Overview

Dataset statistics

Number of variables4
Number of observations141
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory35.9 B

Variable types

Categorical1
Numeric3

Dataset

Description경상남도 김해시의 2011년부터 취수량(표류수)에 대한 데이터로 취수장명, 연도, 월, 취수량에 대한 항목으로 구성되어 있습니다. (단위:세제곱미터) 총 취수량은 생활용수, 농업용수(양수장)를 포함하고 있습니다. 취수장명 : 창암취수장 위치 : 경상남도 김해시 생림면 안양로 458
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15084718/fileData.do

Alerts

취수장명 has constant value ""Constant
연도 is highly overall correlated with 취수량(표류수)High correlation
취수량(표류수) is highly overall correlated with 연도High correlation
취수량(표류수) has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:28:01.188982
Analysis finished2023-12-12 03:28:02.928641
Duration1.74 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

취수장명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
창암취수장
141 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창암취수장 141
100.0%

Length

2023-12-12T12:28:03.023109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:28:03.168068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창암취수장 141
100.0%

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.383
Minimum2011
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T12:28:03.320594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2016
Q32019
95-th percentile2022
Maximum2022
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4051633
Coefficient of variation (CV)0.0016887483
Kurtosis-1.2010266
Mean2016.383
Median Absolute Deviation (MAD)3
Skewness0.013027381
Sum284310
Variance11.595137
MonotonicityIncreasing
2023-12-12T12:28:03.495853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2011 12
8.5%
2012 12
8.5%
2013 12
8.5%
2014 12
8.5%
2015 12
8.5%
2016 12
8.5%
2017 12
8.5%
2018 12
8.5%
2019 12
8.5%
2020 12
8.5%
Other values (2) 21
14.9%
ValueCountFrequency (%)
2011 12
8.5%
2012 12
8.5%
2013 12
8.5%
2014 12
8.5%
2015 12
8.5%
2016 12
8.5%
2017 12
8.5%
2018 12
8.5%
2019 12
8.5%
2020 12
8.5%
ValueCountFrequency (%)
2022 9
6.4%
2021 12
8.5%
2020 12
8.5%
2019 12
8.5%
2018 12
8.5%
2017 12
8.5%
2016 12
8.5%
2015 12
8.5%
2014 12
8.5%
2013 12
8.5%

월
Real number (ℝ)

Distinct12
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4042553
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T12:28:03.662743image/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 deviation3.4350686
Coefficient of variation (CV)0.53637283
Kurtosis-1.1930292
Mean6.4042553
Median Absolute Deviation (MAD)3
Skewness0.031148403
Sum903
Variance11.799696
MonotonicityNot monotonic
2023-12-12T12:28:03.855891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 12
8.5%
2 12
8.5%
3 12
8.5%
4 12
8.5%
5 12
8.5%
6 12
8.5%
7 12
8.5%
8 12
8.5%
9 12
8.5%
10 11
7.8%
Other values (2) 22
15.6%
ValueCountFrequency (%)
1 12
8.5%
2 12
8.5%
3 12
8.5%
4 12
8.5%
5 12
8.5%
6 12
8.5%
7 12
8.5%
8 12
8.5%
9 12
8.5%
10 11
7.8%
ValueCountFrequency (%)
12 11
7.8%
11 11
7.8%
10 11
7.8%
9 12
8.5%
8 12
8.5%
7 12
8.5%
6 12
8.5%
5 12
8.5%
4 12
8.5%
3 12
8.5%

취수량(표류수)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct141
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3898974.3
Minimum1960220
Maximum5400616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T12:28:04.049248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1960220
5-th percentile2704529
Q13160200
median3769510
Q34693600
95-th percentile5248220
Maximum5400616
Range3440396
Interquartile range (IQR)1533400

Descriptive statistics

Standard deviation873902.24
Coefficient of variation (CV)0.22413644
Kurtosis-1.1732154
Mean3898974.3
Median Absolute Deviation (MAD)749510
Skewness-0.00048774175
Sum5.4975538 × 108
Variance7.6370512 × 1011
MonotonicityNot monotonic
2023-12-12T12:28:04.255011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4241760 1
 
0.7%
3659235 1
 
0.7%
3160200 1
 
0.7%
2747938 1
 
0.7%
3198687 1
 
0.7%
2835421 1
 
0.7%
2988138 1
 
0.7%
3512063 1
 
0.7%
3196572 1
 
0.7%
4700540 1
 
0.7%
Other values (131) 131
92.9%
ValueCountFrequency (%)
1960220 1
0.7%
2067544 1
0.7%
2268355 1
0.7%
2343909 1
0.7%
2490696 1
0.7%
2664496 1
0.7%
2697431 1
0.7%
2704529 1
0.7%
2706659 1
0.7%
2739893 1
0.7%
ValueCountFrequency (%)
5400616 1
0.7%
5389933 1
0.7%
5382765 1
0.7%
5359083 1
0.7%
5323098 1
0.7%
5321950 1
0.7%
5260800 1
0.7%
5248220 1
0.7%
5216722 1
0.7%
5163830 1
0.7%

Interactions

2023-12-12T12:28:02.235918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:28:01.309664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:28:01.776624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:28:02.384775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:28:01.457001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:28:01.925964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:28:02.525539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:28:01.605378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:28:02.076865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:28:04.398467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월취수량(표류수)
연도1.0000.0000.789
월0.0001.0000.000
취수량(표류수)0.7890.0001.000
2023-12-12T12:28:04.510394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월취수량(표류수)
연도1.000-0.045-0.642
월-0.0451.0000.018
취수량(표류수)-0.6420.0181.000

Missing values

2023-12-12T12:28:02.737637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:28:02.873192image/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

취수장명연도월취수량(표류수)
0창암취수장201114241760
1창암취수장201123824670
2창암취수장201134163530
3창암취수장201144126060
4창암취수장201154294950
5창암취수장201164359642
6창암취수장201174723288
7창암취수장201184673320
8창암취수장201194494460
9창암취수장2011104530680
취수장명연도월취수량(표류수)
131창암취수장2021123589169
132창암취수장202213678359
133창암취수장202223220762
134창암취수장202233494490
135창암취수장202243395171
136창암취수장202253716478
137창암취수장202263438812
138창암취수장202273138448
139창암취수장202283161036
140창암취수장202293037796