Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells5
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory54.4 B

Variable types

Text2
Numeric2
Categorical1
DateTime1

Dataset

Description대전광역시내에 있는 먹는물공동시설 현황 정보에 대한 데이터로 (명칭, 소재지, 이용인원, 수원, 개발연도) 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15046185/fileData.do

Alerts

작성기준일 has constant value ""Constant
명칭 has 1 (3.3%) missing valuesMissing
소재지 has 1 (3.3%) missing valuesMissing
이용인원 has 1 (3.3%) missing valuesMissing
개발년도 has 1 (3.3%) missing valuesMissing
작성기준일 has 1 (3.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:14:08.058249
Analysis finished2023-12-12 15:14:09.009191
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

명칭
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Memory size372.0 B
2023-12-13T00:14:09.163764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.7241379
Min length2

Characters and Unicode

Total characters79
Distinct characters52
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

Unique29 ?
Unique (%)100.0%

Sample

1st row지푸재
2nd row옻샘
3rd row용천
4th row용수골1
5th row용수골2
ValueCountFrequency (%)
지푸재 1
 
3.4%
까치재 1
 
3.4%
우족산 1
 
3.4%
남도 1
 
3.4%
비래사 1
 
3.4%
밭탕골 1
 
3.4%
쌍암 1
 
3.4%
오량 1
 
3.4%
한천 1
 
3.4%
빼울 1
 
3.4%
Other values (19) 19
65.5%
2023-12-13T00:14:09.532697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 5
 
6.3%
천 5
 
6.3%
보 4
 
5.1%
수 4
 
5.1%
골 4
 
5.1%
암 3
 
3.8%
용 3
 
3.8%
정 2
 
2.5%
남 2
 
2.5%
문 2
 
2.5%
Other values (42) 45
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
97.5%
Decimal Number 2
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 5
 
6.5%
천 5
 
6.5%
보 4
 
5.2%
수 4
 
5.2%
골 4
 
5.2%
암 3
 
3.9%
용 3
 
3.9%
정 2
 
2.6%
남 2
 
2.6%
문 2
 
2.6%
Other values (40) 43
55.8%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
97.5%
Common 2
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 5
 
6.5%
천 5
 
6.5%
보 4
 
5.2%
수 4
 
5.2%
골 4
 
5.2%
암 3
 
3.9%
용 3
 
3.9%
정 2
 
2.6%
남 2
 
2.6%
문 2
 
2.6%
Other values (40) 43
55.8%
Common
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
97.5%
ASCII 2
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 5
 
6.5%
천 5
 
6.5%
보 4
 
5.2%
수 4
 
5.2%
골 4
 
5.2%
암 3
 
3.9%
용 3
 
3.9%
정 2
 
2.6%
남 2
 
2.6%
문 2
 
2.6%
Other values (40) 43
55.8%
ASCII
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

소재지
Text

MISSING 

Distinct26
Distinct (%)89.7%
Missing1
Missing (%)3.3%
Memory size372.0 B
2023-12-13T00:14:09.746509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length22.517241
Min length15

Characters and Unicode

Total characters653
Distinct characters92
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)82.8%

Sample

1st row대전광역시 동구 대별동 산10 (이사동가는길목)
2nd row대전광역시 동구 판암동 38
3rd row대전광역시 동구 용운동 299-1
4th row대전광역시 동구 용운동 74-6 (우송대공사터널쪽)
5th row대전광역시 동구 용운동 74-6 (우송대공사터널쪽)
ValueCountFrequency (%)
대전광역시 29
22.0%
중구 11
 
8.3%
동구 7
 
5.3%
대사동 5
 
3.8%
서구 5
 
3.8%
대덕구 5
 
3.8%
비래동 4
 
3.0%
산3-71 4
 
3.0%
용운동 3
 
2.3%
8-4 2
 
1.5%
Other values (54) 57
43.2%
2023-12-13T00:14:10.398948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
103
 
15.8%
대 42
 
6.4%
동 36
 
5.5%
구 29
 
4.4%
전 29
 
4.4%
시 29
 
4.4%
역 29
 
4.4%
광 29
 
4.4%
1 25
 
3.8%
- 25
 
3.8%
Other values (82) 277
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 403
61.7%
Space Separator 103
 
