Overview

Dataset statistics

Number of variables6
Number of observations51
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory52.6 B

Variable types

Numeric2
Categorical1
Text3

Dataset

Description경상남도 내 정수시설 현황으로, 정수시설 시군명, 정수시설명, 정수장 시설용량(㎥/일)에 대한 정보를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3034244

Alerts

연번 is highly overall correlated with 급수인구(천명) and 1 other fieldsHigh correlation
급수인구(천명) is highly overall correlated with 연번High correlation
시군명 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
정수장명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:18:31.523845
Analysis finished2023-12-11 00:18:32.460351
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-11T09:18:32.550025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q113.5
median26
Q338.5
95-th percentile48.5
Maximum51
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.866069
Coefficient of variation (CV)0.57177187
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)13
Skewness0
Sum1326
Variance221
MonotonicityStrictly increasing
2023-12-11T09:18:32.732643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
2 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
51 1
2.0%
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%

시군명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size540.0 B
남해군
9 
합천군
5 
창원시
5 
산청군
4 
거창군
4 
Other values (12)
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique4 ?
Unique (%)7.8%

Sample

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

Common Values

ValueCountFrequency (%)
남해군 9
17.6%
합천군 5
9.8%
창원시 5
9.8%
산청군 4
 
7.8%
거창군 4
 
7.8%
의령군 4
 
7.8%
양산시 3
 
5.9%
함안군 3
 
5.9%
하동군 2
 
3.9%
함양군 2
 
3.9%
Other values (7) 10
19.6%

Length

2023-12-11T09:18:32.888644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남해군 9
17.6%
창원시 5
9.8%
합천군 5
9.8%
산청군 4
 
7.8%
거창군 4
 
7.8%
의령군 4
 
7.8%
양산시 3
 
5.9%
함안군 3
 
5.9%
창녕군 2
 
3.9%
진주시 2
 
3.9%
Other values (7) 10
19.6%

정수장명
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-11T09:18:33.115139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.4313725
Min length2

Characters and Unicode

Total characters124
Distinct characters73
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

Unique51 ?
Unique (%)100.0%

Sample

1st row대산(1만)
2nd row대산(12만)
3rd row북면
4th row창원칠서
5th row석동
ValueCountFrequency (%)
창원칠서 2
 
3.9%
대산(1만 1
 
2.0%
난음 1
 
2.0%
지족 1
 
2.0%
남면 1
 
2.0%
대곡 1
 
2.0%
창선 1
 
2.0%
항도 1
 
2.0%
두곡 1
 
2.0%
청룡 1
 
2.0%
Other values (40) 40
78.4%
2023-12-11T09:18:33.469951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 4
 
3.2%
동 4
 
3.2%
창 4
 
3.2%
서 4
 
3.2%
대 3
 
2.4%
칠 3
 
2.4%
천 3
 
2.4%
안 3
 
2.4%
함 3
 
2.4%
곡 3
 
2.4%
Other values (63) 90
72.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
91.1%
Decimal Number 5
 
4.0%
Open Punctuation 3
 
2.4%
Close Punctuation 3
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 4
 
3.5%
동 4
 
3.5%
창 4
 
3.5%
서 4
 
3.5%
대 3
 
2.7%
칠 3
 
2.7%
천 3
 
2.7%
안 3
 
2.7%
함 3
 
2.7%
곡 3
 
2.7%
Other values (59) 79
69.9%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
2 2
40.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
91.1%
Common 11
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 4
 
3.5%
동 4
 
3.5%
창 4
 
3.5%
서 4
 
3.5%
대 3
 
2.7%
칠 3
 
2.7%
천 3
 
2.7%
안 3
 
2.7%
함 3
 
2.7%
곡 3
 
2.7%
Other values (59) 79
69.9%
Common
ValueCountFrequency (%)
( 3
27.3%
1 3
27.3%
) 3
27.3%
2 2
18.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
91.1%
ASCII 11
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 4
 
3.5%
동 4
 
3.5%
창 4
 
3.5%
서 4
 
3.5%
대 3
 
2.7%
칠 3
 
2.7%
천 3
 
2.7%
안 3
 
2.7%
함 3
 
2.7%
곡 3
 
2.7%
Other values (59) 79
69.9%
ASCII
ValueCountFrequency (%)
( 3
27.3%
1 3
27.3%
) 3
27.3%
2 2
18.2%

정수장 시설용량(세제곱미터/일)
Text

Distinct29
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-11T09:18:34.059932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.2156863
Min length2

