%)12.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 16
64.0%
3 4
 
16.0%
1 2
 
8.0%
6 1
 
4.0%
4 1
 
4.0%
2(2) 1
 
4.0%

Length

2023-12-12T18:26:32.535254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:32.679278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 16
64.0%
3 4
 
16.0%
1 2
 
8.0%
6 1
 
4.0%
4 1
 
4.0%
2(2 1
 
4.0%

수혜지역
Text

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T18:26:32.886829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8
Min length2

Characters and Unicode

Total characters70
Distinct characters30
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

Unique17 ?
Unique (%)68.0%

Sample

1st row아지리
2nd row신전리
3rd row거남리
4th row상리
5th row안리
ValueCountFrequency (%)
남지리 2
 
8.0%
마천리 2
 
8.0%
안리 2
 
8.0%
세진리 2
 
8.0%
수다리 1
 
4.0%
아지리 1
 
4.0%
산지리 1
 
4.0%
도천리 1
 
4.0%
비봉리 1
 
4.0%
노리 1
 
4.0%
Other values (11) 11
44.0%
2023-12-12T18:26:33.240805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 25
35.7%
지 4
 
5.7%
남 3
 
4.3%
천 3
 
4.3%
곡 2
 
2.9%
봉 2
 
2.9%
대 2
 
2.9%
산 2
 
2.9%
성 2
 
2.9%
안 2
 
2.9%
Other values (20) 23
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 25
35.7%
지 4
 
5.7%
남 3
 
4.3%
천 3
 
4.3%
곡 2
 
2.9%
봉 2
 
2.9%
대 2
 
2.9%
산 2
 
2.9%
성 2
 
2.9%
안 2
 
2.9%
Other values (20) 23
32.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 25
35.7%
지 4
 
5.7%
남 3
 
4.3%
천 3
 
4.3%
곡 2
 
2.9%
봉 2
 
2.9%
대 2
 
2.9%
산 2
 
2.9%
성 2
 
2.9%
안 2
 
2.9%
Other values (20) 23
32.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 25
35.7%
지 4
 
5.7%
남 3
 
4.3%
천 3
 
4.3%
곡 2
 
2.9%
봉 2
 
2.9%
대 2
 
2.9%
산 2
 
2.9%
성 2
 
2.9%
안 2
 
2.9%
Other values (20) 23
32.9%

방류하천명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
낙동강
7 
현창천
3 
토평천
2 
초곡천
2 
동정천
2 
Other values (7)
9 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique5 ?
Unique (%)20.0%

Sample

1st row낙동강
2nd row낙동강
3rd row토평천
4th row현창천
5th row문산천

Common Values

ValueCountFrequency (%)
낙동강 7
28.0%
현창천 3
12.0%
토평천 2
 
8.0%
초곡천 2
 
8.0%
동정천 2
 
8.0%
마천천 2
 
8.0%
온정천 2
 
8.0%
문산천 1
 
4.0%
창녕천 1
 
4.0%
계성천 1
 
4.0%
Other values (2) 2
 
8.0%

Length

2023-12-12T18:26:33.403622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙동강 7
28.0%
현창천 3
12.0%
토평천 2
 
8.0%
초곡천 2
 
8.0%
동정천 2
 
8.0%
마천천 2
 
8.0%
온정천 2
 
8.0%
문산천 1
 
4.0%
창녕천 1
 
4.0%
계성천 1
 
4.0%
Other values (2) 2
 
8.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
창녕군
25 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창녕군 25
100.0%

Length

2023-12-12T18:26:33.547890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:33.719615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창녕군 25
100.0%

관리부서
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
건설교통과
18 
수도과(창녕군시설관리공단)
3 
안전치수과
3 
우포생태따오기과
 
1

Length

Max length14
Median length5
Mean length6.2
Min length5

Unique

Unique1 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
건설교통과 18
72.0%
수도과(창녕군시설관리공단) 3
 
12.0%
안전치수과 3
 
12.0%
우포생태따오기과 1
 
4.0%

Length

2023-12-12T18:26:33.817863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:33.927653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설교통과 18
72.0%
수도과(창녕군시설관리공단 3
 
12.0%
안전치수과 3
 
12.0%
우포생태따오기과 1
 
4.0%

운영기간
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
04-01~10-31
11 
01-01~12-31
10 
04-10~10-31
2 
03-02~11-30
2 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04-01~10-31
2nd row04-01~10-31
3rd row01-01~12-31
4th row04-01~10-31
5th row01-01~12-31

Common Values

ValueCountFrequency (%)
04-01~10-31 11
44.0%
01-01~12-31 10
40.0%
04-10~10-31 2
 
8.0%
03-02~11-30 2
 
8.0%

Length

2023-12-12T18:26:34.055711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:34.178223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
04-01~10-31 11
44.0%
01-01~12-31 10
40.0%
04-10~10-31 2
 