15.8%
Decimal Number 94
 
14.4%
Dash Punctuation 25
 
3.8%
Open Punctuation 13
 
2.0%
Close Punctuation 13
 
2.0%
Other Punctuation 1
 
0.2%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 42
 
10.4%
동 36
 
8.9%
구 29
 
7.2%
전 29
 
7.2%
시 29
 
7.2%
역 29
 
7.2%
광 29
 
7.2%
사 18
 
4.5%
산 17
 
4.2%
중 11
 
2.7%
Other values (66) 134
33.3%
Decimal Number
ValueCountFrequency (%)
1 25
26.6%
3 13
13.8%
7 11
11.7%
8 10
 
10.6%
4 10
 
10.6%
2 7
 
7.4%
6 7
 
7.4%
5 6
 
6.4%
9 4
 
4.3%
0 1
 
1.1%
Space Separator
ValueCountFrequency (%)
103
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 403
61.7%
Common 249
38.1%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 42
 
10.4%
동 36
 
8.9%
구 29
 
7.2%
전 29
 
7.2%
시 29
 
7.2%
역 29
 
7.2%
광 29
 
7.2%
사 18
 
4.5%
산 17
 
4.2%
중 11
 
2.7%
Other values (66) 134
33.3%
Common
ValueCountFrequency (%)
103
41.4%
1 25
 
10.0%
- 25
 
10.0%
( 13
 
5.2%
) 13
 
5.2%
3 13
 
5.2%
7 11
 
4.4%
8 10
 
4.0%
4 10
 
4.0%
2 7
 
2.8%
Other values (5) 19
 
7.6%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 403
61.7%
ASCII 250
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
103
41.2%
1 25
 
10.0%
- 25
 
10.0%
( 13
 
5.2%
) 13
 
5.2%
3 13
 
5.2%
7 11
 
4.4%
8 10
 
4.0%
4 10
 
4.0%
2 7
 
2.8%
Other values (6) 20
 
8.0%
Hangul
ValueCountFrequency (%)
대 42
 
10.4%
동 36
 
8.9%
구 29
 
7.2%
전 29
 
7.2%
시 29
 
7.2%
역 29
 
7.2%
광 29
 
7.2%
사 18
 
4.5%
산 17
 
4.2%
중 11
 
2.7%
Other values (66) 134
33.3%

이용인원
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)24.1%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean150.34483
Minimum50
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:14:10.578516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q1100
median150
Q3200
95-th percentile300
Maximum300
Range250
Interquartile range (IQR)100

Descriptive statistics

Standard deviation81.961784
Coefficient of variation (CV)0.54515865
Kurtosis-1.0120503
Mean150.34483
Median Absolute Deviation (MAD)50
Skewness0.31883612
Sum4360
Variance6717.734
MonotonicityNot monotonic
2023-12-13T00:14:10.677886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
200 10
33.3%
100 7
23.3%
50 6
20.0%
300 3
 
10.0%
60 1
 
3.3%
250 1
 
3.3%
150 1
 
3.3%
(Missing) 1
 
3.3%
ValueCountFrequency (%)
50 6
20.0%
60 1
 
3.3%
100 7
23.3%
150 1
 
3.3%
200 10
33.3%
250 1
 
3.3%
300 3
 
10.0%
ValueCountFrequency (%)
300 3
 
10.0%
250 1
 
3.3%
200 10
33.3%
150 1
 
3.3%
100 7
23.3%
60 1
 
3.3%
50 6
20.0%

수 원
Categorical

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
계곡수
13 
석간수
11 
지하수
4 
용천수
 
1
<NA>
 
1

Length

Max length4
Median length3
Mean length3.0333333
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row석간수
2nd row석간수
3rd row지하수
4th row지하수
5th row석간수

Common Values

ValueCountFrequency (%)
계곡수 13
43.3%
석간수 11
36.7%
지하수 4
 
13.3%
용천수 1
 
3.3%
<NA> 1
 
3.3%

Length

2023-12-13T00:14:10.791247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:14:10.900903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계곡수 13
43.3%
석간수 11
36.7%
지하수 4
 
13.3%
용천수 1
 
3.3%
na 1
 
3.3%

개발년도
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)55.2%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean1986.931
Minimum1950
Maximum2006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:14:11.009101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1966.6
Q11986
median1988
Q31990
95-th percentile2003
Maximum2006
Range56
Interquartile range (IQR)4

