Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory57.4 B

Variable types

Numeric5
Categorical1

Dataset

Description대전광역시 상수도사업본부 업종별 급수전수, 상수도 사용부과량, 부과량에 따른 부과액 , 톤당 단가에 관한 통계자료입니다.
Author대전광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15062635/fileData.do

Alerts

급수전수 is highly overall correlated with 부과량(세제곱미터) and 2 other fieldsHigh correlation
부과량(세제곱미터) is highly overall correlated with 급수전수 and 2 other fieldsHigh correlation
부과액(천원) is highly overall correlated with 급수전수 and 2 other fieldsHigh correlation
세제곱미터당 단가(원) is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 급수전수 and 3 other fieldsHigh correlation
급수전수 has 6 (20.0%) zerosZeros

Reproduction

Analysis started2024-01-14 13:38:32.615848
Analysis finished2024-01-14 13:38:36.081912
Duration3.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T22:38:36.153124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019.5
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7370208
Coefficient of variation (CV)0.00086012421
Kurtosis-1.2783673
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum60585
Variance3.0172414
MonotonicityDecreasing
2024-01-14T22:38:36.321604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 5
16.7%
2021 5
16.7%
2020 5
16.7%
2019 5
16.7%
2018 5
16.7%
2017 5
16.7%
ValueCountFrequency (%)
2017 5
16.7%
2018 5
16.7%
2019 5
16.7%
2020 5
16.7%
2021 5
16.7%
2022 5
16.7%
ValueCountFrequency (%)
2022 5
16.7%
2021 5
16.7%
2020 5
16.7%
2019 5
16.7%
2018 5
16.7%
2017 5
16.7%

구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
가정용
6 
목욕용
6 
일반용
6 
공업용
6 
기타(계룡, 세종시)
4 

Length

Max length12
Median length3
Mean length4.6666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가정용
2nd row목욕용
3rd row일반용
4th row공업용
5th row기타(계룡, 세종시)

Common Values

ValueCountFrequency (%)
가정용 6
20.0%
목욕용 6
20.0%
일반용 6
20.0%
공업용 6
20.0%
기타(계룡, 세종시) 4
13.3%
기 타(계룡,세종시) 2
 
6.7%

Length

2024-01-14T22:38:36.494847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:38:36.647049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가정용 6
16.7%
목욕용 6
16.7%
일반용 6
16.7%
공업용 6
16.7%
기타(계룡 4
11.1%
세종시 4
11.1%
기 2
 
5.6%
타(계룡,세종시 2
 
5.6%

급수전수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27148.133
Minimum0
Maximum85958
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T22:38:36.832162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q181
median116
Q350529
95-th percentile85419
Maximum85958
Range85958
Interquartile range (IQR)50448

Descriptive statistics

Standard deviation35591.337
Coefficient of variation (CV)1.3110049
Kurtosis-1.210819
Mean27148.133
Median Absolute Deviation (MAD)116
Skewness0.73879893
Sum814444
Variance1.2667433 × 109
MonotonicityNot monotonic
2024-01-14T22:38:37.040159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 6
20.0%
115 3
 
10.0%
85419 2
 
6.7%
81 2
 
6.7%
50529 2
 
6.7%
118 2
 
6.7%
85958 1
 
3.3%
85276 1
 
3.3%
48604 1
 
3.3%
98 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
0 6
20.0%
74 1
 
3.3%
81 2
 
6.7%
88 1
 
3.3%
92 1
 
3.3%
98 1
 
3.3%
115 3
10.0%
117 1
 
3.3%
118 2
 
6.7%
48604 1
 
3.3%
ValueCountFrequency (%)
85958 1
3.3%
85419 2
6.7%
85342 1
3.3%
85276 1
3.3%
84939 1
3.3%
51928 1
3.3%
50529 2
6.7%
49968 1
3.3%
49321 1
3.3%
48604 1
3.3%

부과량(세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39953896
Minimum825528
Maximum1.1068405 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T22:38:37.174077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum825528
5-th percentile930636
Q111403765
median29112772
Q352046278
95-th percentile1.0957164 × 108
Maximum1.1068405 × 108
Range1.0985852 × 108
Interquartile range (IQR)40642513

Descriptive statistics

Standard deviation38735407
Coefficient of variation (CV)0.96950263
Kurtosis-0.51512597
Mean39953896
Median Absolute Deviation (MAD)21085318
Skewness0.91228941
Sum1.1986169 × 109
Variance1.5004318 × 1015
MonotonicityNot monotonic
2024-01-14T22:38:37.404825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
109571645 2
 
6.7%
930636 2
 
6.7%
47760808 2
 
6.7%
11403765 2
 
6.7%
107728633 1
 
3.3%
106384508 1
 
3.3%
21723340 1
 
3.3%
12635335 1
 
3.3%
53527493 1
 
3.3%
1361505 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
825528 1
3.3%
930636 2
6.7%
1294253 1
3.3%
1351933 1
3.3%
1361505 1
3.3%
11094714 1
3.3%
11403765 2
6.7%
12015241 1
3.3%
12078150 1
3.3%
12635335 1
3.3%
ValueCountFrequency (%)
110684051 1
3.3%
109571645 2
6.7%
107728633 1
3.3%
106384508 1
3.3%
106382553 1
3.3%
53527493 1
3.3%
52275534 1
3.3%
51358511 1
3.3%
49037668 1
3.3%
47760808 2
6.7%