8.0%
03-02~11-30 2
 
8.0%

Interactions

2023-12-12T18:26:28.206052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:26:34.290123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배수장명칭배수장위치준공연도펌프마력(HP)펌프대수처리능력(세제곱미터,분)유수지용량(세제곱미터)수문(기)수혜지역방류하천명관리부서운영기간
배수장명칭1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000
배수장위치1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000
준공연도1.0001.0001.0000.5730.5790.9800.0000.4590.0000.5880.3460.634
펌프마력(HP)1.0001.0000.5731.0000.6471.0000.0000.5730.8590.0000.000NaN
펌프대수1.0001.0000.5790.6471.0000.9250.0000.8860.0000.0000.0000.308
처리능력(세제곱미터,분)1.0001.0000.9801.0000.9251.0000.0000.9670.3760.8050.8100.848
유수지용량(세제곱미터)0.0000.0000.0000.0000.0000.0001.0000.000NaNNaNNaNNaN
수문(기)1.0001.0000.4590.5730.8860.9670.0001.0000.4990.0000.0000.594
수혜지역1.0001.0000.0000.8590.0000.376NaN0.4991.0000.9030.0000.942
방류하천명1.0001.0000.5880.0000.0000.805NaN0.0000.9031.0000.0000.819
관리부서1.0001.0000.3460.0000.0000.810NaN0.0000.0000.0001.0000.542
운영기간1.0001.0000.634NaN0.3080.848NaN0.5940.9420.8190.5421.000
2023-12-12T18:26:34.483372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보강연도유수지용량(세제곱미터)방류하천명펌프대수운영기간수문(기)관리부서
보강연도1.000NaNNaNNaNNaNNaNNaN
유수지용량(세제곱미터)NaN1.0001.0001.0001.0001.0001.000
방류하천명NaN1.0001.0000.0000.3830.0000.000
펌프대수NaN1.0000.0001.0000.2350.7960.000
운영기간NaN1.0000.3830.2351.0000.3950.224
수문(기)NaN1.0000.0000.7960.3951.0000.000
관리부서NaN1.0000.0000.0000.2240.0001.000
2023-12-12T18:26:34.656494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공연도보강연도펌프대수유수지용량(세제곱미터)수문(기)방류하천명관리부서운영기간
준공연도1.000NaN0.5521.0000.3530.2700.0810.416
보강연도NaN1.000NaNNaNNaNNaNNaNNaN
펌프대수0.552NaN1.0001.0000.7960.0000.0000.235
유수지용량(세제곱미터)1.000NaN1.0001.0001.0001.0001.0001.000
수문(기)0.353NaN0.7961.0001.0000.0000.0000.395
방류하천명0.270NaN0.0001.0000.0001.0000.0000.383
관리부서0.081NaN0.0001.0000.0000.0001.0000.224
운영기간0.416NaN0.2351.0000.3950.3830.2241.000

Missing values

2023-12-12T18:26:28.395591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:26:28.610470image/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

배수장명칭배수장위치준공연도보강연도펌프마력(HP)펌프대수처리능력(세제곱미터,분)유수지용량(세제곱미터)수문(기)수혜지역방류하천명관리기관명관리부서운영기간
0창아지 배수장창녕군 남지읍 아지리 940-12004<NA><NA>2150<NA>2아지리낙동강창녕군건설교통과04-01~10-31
1영아지 배수장창녕군 남지읍 신전리 938-32005<NA><NA>2120<NA>2신전리낙동강창녕군건설교통과04-01~10-31
2양정 배수장창녕군 이방면 거남리 483-11984<NA><NA>145<NA>1거남리토평천창녕군건설교통과01-01~12-31
3상리 배수장창녕군 이방면 상리 435-12003<NA><NA>2150<NA>2상리현창천창녕군건설교통과04-01~10-31
4우만 배수장창녕군 이방면 안리 1263-52003<NA><NA>270<NA>2안리문산천창녕군건설교통과01-01~12-31
5모곡 배수장창녕군 이방면 모곡리 6901988<NA><NA>2222<NA>2모곡리초곡천창녕군건설교통과01-01~12-31
6등림 배수장창녕군 이방면 등림리 3392004<NA><NA>2160<NA>2등림리낙동강창녕군건설교통과04-01~10-31
7성산 배수장창녕군 이방면 성산리 5-32004<NA><NA>289<NA>6성산리현창천창녕군건설교통과04-01~10-31
8신기 배수장창녕군 이방 초곡 1347-12006<NA><NA>290<NA>2초곡리초곡천창녕군건설교통과04-01~10-31
9세진1 배수장창녕군 유어 세진 420-62004<NA><NA>2120<NA>2세진리토평천창녕군우포생태따오기과04-01~10-31
배수장명칭배수장위치준공연도보강연도펌프마력(HP)펌프대수처리능력(세제곱미터,분)유수지용량(세제곱미터)수문(기)수혜지역방류하천명관리기관명관리부서운영기간
15비봉2 배수장창녕군 부곡 수다 173-42012<NA><NA>2160<NA>2수다리수다천창녕군건설교통과03-02~11-30
16청암 배수장창녕군 부곡 청암 1052-182012<NA><NA>4528<NA>4청암리온정천창녕군건설교통과04-10~10-31
17노리 배수장창녕군 부곡 노리 603-22000<NA><NA>2127<NA>2노리낙동강창녕군건설교통과04-01~10-31
18비봉1 배수장창녕군 부곡면 비봉리 169-12006<NA><NA>2160<NA>2비봉리온정천창녕군건설교통과03-02~11-30
19남지 배수장창녕군 남지 남지 7-32001<NA>200319530920.03남지리낙동강창녕군수도과(창녕군시설관리공단)01-01~12-31
20상남 배수장창녕군 남지 남지 833-72007201740(308)2(2)20(180)181.92(2)남지리낙동강창녕군수도과(창녕군시설관리공단)01-01~12-31
21어릿골배수장창녕군 도천면 도천리 736-82013<NA>30224<NA>1도천리영산천창녕군수도과(창녕군시설관리공단)01-01~12-31
22신덕 배수장창녕군 길곡 마천리 7432008<NA>40236<NA>2마천리마천천창녕군안전치수과01-01~12-31
23대성 배수장창녕군 남지 성사리 95-32013<NA>60223<NA>2대성리낙동강창녕군안전치수과01-01~12-31
24부곡 배수장창녕군 이방 안리 1129-92014<NA>30333<NA>3안리현창천창녕군안전치수과01-01~12-31