Overview

Dataset statistics

Number of variables5
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory44.1 B

Variable types

Text1
Numeric3
Categorical1

Dataset

Description양산시 2011년부터 2020년까지 월별 취수량을 원동취수장, 물금취수장, 신도시취수장 등 지역별 취수량을 확인할 수 있습니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15085964

Alerts

기준일 has constant value ""Constant
온양가압장(원동취수장) 월 취수량 is highly overall correlated with 물금취수장 월 취수량 and 1 other fieldsHigh correlation
물금취수장 월 취수량 is highly overall correlated with 온양가압장(원동취수장) 월 취수량High correlation
신도시취수장 월 취수량 is highly overall correlated with 온양가압장(원동취수장) 월 취수량High correlation
연도 has unique valuesUnique
온양가압장(원동취수장) 월 취수량 has unique valuesUnique
신도시취수장 월 취수량 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:04:41.585498
Analysis finished2023-12-11 00:04:42.839312
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Text

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T09:04:43.070287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.25
Min length7

Characters and Unicode

Total characters870
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

Unique120 ?
Unique (%)100.0%

Sample

1st row2011년1월
2nd row2011년2월
3rd row2011년3월
4th row2011년4월
5th row2011년5월
ValueCountFrequency (%)
2011년1월 1
 
0.8%
2011년2월 1
 
0.8%
2018년5월 1
 
0.8%
2018년4월 1
 
0.8%
2018년3월 1
 
0.8%
2018년2월 1
 
0.8%
2018년1월 1
 
0.8%
2017년12월 1
 
0.8%
2017년11월 1
 
0.8%
2017년10월 1
 
0.8%
Other values (110) 110
91.7%
2023-12-11T09:04:43.492284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 170
19.5%
2 164
18.9%
0 142
16.3%
년 120
13.8%
월 120
13.8%
3 22
 
2.5%
4 22
 
2.5%
5 22
 
2.5%
6 22
 
2.5%
7 22
 
2.5%
Other values (2) 44
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 630
72.4%
Other Letter 240
 
27.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 170
27.0%
2 164
26.0%
0 142
22.5%
3 22
 
3.5%
4 22
 
3.5%
5 22
 
3.5%
6 22
 
3.5%
7 22
 
3.5%
8 22
 
3.5%
9 22
 
3.5%
Other Letter
ValueCountFrequency (%)
년 120
50.0%
월 120
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 630
72.4%
Hangul 240
 
27.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 170
27.0%
2 164
26.0%
0 142
22.5%
3 22
 
3.5%
4 22
 
3.5%
5 22
 
3.5%
6 22
 
3.5%
7 22
 
3.5%
8 22
 
3.5%
9 22
 
3.5%
Hangul
ValueCountFrequency (%)
년 120
50.0%
월 120
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 630
72.4%
Hangul 240
 
27.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 170
27.0%
2 164
26.0%
0 142
22.5%
3 22
 
3.5%
4 22
 
3.5%
5 22
 
3.5%
6 22
 
3.5%
7 22
 
3.5%
8 22
 
3.5%
9 22
 
3.5%
Hangul
ValueCountFrequency (%)
년 120
50.0%
월 120
50.0%

온양가압장(원동취수장) 월 취수량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean836826.43
Minimum630870
Maximum998859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T09:04:43.640147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum630870
5-th percentile676562
Q1780427.5
median849300
Q3907515
95-th percentile964677
Maximum998859
Range367989
Interquartile range (IQR)127087.5

Descriptive statistics

Standard deviation91401.748
Coefficient of variation (CV)0.10922426
Kurtosis-0.73082249
Mean836826.43
Median Absolute Deviation (MAD)64600
Skewness-0.43797834
Sum1.0041917 × 108
Variance8.3542795 × 109
MonotonicityNot monotonic
2023-12-11T09:04:43.782463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
752710 1
 
0.8%
782320 1
 
0.8%
847820 1
 
0.8%
901630 1
 
0.8%
863500 1
 
0.8%
893820 1
 
0.8%
843830 1
 
0.8%
913310 1
 
0.8%
947690 1
 
0.8%
913080 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
630870 1
0.8%
636440 1
0.8%
638350 1
0.8%
670160 1
0.8%
671250 1
0.8%
674890 1
0.8%
676650 1
0.8%
678800 1
0.8%
680570 1
0.8%
682600 1
0.8%
ValueCountFrequency (%)
998859 1
0.8%
990420 1
0.8%
974910 1
0.8%
973289 1
0.8%
972510 1
0.8%
972030 1
0.8%
964290 1
0.8%
955800 1
0.8%
947690 1
0.8%
947170 1
0.8%

물금취수장 월 취수량
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425299.03
Minimum79400
Maximum1052620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T09:04:43.926838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79400
5-th percentile149427
Q1244558
median338490
Q3629630
95-th percentile887337
Maximum1052620
Range973220
Interquartile range (IQR)385072

