Overview

Dataset statistics

Number of variables4
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory35.7 B

Variable types

Numeric2
Categorical1
Text1

Dataset

Description경상남도 밀양시 취수장의 2015년도부터 2021년 까지 월별 취수량 현황입니다
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15087163

Alerts

취수장 has constant value ""Constant
취수량 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:23:32.401597
Analysis finished2023-12-11 00:23:32.934802
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct7
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.8101
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-11T09:23:32.974106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32019
95-th percentile2021
Maximum2021
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9220445
Coefficient of variation (CV)0.00095253983
Kurtosis-1.1841187
Mean2017.8101
Median Absolute Deviation (MAD)2
Skewness0.054977519
Sum159407
Variance3.6942551
MonotonicityIncreasing
2023-12-11T09:23:33.066456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2015 12
15.2%
2016 12
15.2%
2017 12
15.2%
2018 12
15.2%
2019 12
15.2%
2020 12
15.2%
2021 7
8.9%
ValueCountFrequency (%)
2015 12
15.2%
2016 12
15.2%
2017 12
15.2%
2018 12
15.2%
2019 12
15.2%
2020 12
15.2%
2021 7
8.9%
ValueCountFrequency (%)
2021 7
8.9%
2020 12
15.2%
2019 12
15.2%
2018 12
15.2%
2017 12
15.2%
2016 12
15.2%
2015 12
15.2%

월
Real number (ℝ)

Distinct12
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.278481
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-11T09:23:33.161494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4453116
Coefficient of variation (CV)0.54874923
Kurtosis-1.178951
Mean6.278481
Median Absolute Deviation (MAD)3
Skewness0.095453079
Sum496
Variance11.870172
MonotonicityNot monotonic
2023-12-11T09:23:33.259205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 7
8.9%
2 7
8.9%
3 7
8.9%
4 7
8.9%
5 7
8.9%
6 7
8.9%
7 7
8.9%
8 6
7.6%
9 6
7.6%
10 6
7.6%
Other values (2) 12
15.2%
ValueCountFrequency (%)
1 7
8.9%
2 7
8.9%
3 7
8.9%
4 7
8.9%
5 7
8.9%
6 7
8.9%
7 7
8.9%
8 6
7.6%
9 6
7.6%
10 6
7.6%
ValueCountFrequency (%)
12 6
7.6%
11 6
7.6%
10 6
7.6%
9 6
7.6%
8 6
7.6%
7 7
8.9%
6 7
8.9%
5 7
8.9%
4 7
8.9%
3 7
8.9%

취수장
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size764.0 B
교동 취수장
79 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교동 취수장
2nd row교동 취수장
3rd row교동 취수장
4th row교동 취수장
5th row교동 취수장

Common Values

ValueCountFrequency (%)
교동 취수장 79
100.0%

Length

2023-12-11T09:23:33.385045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:33.475533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교동 79
50.0%
취수장 79
50.0%

취수량
Text

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
2023-12-11T09:23:33.682581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters632
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

Unique79 ?
Unique (%)100.0%

Sample

1st row472,471
2nd row424,755
3rd row474,458
4th row453,842
5th row463,092
ValueCountFrequency (%)
472,471 1
 
1.3%
393,231 1
 
1.3%
410,917 1
 
1.3%
372,892 1
 
1.3%
339,580 1
 
1.3%
339,313 1
 
1.3%
350,572 1
 
1.3%
360,739 1
 
1.3%
304,175 1
 
1.3%
285,529 1
 
1.3%
Other values (69) 69
87.3%
2023-12-11T09:23:34.113319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 85
13.4%
, 79
12.5%
79
12.5%
3 73
11.6%
7 53
8.4%
2 45
7.1%
5 40
6.3%
1 39
6.2%
9 39
6.2%
0 35
5.5%
Other values (2) 65
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 474
75.0%
Other Punctuation 79
 
12.5%
Space Separator 79
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 85
17.9%
3 73
15.4%
7 53
11.2%
2 45
9.5%
5 40
8.4%
1 39
8.2%
9 39
8.2%
0 35
7.4%
6 33
 
7.0%
8 32
 
6.8%
Other Punctuation
ValueCountFrequency (%)
, 79
100.0%
Space Separator
ValueCountFrequency (%)
79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 632
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 85
13.4%
, 79
12.5%
79
12.5%
3 73
11.6%
7 53
8.4%
2 45
7.1%
5 40
6.3%
1 39
6.2%
9 39
6.2%
0 35
5.5%
Other values (2) 65
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 85
13.4%
, 79
12.5%
79
12.5%
3 73
11.6%
7 53
8.4%
2 45
7.1%
5 40
6.3%
1 39
6.2%
9 39
6.2%
0 35
5.5%
Other values (2) 65
10.3%

Interactions

2023-12-11T09:23:32.655398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:32.488580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:32.733553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:32.578428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:23:34.244202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월취수량
연도1.0000.0001.000
월0.0001.0001.000
취수량1.0001.0001.000
2023-12-11T09:23:34.324145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월
연도1.000-0.102
월-0.1021.000

Missing values

2023-12-11T09:23:32.828748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:23:32.905639image/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

연도월취수장취수량
020151교동 취수장472,471
120152교동 취수장424,755
220153교동 취수장474,458
320154교동 취수장453,842
420155교동 취수장463,092
520156교동 취수장450,797
620157교동 취수장470,826
720158교동 취수장477,078
820159교동 취수장463,307
9201510교동 취수장475,974
연도월취수장취수량
69202010교동 취수장360,745
70202011교동 취수장367,727
71202012교동 취수장395,915
7220211교동 취수장436,044
7320212교동 취수장381,728
7420213교동 취수장411,283
7520214교동 취수장392,603
7620215교동 취수장420,210
7720216교동 취수장392,596
7820217교동 취수장421,640