부과액(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21482185
Minimum530309
Maximum52643945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T22:38:37.605541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum530309
5-th percentile618714
Q12014880
median9646703.5
Q345707580
95-th percentile52461513
Maximum52643945
Range52113636
Interquartile range (IQR)43692700

Descriptive statistics

Standard deviation21961443
Coefficient of variation (CV)1.0223095
Kurtosis-1.7956521
Mean21482185
Median Absolute Deviation (MAD)8906605.5
Skewness0.42022341
Sum6.4446556 × 108
Variance4.8230498 × 1014
MonotonicityNot monotonic
2024-01-14T22:38:37.799908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
52461513 2
 
6.7%
618714 2
 
6.7%
41310461 2
 
6.7%
2014880 2
 
6.7%
9511894 2
 
6.7%
48879095 1
 
3.3%
861482 1
 
3.3%
8571051 1
 
3.3%
2206388 1
 
3.3%
46759108 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
530309 1
3.3%
618714 2
6.7%
861482 1
3.3%
894087 1
3.3%
900622 1
3.3%
1964492 1
3.3%
2014880 2
6.7%
2042571 1
3.3%
2131602 1
3.3%
2206388 1
3.3%
ValueCountFrequency (%)
52643945 1
3.3%
52461513 2
6.7%
51103139 1
3.3%
50880135 1
3.3%
48879095 1
3.3%
46759108 1
3.3%
45929131 1
3.3%
45042929 1
3.3%
41310461 2
6.7%
40292290 1
3.3%

세제곱미터당 단가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean504.66667
Minimum170
Maximum879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T22:38:37.987930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum170
5-th percentile175.45
Q1319.75
median478.5
Q3665.75
95-th percentile875.65
Maximum879
Range709
Interquartile range (IQR)346

Descriptive statistics

Standard deviation244.11746
Coefficient of variation (CV)0.48372019
Kurtosis-1.1890132
Mean504.66667
Median Absolute Deviation (MAD)186.5
Skewness0.15988396
Sum15140
Variance59593.333
MonotonicityNot monotonic
2024-01-14T22:38:38.160218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
177 3
 
10.0%
666 2
 
6.7%
479 2
 
6.7%
665 2
 
6.7%
865 2
 
6.7%
480 1
 
3.3%
395 1
 
3.3%
175 1
 
3.3%
874 1
 
3.3%
657 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
170 1
 
3.3%
175 1
 
3.3%
176 1
 
3.3%
177 3
10.0%
313 1
 
3.3%
314 1
 
3.3%
337 1
 
3.3%
350 1
 
3.3%
390 1
 
3.3%
395 1
 
3.3%
ValueCountFrequency (%)
879 1
3.3%
877 1
3.3%
874 1
3.3%
865 2
6.7%
822 1
3.3%
666 2
6.7%
665 2
6.7%
657 1
3.3%
642 1
3.3%
480 1
3.3%

Interactions

2024-01-14T22:38:35.328337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:32.886618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.495223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:34.087081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:34.867182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:35.416856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.013249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.618706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:34.210464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:34.949533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:35.505856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.115020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.727513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:34.620008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:35.063898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:35.642802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.251297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.829084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:34.699930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:35.160381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:35.750120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.369031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:33.928730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:34.781630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:38:35.241816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T22:38:38.292410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분급수전수부과량(세제곱미터)부과액(천원)세제곱미터당 단가(원)
연도1.0000.0000.0000.0000.0000.000
구분0.0001.0000.8940.9170.9691.000
급수전수0.0000.8941.0000.8180.9290.893
부과량(세제곱미터)0.0000.9170.8181.0000.8760.914
부과액(천원)0.0000.9690.9290.8761.0000.968
세제곱미터당 단가(원)0.0001.0000.8930.9140.9681.000
2024-01-14T22:38:38.446347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도급수전수부과량(세제곱미터)부과액(천원)세제곱미터당 단가(원)구분
연도1.0000.066-0.051-0.090-0.0870.000
급수전수0.0661.0000.6740.6680.2870.742
부과량(세제곱미터)-0.0510.6741.0000.9930.1870.859
부과액(천원)-0.0900.6680.9931.0000.1990.735
세제곱미터당 단가(원)-0.0870.2870.1870.1991.0001.000
구분0.0000.7420.8590.7351.0001.000

Missing values

2024-01-14T22:38:35.911261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T22:38:36.029461image/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

연도구분급수전수부과량(세제곱미터)부과액(천원)세제곱미터당 단가(원)
02022가정용8595810772863348879095454
12022목욕용74825528530309642
22022일반용519284903766840292290822
32022공업용117120152412042571170
42022기타(계룡, 세종시)03315546111160696337
52021가정용8541910957164552461513479
62021목욕용81930636618714665
72021일반용505294776080841310461865
82021공업용118114037652014880177
92021기타(계룡, 세종시)0303446669511894313
연도구분급수전수부과량(세제곱미터)부과액(천원)세제곱미터당 단가(원)
202018가정용8527610638450851103139480
212018목욕용921294253861482666
222018일반용493215227553445929131879
232018공업용115120781502131602176
242018기 타(계룡,세종시)02579854610056050390
252017가정용8493911068405152643945476
262017목욕용981361505894087657
272017일반용486045352749346759108874
282017공업용115126353352206388175
292017기 타(계룡,세종시)0217233408571051395