Overview

Dataset statistics

Number of variables6
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory53.1 B

Variable types

Categorical2
Text3
Numeric1

Dataset

Description광주광역시에서 관리 및 운영중인 관내 구별 배수펌프장 현황입니다.(서구 2개소, 남구 4개소, 광산 25개소, 광주환경공단 1개소)배수펌프장명, 위치, 준공년도, 수문, 펌프대수, 양수량, 관리주체 등의 항목을 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15001834/fileData.do

Alerts

구분 is highly overall correlated with 관리주체High correlation
관리주체 is highly overall correlated with 구분High correlation
배수펌프장 has unique valuesUnique
위치 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:23:06.350196
Analysis finished2023-12-12 14:23:06.943683
Duration0.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
광주광역시 광산구
26 
광주광역시 남구
4 
광주광역시 서구
 
2

Length

Max length9
Median length9
Mean length8.8125
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주광역시 서구
2nd row광주광역시 서구
3rd row광주광역시 남구
4th row광주광역시 남구
5th row광주광역시 남구

Common Values

ValueCountFrequency (%)
광주광역시 광산구 26
81.2%
광주광역시 남구 4
 
12.5%
광주광역시 서구 2
 
6.2%

Length

2023-12-12T23:23:07.003406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:23:07.109822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주광역시 32
50.0%
광산구 26
40.6%
남구 4
 
6.2%
서구 2
 
3.1%

배수펌프장
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:23:07.294019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1875
Min length5

Characters and Unicode

Total characters198
Distinct characters54
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

Unique32 ?
Unique (%)100.0%

Sample

1st row상무4지구 빗물펌프장
2nd row용두양배수장
3rd row양촌 배수펌프장
4th row화장 배수펌프장
5th row오산.신장1 배수장
ValueCountFrequency (%)
배수펌프장 2
 
5.3%
배수장 2
 
5.3%
상무4지구 1
 
2.6%
신기마을배수장 1
 
2.6%
신촌배수장 1
 
2.6%
장암배수장 1
 
2.6%
매결마을배수장 1
 
2.6%
신창1배수장 1
 
2.6%
신가1배수장 1
 
2.6%
신가2배수장 1
 
2.6%
Other values (26) 26
68.4%
2023-12-12T23:23:07.664736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 37
18.7%
수 32
16.2%
배 31
15.7%
산 10
 
5.1%
신 8
 
4.0%
6
 
3.0%
1 5
 
2.5%
2 4
 
2.0%
송 3
 
1.5%
월 3
 
1.5%
Other values (44) 59
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 179
90.4%
Decimal Number 11
 
5.6%
Space Separator 6
 
3.0%
Other Punctuation 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 37
20.7%
수 32
17.9%
배 31
17.3%
산 10
 
5.6%
신 8
 
4.5%
송 3
 
1.7%
월 3
 
1.7%
프 3
 
1.7%
펌 3
 
1.7%
가 2
 
1.1%
Other values (38) 47
26.3%
Decimal Number
ValueCountFrequency (%)
1 5
45.5%
2 4
36.4%
3 1
 
9.1%
4 1
 
9.1%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 179
90.4%
Common 19
 
9.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 37
20.7%
수 32
17.9%
배 31
17.3%
산 10
 
5.6%
신 8
 
4.5%
송 3
 
1.7%
월 3
 
1.7%
프 3
 
1.7%
펌 3
 
1.7%
가 2
 
1.1%
Other values (38) 47
26.3%
Common
ValueCountFrequency (%)
6
31.6%
1 5
26.3%
2 4
21.1%
. 2
 
10.5%
3 1
 
5.3%
4 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 179
90.4%
ASCII 19
 
9.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 37
20.7%
수 32
17.9%
배 31
17.3%
산 10
 
5.6%
신 8
 
4.5%
송 3
 
1.7%
월 3
 
1.7%
프 3
 
1.7%
펌 3
 
1.7%
가 2
 
1.1%
Other values (38) 47
26.3%
ASCII
ValueCountFrequency (%)
6
31.6%
1 5
26.3%
2 4
21.1%
. 2
 
10.5%
3 1
 
5.3%
4 1
 
5.3%

위치
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:23:07.924722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length19
Min length15

Characters and Unicode

Total characters608
Distinct characters69
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

