Overview

Dataset statistics

Number of variables6
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory56.7 B

Variable types

Numeric3
Text3

Dataset

Description1999년부터 2021년 까지 연도별 광주광역시 유수율(생산량 대비 사용량)과 누수율(생산량 대비 누수량)정보입니다.
Author광주광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15100205/fileData.do

Alerts

연도 is highly overall correlated with 유수율(퍼센트) and 1 other fieldsHigh correlation
유수율(퍼센트) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
누수율(퍼센트) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
연도 has unique valuesUnique
생산량(세제곱미터) has unique valuesUnique
사용량(세제곱미터) has unique valuesUnique
누수량(세제곱미터) has unique valuesUnique
누수율(퍼센트) has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:21:35.400318
Analysis finished2023-12-12 13:21:36.876105
Duration1.48 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010
Minimum1999
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T22:21:36.937665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2000.1
Q12004.5
median2010
Q32015.5
95-th percentile2019.9
Maximum2021
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.0033742935
Kurtosis-1.2
Mean2010
Median Absolute Deviation (MAD)6
Skewness0
Sum46230
Variance46
MonotonicityStrictly increasing
2023-12-12T22:21:37.044995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1999 1
 
4.3%
2000 1
 
4.3%
2021 1
 
4.3%
2020 1
 
4.3%
2019 1
 
4.3%
2018 1
 
4.3%
2017 1
 
4.3%
2016 1
 
4.3%
2015 1
 
4.3%
2014 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1999 1
4.3%
2000 1
4.3%
2001 1
4.3%
2002 1
4.3%
2003 1
4.3%
2004 1
4.3%
2005 1
4.3%
2006 1
4.3%
2007 1
4.3%
2008 1
4.3%
ValueCountFrequency (%)
2021 1
4.3%
2020 1
4.3%
2019 1
4.3%
2018 1
4.3%
2017 1
4.3%
2016 1
4.3%
2015 1
4.3%
2014 1
4.3%
2013 1
4.3%
2012 1
4.3%

생산량(세제곱미터)
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T22:21:37.226251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters253
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row145,883,381
2nd row147,544,961
3rd row146,197,504
4th row148,709,883
5th row149,489,539
ValueCountFrequency (%)
145,883,381 1
 
4.3%
173,422,895 1
 
4.3%
176,827,727 1
 
4.3%
180,010,648 1
 
4.3%
182,942,051 1
 
4.3%
178,558,718 1
 
4.3%
178,468,448 1
 
4.3%
173,271,063 1
 
4.3%
171,970,090 1
 
4.3%
174,178,846 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T22:21:37.540548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 46
18.2%
1 41
16.2%
8 26
10.3%
7 24
9.5%
4 23
9.1%
5 18
 
7.1%
9 17
 
6.7%
0 17
 
6.7%
3 16
 
6.3%
6 13
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 207
81.8%
Other Punctuation 46
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 41
19.8%
8 26
12.6%
7 24
11.6%
4 23
11.1%
5 18
8.7%
9 17
8.2%
0 17
8.2%
3 16
 
7.7%
6 13
 
6.3%
2 12
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 46
18.2%
1 41
16.2%
8 26
10.3%
7 24
9.5%
4 23
9.1%
5 18
 
7.1%
9 17
 
6.7%
0 17
 
6.7%
3 16
 
6.3%
6 13
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 46
18.2%
1 41
16.2%
8 26
10.3%
7 24
9.5%
4 23
9.1%
5 18
 
7.1%
9 17
 
6.7%
0 17
 
6.7%
3 16
 
6.3%
6 13
 
5.1%

사용량(세제곱미터)
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T22:21:37.726996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters253
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row109,106,236
2nd row114,252,233
3rd row115,616,799
4th row117,629,208
5th row118,849,264
ValueCountFrequency (%)
109,106,236 1
 
4.3%
145,801,650 1
 
4.3%
160,640,296 1
 
4.3%
158,781,505 1
 
4.3%
160,409,538 1
 
4.3%
154,815,465 1
 
4.3%
153,839,802 1
 
4.3%
148,894,662 1
 
4.3%
146,416,103 1
 
4.3%
147,795,372 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T22:21:38.036407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 46
18.2%
1 38
15.0%
4 25
9.9%
6 23
9.1%
8 21
8.3%
0 19
7.5%
2 19
7.5%
5 19
7.5%
3 18
 
7.1%
9 13
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 207
81.8%
Other Punctuation 46
 
18.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38
18.4%
4 25
12.1%
6 23
11.1%
8 21
10.1%
0 19
9.2%
2 19
9.2%
5 19
9.2%
3 18
8.7%
9 13
 
6.3%
7 12
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 46
18.2%
1 38
15.0%
4 25
9.9%
6 23
9.1%
8 21
8.3%
0 19
7.5%
2 19
7.5%
5 19
7.5%
3 18
 
7.1%
9 13
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 46
18.2%
1 38
15.0%
4 25
9.9%
6 23
9.1%
8 21
8.3%
0 19
7.5%
2 19
7.5%
5 19
7.5%
3 18
 