Descriptive statistics

Standard deviation240180.63
Coefficient of variation (CV)0.56473355
Kurtosis-0.37740954
Mean425299.03
Median Absolute Deviation (MAD)134610
Skewness0.77726677
Sum51035884
Variance5.7686737 × 1010
MonotonicityNot monotonic
2023-12-11T09:04:44.078075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
244360 2
 
1.7%
167130 1
 
0.8%
464230 1
 
0.8%
865780 1
 
0.8%
871340 1
 
0.8%
1001000 1
 
0.8%
1052620 1
 
0.8%
946940 1
 
0.8%
1004840 1
 
0.8%
959480 1
 
0.8%
Other values (109) 109
90.8%
ValueCountFrequency (%)
79400 1
0.8%
79750 1
0.8%
102350 1
0.8%
114640 1
0.8%
125410 1
0.8%
126380 1
0.8%
150640 1
0.8%
157300 1
0.8%
160910 1
0.8%
166180 1
0.8%
ValueCountFrequency (%)
1052620 1
0.8%
1004840 1
0.8%
1001000 1
0.8%
959480 1
0.8%
950030 1
0.8%
946940 1
0.8%
884200 1
0.8%
871340 1
0.8%
865780 1
0.8%
811970 1
0.8%

신도시취수장 월 취수량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365715.38
Minimum830
Maximum1009090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T09:04:44.560288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum830
5-th percentile167671
Q1263275
median347030
Q3403407.5
95-th percentile805180
Maximum1009090
Range1008260
Interquartile range (IQR)140132.5

Descriptive statistics

Standard deviation172069.53
Coefficient of variation (CV)0.4705012
Kurtosis4.3457919
Mean365715.38
Median Absolute Deviation (MAD)69365
Skewness1.8131661
Sum43885846
Variance2.9607922 × 1010
MonotonicityNot monotonic
2023-12-11T09:04:44.710505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
238700 1
 
0.8%
304400 1
 
0.8%
690080 1
 
0.8%
750120 1
 
0.8%
831840 1
 
0.8%
828360 1
 
0.8%
803960 1
 
0.8%
931500 1
 
0.8%
957640 1
 
0.8%
966190 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
830 1
0.8%
106060 1
0.8%
141110 1
0.8%
145730 1
0.8%
158980 1
0.8%
166360 1
0.8%
167740 1
0.8%
168190 1
0.8%
181010 1
0.8%
195240 1
0.8%
ValueCountFrequency (%)
1009090 1
0.8%
966190 1
0.8%
957640 1
0.8%
931500 1
0.8%
831840 1
0.8%
828360 1
0.8%
803960 1
0.8%
750120 1
0.8%
690080 1
0.8%
555010 1
0.8%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2021-08-12
120 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-08-12
2nd row2021-08-12
3rd row2021-08-12
4th row2021-08-12
5th row2021-08-12

Common Values

ValueCountFrequency (%)
2021-08-12 120
100.0%

Length

2023-12-11T09:04:44.887519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:04:45.049920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-08-12 120
100.0%

Interactions

2023-12-11T09:04:42.385798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:41.727043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:42.067364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:42.508399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:41.831198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:42.189448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:42.597638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:41.945122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:04:42.277084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:04:45.150962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온양가압장(원동취수장) 월 취수량물금취수장 월 취수량신도시취수장 월 취수량
온양가압장(원동취수장) 월 취수량1.0000.5220.305
물금취수장 월 취수량0.5221.0000.745
신도시취수장 월 취수량0.3050.7451.000
2023-12-11T09:04:45.245518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온양가압장(원동취수장) 월 취수량물금취수장 월 취수량신도시취수장 월 취수량
온양가압장(원동취수장) 월 취수량1.0000.6420.619
물금취수장 월 취수량0.6421.0000.403
신도시취수장 월 취수량0.6190.4031.000

Missing values

2023-12-11T09:04:42.706068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:04:42.799704image/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

연도온양가압장(원동취수장) 월 취수량물금취수장 월 취수량신도시취수장 월 취수량기준일
02011년1월7527101671302387002021-08-12
12011년2월6957801573001589802021-08-12
22011년3월6364402586302195302021-08-12
32011년4월6383501661802823602021-08-12
42011년5월6701601762902379102021-08-12
52011년6월6712502195902437702021-08-12
62011년7월7298402687602970102021-08-12
72011년8월7235802296803391202021-08-12
82011년9월6918102443602988902021-08-12
92011년10월6975101254104045402021-08-12
연도온양가압장(원동취수장) 월 취수량물금취수장 월 취수량신도시취수장 월 취수량기준일
1102020년3월9166807156503788802021-08-12
1112020년4월8892976850003528502021-08-12
1122020년5월9332006994503426802021-08-12
1132020년6월9057007097203942002021-08-12
1142020년7월9348168842002585302021-08-12
1152020년8월9725109500302208402021-08-12
1162020년9월9073107010004014002021-08-12
1172020년10월9300907082703914702021-08-12
1182020년11월8984906853403910202021-08-12
1192020년12월9443706947104030302021-08-12