Characters and Unicode

Total characters215
Distinct characters12
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

Unique19 ?
Unique (%)37.3%

Sample

1st row10000
2nd row120000
3rd row10000
4th row400000
5th row100000
ValueCountFrequency (%)
800 5
 
9.8%
2000 5
 
9.8%
1000 5
 
9.8%
10000 3
 
5.9%
4000 3
 
5.9%
3000 3
 
5.9%
900 2
 
3.9%
2500 2
 
3.9%
700 2
 
3.9%
20000 2
 
3.9%
Other values (19) 19
37.3%
2023-12-11T09:18:34.480237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 148
68.8%
1 16
 
7.4%
2 13
 
6.0%
5 8
 
3.7%
8 6
 
2.8%
4 6
 
2.8%
3 6
 
2.8%
6 5
 
2.3%
9 3
 
1.4%
7 2
 
0.9%
Other values (2) 2
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 213
99.1%
Other Letter 2
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 148
69.5%
1 16
 
7.5%
2 13
 
6.1%
5 8
 
3.8%
8 6
 
2.8%
4 6
 
2.8%
3 6
 
2.8%
6 5
 
2.3%
9 3
 
1.4%
7 2
 
0.9%
Other Letter
ValueCountFrequency (%)
없 1
50.0%
음 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 213
99.1%
Hangul 2
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 148
69.5%
1 16
 
7.5%
2 13
 
6.1%
5 8
 
3.8%
8 6
 
2.8%
4 6
 
2.8%
3 6
 
2.8%
6 5
 
2.3%
9 3
 
1.4%
7 2
 
0.9%
Hangul
ValueCountFrequency (%)
없 1
50.0%
음 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 213
99.1%
Hangul 2
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 148
69.5%
1 16
 
7.5%
2 13
 
6.1%
5 8
 
3.8%
8 6
 
2.8%
4 6
 
2.8%
3 6
 
2.8%
6 5
 
2.3%
9 3
 
1.4%
7 2
 
0.9%
Hangul
ValueCountFrequency (%)
없 1
50.0%
음 1
50.0%

급수구역
Text

Distinct50
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-11T09:18:34.698092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length7.6862745
Min length2

Characters and Unicode

Total characters392
Distinct characters99
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)96.1%

Sample

1st row동읍, 대산면
2nd row의창구, 성산구
3rd row북면
4th row합포구, 회원구, 의창구, 성산구, 함안군
5th row진해구 전역(용원동,웅동 일부 제외)
ValueCountFrequency (%)
외 6
 
6.5%
동 3
 
3.2%
삼성동 2
 
2.2%
의창구 2
 
2.2%
강서동 2
 
2.2%
의령읍 2
 
2.2%
3개 2
 
2.2%
일부 2
 
2.2%
함안군 2
 
2.2%
성산구 2
 
2.2%
Other values (65) 68
73.1%
2023-12-11T09:18:35.043559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 58
 
14.8%
44
 
11.2%
, 34
 
8.7%
동 20
 
5.1%
읍 13
 
3.3%
구 8
 
2.0%
외 8
 
2.0%
서 7
 
1.8%
지 7
 
1.8%
개 7
 
1.8%
Other values (89) 186
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 302
77.0%
Space Separator 44
 
11.2%
Other Punctuation 34
 
8.7%
Decimal Number 8
 
2.0%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 58
 
19.2%
동 20
 
6.6%
읍 13
 
4.3%
구 8
 
2.6%
외 8
 
2.6%
서 7
 
2.3%
지 7
 
2.3%
개 7
 
2.3%
창 6
 
2.0%
성 6
 
2.0%
Other values (80) 162
53.6%
Decimal Number
ValueCountFrequency (%)
3 3
37.5%
4 2
25.0%
1 1
 
12.5%
2 1
 
12.5%
5 1
 
12.5%
Space Separator
ValueCountFrequency (%)
44
100.0%
Other Punctuation
ValueCountFrequency (%)
, 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 302
77.0%
Common 90
 
23.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 58
 
19.2%
동 20
 
6.6%
읍 13
 
4.3%
구 8
 
2.6%
외 8
 
2.6%
서 7
 
2.3%
지 7
 
2.3%
개 7
 
2.3%
창 6
 
2.0%
성 6
 
2.0%
Other values (80) 162
53.6%
Common
ValueCountFrequency (%)
44
48.9%
, 34
37.8%
3 3
 