Unique32 ?
Unique (%)100.0%

Sample

1st row광주광역시 서구 상무버들로 54번길 34
2nd row광주광역시 서구 용두동 730-12외 1필지
3rd row전남 나주시 금천면 신가리1215-12
4th row광주광역시 남구 화장동 1006-12
5th row전남 나주시 남평읍 평산리 922번지
ValueCountFrequency (%)
광주광역시 29
23.2%
광산구 25
20.0%
신창동 5
 
4.0%
전남 3
 
2.4%
나주시 3
 
2.4%
서구 2
 
1.6%
신가동 2
 
1.6%
남평읍 2
 
1.6%
평산리 2
 
1.6%
평동로 1
 
0.8%
Other values (51) 51
40.8%
2023-12-12T23:23:08.406135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93
15.3%
광 83
13.7%
산 33
 
5.4%
시 32
 
5.3%
주 32
 
5.3%
1 32
 
5.3%
역 29
 
4.8%
구 28
 
4.6%
- 21
 
3.5%
동 20
 
3.3%
Other values (59) 205
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 364
59.9%
Decimal Number 130
 
21.4%
Space Separator 93
 
15.3%
Dash Punctuation 21
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 83
22.8%
산 33
 
9.1%
시 32
 
8.8%
주 32
 
8.8%
역 29
 
8.0%
구 28
 
7.7%
동 20
 
5.5%
신 9
 
2.5%
길 9
 
2.5%
남 7
 
1.9%
Other values (47) 82
22.5%
Decimal Number
ValueCountFrequency (%)
1 32
24.6%
2 18
13.8%
5 14
10.8%
0 14
10.8%
3 11
 
8.5%
9 10
 
7.7%
6 9
 
6.9%
7 8
 
6.2%
4 8
 
6.2%
8 6
 
4.6%
Space Separator
ValueCountFrequency (%)
93
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 364
59.9%
Common 244
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 83
22.8%
산 33
 
9.1%
시 32
 
8.8%
주 32
 
8.8%
역 29
 
8.0%
구 28
 
7.7%
동 20
 
5.5%
신 9
 
2.5%
길 9
 
2.5%
남 7
 
1.9%
Other values (47) 82
22.5%
Common
ValueCountFrequency (%)
93
38.1%
1 32
 
13.1%
- 21
 
8.6%
2 18
 
7.4%
5 14
 
5.7%
0 14
 
5.7%
3 11
 
4.5%
9 10
 
4.1%
6 9
 
3.7%
7 8
 
3.3%
Other values (2) 14
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 364
59.9%
ASCII 244
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
93
38.1%
1 32
 
13.1%
- 21
 
8.6%
2 18
 
7.4%
5 14
 
5.7%
0 14
 
5.7%
3 11
 
4.5%
9 10
 
4.1%
6 9
 
3.7%
7 8
 
3.3%
Other values (2) 14
 
5.7%
Hangul
ValueCountFrequency (%)
광 83
22.8%
산 33
 
9.1%
시 32
 
8.8%
주 32
 
8.8%
역 29
 
8.0%
구 28
 
7.7%
동 20
 
5.5%
신 9
 
2.5%
길 9
 
2.5%
남 7
 
1.9%
Other values (47) 82
22.5%

준공년도(보강년도)
Text

Distinct20
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-12T23:23:08.625814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.75
Min length4

Characters and Unicode

Total characters152
Distinct characters11
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

Unique12 ?
Unique (%)37.5%

Sample

1st row2000
2nd row2012
3rd row2019
4th row1996
5th row2010
ValueCountFrequency (%)
2000 4
 
12.5%
2010 4
 
12.5%
2008 2
 
6.2%
2019 2
 
6.2%
2015 2
 
6.2%
2009 2
 
6.2%
2005 2
 
6.2%
2012 2
 
6.2%
2005(2021 1
 
3.1%
2016(2018 1
 
3.1%
Other values (10) 10
31.2%
2023-12-12T23:23:08.990314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 55
36.2%
2 39
25.7%
1 23
15.1%
9 9
 
5.9%
8 5
 
3.3%
5 5
 
3.3%
6 4
 
2.6%
( 4
 
2.6%
) 4
 
2.6%
3 3
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144
94.7%
Open Punctuation 4
 