Descriptive statistics

Standard deviation11.003022
Coefficient of variation (CV)0.0055376972
Kurtosis4.5600931
Mean1986.931
Median Absolute Deviation (MAD)2
Skewness-1.5689112
Sum57621
Variance121.0665
MonotonicityNot monotonic
2023-12-13T00:14:11.142155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1988 5
16.7%
1990 5
16.7%
1989 3
10.0%
1987 2
 
6.7%
1985 2
 
6.7%
1992 2
 
6.7%
1996 1
 
3.3%
1978 1
 
3.3%
2006 1
 
3.3%
1975 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
1950 1
 
3.3%
1961 1
 
3.3%
1975 1
 
3.3%
1978 1
 
3.3%
1979 1
 
3.3%
1985 2
 
6.7%
1986 1
 
3.3%
1987 2
 
6.7%
1988 5
16.7%
1989 3
10.0%
ValueCountFrequency (%)
2006 1
 
3.3%
2005 1
 
3.3%
2000 1
 
3.3%
1996 1
 
3.3%
1992 2
 
6.7%
1990 5
16.7%
1989 3
10.0%
1988 5
16.7%
1987 2
 
6.7%
1986 1
 
3.3%

작성기준일
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)3.4%
Missing1
Missing (%)3.3%
Memory size372.0 B
Minimum2023-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-12-13T00:14:11.249339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:11.350980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T00:14:08.515603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:08.319811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:08.610307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:14:08.407482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:14:11.417801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
명칭소재지이용인원수 원개발년도
명칭1.0001.0001.0001.0001.000
소재지1.0001.0000.7090.8010.000
이용인원1.0000.7091.0000.0000.239
수 원1.0000.8010.0001.0000.291
개발년도1.0000.0000.2390.2911.000
2023-12-13T00:14:11.531832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이용인원개발년도수 원
이용인원1.000-0.1760.000
개발년도-0.1761.0000.102
수 원0.0000.1021.000

Missing values

2023-12-13T00:14:08.714036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:14:08.818600image/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.
2023-12-13T00:14:08.935805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

명칭소재지이용인원수 원개발년도작성기준일
0지푸재대전광역시 동구 대별동 산10 (이사동가는길목)60석간수19962023-01-01
1옻샘대전광역시 동구 판암동 38200석간수19902023-01-01
2용천대전광역시 동구 용운동 299-1300지하수19872023-01-01
3용수골1대전광역시 동구 용운동 74-6 (우송대공사터널쪽)50지하수19862023-01-01
4용수골2대전광역시 동구 용운동 74-6 (우송대공사터널쪽)250석간수20052023-01-01
5옥정사대전광역시 동구 가양동 산3-1 (가양,우암사적공원)200석간수19882023-01-01
6모암대전광역시 동구 이사동 산7-1 옥계초등학교뒤200석간수19882023-01-01
7고촉사대전광역시 중구 보문산공원로 252-57100석간수19872023-01-01
8명수천대전광역시 중구 부사동 산2-13100석간수19882023-01-01
9수정암대전광역시 중구 대사동 산3-71200석간수19612023-01-01
명칭소재지이용인원수 원개발년도작성기준일
20빼울대전광역시 서구 가수원동 618-4 (은아A뒤)50석간수19852023-01-01
21한천대전광역시 서구 관저동 838-1(산68-1)50용천수20062023-01-01
22오량대전광역시 서구 복수동 336-31 (복수초뒤)100지하수19922023-01-01
23쌍암대전광역시 유성구 교촌동 568-150계곡수19902023-01-01
24밭탕골대전광역시 대덕구 비래동 산34-19200계곡수19892023-01-01
25비래사대전광역시 대덕구 비래동 산1-15 (비래사위)150계곡수19902023-01-01
26남도대전광역시 대덕구 비래동 8-4 (비래사내려와옆길)100계곡수19892023-01-01
27우족산대전광역시 대덕구 비래동 8-4100계곡수19892023-01-01
28계족산대전광역시 대덕구 법동 산17 (선비1단지쪽)100계곡수19902023-01-01
29<NA><NA><NA><NA><NA><NA>