3.3%
( 2
 
2.2%
4 2
 
2.2%
) 2
 
2.2%
1 1
 
1.1%
2 1
 
1.1%
5 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 302
77.0%
ASCII 90
 
23.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 58
 
19.2%
동 20
 
6.6%
읍 13
 
4.3%
구 8
 
2.6%
외 8
 
2.6%
서 7
 
2.3%
지 7
 
2.3%
개 7
 
2.3%
창 6
 
2.0%
성 6
 
2.0%
Other values (80) 162
53.6%
ASCII
ValueCountFrequency (%)
44
48.9%
, 34
37.8%
3 3
 
3.3%
( 2
 
2.2%
4 2
 
2.2%
) 2
 
2.2%
1 1
 
1.1%
2 1
 
1.1%
5 1
 
1.1%

급수인구(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.786275
Minimum0.4
Maximum623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-11T09:18:35.183858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.15
Q12.15
median5.1
Q323.7
95-th percentile226.15
Maximum623
Range622.6
Interquartile range (IQR)21.55

Descriptive statistics

Standard deviation107.4505
Coefficient of variation (CV)2.346784
Kurtosis16.991695
Mean45.786275
Median Absolute Deviation (MAD)3.7
Skewness3.8045961
Sum2335.1
Variance11545.609
MonotonicityNot monotonic
2023-12-11T09:18:35.313899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
23.2 2
 
3.9%
2.6 2
 
3.9%
1.7 2
 
3.9%
1.8 2
 
3.9%
3.4 2
 
3.9%
5.7 2
 
3.9%
20.1 1
 
2.0%
1.5 1
 
2.0%
4.1 1
 
2.0%
9.5 1
 
2.0%
Other values (35) 35
68.6%
ValueCountFrequency (%)
0.4 1
2.0%
0.8 1
2.0%
1.1 1
2.0%
1.2 1
2.0%
1.3 1
2.0%
1.5 1
2.0%
1.7 2
3.9%
1.8 2
3.9%
1.9 1
2.0%
2.0 1
2.0%
ValueCountFrequency (%)
623.0 1
2.0%
313.2 1
2.0%
238.9 1
2.0%
213.4 1
2.0%
206.0 1
2.0%
166.6 1
2.0%
92.3 1
2.0%
81.7 1
2.0%
51.5 1
2.0%
41.3 1
2.0%

Interactions

2023-12-11T09:18:32.075497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.875102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.164595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.971734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:18:35.415437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군명정수장명정수장 시설용량(세제곱미터/일)급수구역급수인구(천명)
연번1.0000.9731.0000.7431.0000.147
시군명0.9731.0001.0000.7671.0000.353
정수장명1.0001.0001.0001.0001.0001.000
정수장 시설용량(세제곱미터/일)0.7430.7671.0001.0000.8261.000
급수구역1.0001.0001.0000.8261.0001.000
급수인구(천명)0.1470.3531.0001.0001.0001.000
2023-12-11T09:18:35.528594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번급수인구(천명)시군명
연번1.000-0.5260.776
급수인구(천명)-0.5261.0000.128
시군명0.7760.1281.000

Missing values

2023-12-11T09:18:32.294836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:18:32.406166image/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

연번시군명정수장명정수장 시설용량(세제곱미터/일)급수구역급수인구(천명)
01창원시대산(1만)10000동읍, 대산면30.6
12창원시대산(12만)120000의창구, 성산구213.4
23창원시북면10000북면20.0
34창원시창원칠서400000합포구, 회원구, 의창구, 성산구, 함안군623.0
45창원시석동100000진해구 전역(용원동,웅동 일부 제외)166.6
56진주시진주160000판문동 외 4개 동,면81.7
67진주시진주2140000수곡면 외 12개 면동238.9
78통영시욕지800욕지면1.7
89사천시곤명2000곤양면, 곤명면 일부3.8
910김해시삼계165000동 지역, 생림면313.2
연번시군명정수장명정수장 시설용량(세제곱미터/일)급수구역급수인구(천명)
4142함양군안의2000안의면2.7
4243거창군위천800위천면1.8
4344거창군거창20000거창읍41.3
4445거창군가조3300가조면2.4
4546거창군웅양800웅양면, 주상면1.9
4647합천군합천10000합천읍,대양면12.7
4748합천군적중2000적중면 외 3개 면5.1
4849합천군해인사700가야면 집단시설지구0.4
4950합천군삼가1000삼가면2.0
5051합천군가야2200가야면,야로면4.0