2.6%
Close Punctuation 4
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55
38.2%
2 39
27.1%
1 23
16.0%
9 9
 
6.2%
8 5
 
3.5%
5 5
 
3.5%
6 4
 
2.8%
3 3
 
2.1%
4 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55
36.2%
2 39
25.7%
1 23
15.1%
9 9
 
5.9%
8 5
 
3.3%
5 5
 
3.3%
6 4
 
2.6%
( 4
 
2.6%
) 4
 
2.6%
3 3
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55
36.2%
2 39
25.7%
1 23
15.1%
9 9
 
5.9%
8 5
 
3.3%
5 5
 
3.3%
6 4
 
2.6%
( 4
 
2.6%
) 4
 
2.6%
3 3
 
2.0%

펌프대수(대)
Real number (ℝ)

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.65625
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T23:23:09.144237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2231825
Coefficient of variation (CV)0.83696281
Kurtosis15.278976
Mean2.65625
Median Absolute Deviation (MAD)1
Skewness3.4893651
Sum85
Variance4.9425403
MonotonicityNot monotonic
2023-12-12T23:23:09.267678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 15
46.9%
1 7
21.9%
3 4
 
12.5%
5 3
 
9.4%
4 2
 
6.2%
13 1
 
3.1%
ValueCountFrequency (%)
1 7
21.9%
2 15
46.9%
3 4
 
12.5%
4 2
 
6.2%
5 3
 
9.4%
13 1
 
3.1%
ValueCountFrequency (%)
13 1
 
3.1%
5 3
 
9.4%
4 2
 
6.2%
3 4
 
12.5%
2 15
46.9%
1 7
21.9%

관리주체
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
광산구청
25 
남구청
4 
서구청
 
2
광주환경공단
 
1

Length

Max length6
Median length4
Mean length3.875
Min length3

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row서구청
2nd row서구청
3rd row남구청
4th row남구청
5th row남구청

Common Values

ValueCountFrequency (%)
광산구청 25
78.1%
남구청 4
 
12.5%
서구청 2
 
6.2%
광주환경공단 1
 
3.1%

Length

2023-12-12T23:23:09.424478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:23:09.568395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광산구청 25
78.1%
남구청 4
 
12.5%
서구청 2
 
6.2%
광주환경공단 1
 
3.1%

Interactions

2023-12-12T23:23:06.630867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:23:09.692554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분배수펌프장위치준공년도(보강년도)펌프대수(대)관리주체
구분1.0001.0001.0000.0000.0001.000
배수펌프장1.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.000
준공년도(보강년도)0.0001.0001.0001.0000.8760.000
펌프대수(대)0.0001.0001.0000.8761.0000.205
관리주체1.0001.0001.0000.0000.2051.000
2023-12-12T23:23:09.827045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분관리주체
구분1.0000.983
관리주체0.9831.000
2023-12-12T23:23:10.266035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
펌프대수(대)구분관리주체
펌프대수(대)1.0000.0000.152
구분0.0001.0000.983
관리주체0.1520.9831.000

Missing values

2023-12-12T23:23:06.806008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:23:06.906028image/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

구분배수펌프장위치준공년도(보강년도)펌프대수(대)관리주체
0광주광역시 서구상무4지구 빗물펌프장광주광역시 서구 상무버들로 54번길 3420002서구청
1광주광역시 서구용두양배수장광주광역시 서구 용두동 730-12외 1필지20122서구청
2광주광역시 남구양촌 배수펌프장전남 나주시 금천면 신가리1215-1220194남구청
3광주광역시 남구화장 배수펌프장광주광역시 남구 화장동 1006-1219963남구청
4광주광역시 남구오산.신장1 배수장전남 나주시 남평읍 평산리 922번지20102남구청
5광주광역시 남구오산.신장 2 배수장전남 나주시 남평읍 평산리 839번지20102남구청
6광주광역시 광산구동곡배수장광주광역시 광산구 마곡길391991(2023)13광산구청
7광주광역시 광산구용봉배수장광주광역시 광산구 용봉길9220093광산구청
8광주광역시 광산구송대1배수장광주광역시 광산구 극락둑길3491982(2003)5광산구청
9광주광역시 광산구우산배수장광주광역시 광산구 우산천변길26120002광산구청
구분배수펌프장위치준공년도(보강년도)펌프대수(대)관리주체
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