7.1%
9 13
 
5.1%

유수율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.292174
Minimum74.79
Maximum90.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T22:21:38.175020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74.79
5-th percentile77.606
Q180.26
median82.8
Q386.065
95-th percentile90.586
Maximum90.86
Range16.07
Interquartile range (IQR)5.805

Descriptive statistics

Standard deviation4.1357718
Coefficient of variation (CV)0.049653787
Kurtosis-0.35427785
Mean83.292174
Median Absolute Deviation (MAD)3.13
Skewness0.053243554
Sum1915.72
Variance17.104609
MonotonicityIncreasing
2023-12-12T22:21:38.335614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
79.1 2
 
8.7%
74.79 1
 
4.3%
84.52 1
 
4.3%
90.86 1
 
4.3%
90.85 1
 
4.3%
88.21 1
 
4.3%
87.68 1
 
4.3%
86.7 1
 
4.3%
86.2 1
 
4.3%
85.93 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
74.79 1
4.3%
77.44 1
4.3%
79.1 2
8.7%
79.5 1
4.3%
80.0 1
4.3%
80.52 1
4.3%
81.4 1
4.3%
81.68 1
4.3%
82.03 1
4.3%
82.35 1
4.3%
ValueCountFrequency (%)
90.86 1
4.3%
90.85 1
4.3%
88.21 1
4.3%
87.68 1
4.3%
86.7 1
4.3%
86.2 1
4.3%
85.93 1
4.3%
85.14 1
4.3%
84.85 1
4.3%
84.52 1
4.3%

누수량(세제곱미터)
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T22:21:38.548042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9130435
Min length9

Characters and Unicode

Total characters228
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row20,184,781
2nd row17,843,646
3rd row15,826,296
4th row16,029,361
5th row15,745,471
ValueCountFrequency (%)
20,184,781 1
 
4.3%
20,628,200 1
 
4.3%
9,158,064 1
 
4.3%
13,810,387 1
 
4.3%
15,300,145 1
 
4.3%
16,547,792 1
 
4.3%
17,586,675 1
 
4.3%
17,528,261 1
 
4.3%
18,310,254 1
 
4.3%
19,425,514 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T22:21:38.884778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 46
20.2%
1 30
13.2%
2 26
11.4%
7 19
8.3%
6 19
8.3%
8 18
 
7.9%
4 18
 
7.9%
5 18
 
7.9%
0 13
 
5.7%
3 11
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 182
79.8%
Other Punctuation 46
 
20.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30
16.5%
2 26
14.3%
7 19
10.4%
6 19
10.4%
8 18
9.9%
4 18
9.9%
5 18
9.9%
0 13
7.1%
3 11
 
6.0%
9 10
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 46
20.2%
1 30
13.2%
2 26
11.4%
7 19
8.3%
6 19
8.3%
8 18
 
7.9%
4 18
 
7.9%
5 18
 
7.9%
0 13
 
5.7%
3 11
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 46
20.2%
1 30
13.2%
2 26
11.4%
7 19
8.3%
6 19
8.3%
8 18
 
7.9%
4 18
 
7.9%
5 18
 
7.9%
0 13
 
5.7%
3 11
 
4.8%

누수율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.231304
Minimum5.18
Maximum13.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T22:21:39.034164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.18
5-th percentile5.438
Q18.915
median10.53
Q311.55
95-th percentile13.815
Maximum13.91
Range8.73
Interquartile range (IQR)2.635

Descriptive statistics

Standard deviation2.3736265
Coefficient of variation (CV)0.23199647
Kurtosis0.19859993
Mean10.231304
Median Absolute Deviation (MAD)1.52
Skewness-0.43410484
Sum235.32
Variance5.6341028
MonotonicityNot monotonic
2023-12-12T22:21:39.145874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
13.84 1
 
4.3%
12.09 1
 
4.3%
5.19 1
 
4.3%
5.18 1
 
4.3%
7.67 1
 
4.3%
8.36 1
 
4.3%
9.27 1
 
4.3%
9.85 1
 
4.3%
10.12 1
 
4.3%
10.65 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
5.18 1
4.3%
5.19 1
4.3%
7.67 1
4.3%
8.36 1
4.3%
8.67 1
4.3%
8.82 1
4.3%
9.01 1
4.3%
9.27 1
4.3%
9.53 1
4.3%
9.85 1
4.3%
ValueCountFrequency (%)
13.91 1
4.3%
13.84 1
4.3%
13.59 1
4.3%
13.18 1
4.3%
12.09 1
4.3%
11.89 1
4.3%
11.21 1
4.3%
11.15 1
4.3%
10.83 1
4.3%
10.78 1
4.3%

Interactions

2023-12-12T22:21:36.465846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:35.616867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:36.194676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:36.535180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:35.992214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:36.286415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:36.620519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:36.116142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:36.387020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:21:39.219980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도생산량(세제곱미터)사용량(세제곱미터)유수율(퍼센트)누수량(세제곱미터)누수율(퍼센트)
연도1.0001.0001.0000.8661.0000.860
생산량(세제곱미터)1.0001.0001.0001.0001.0001.000
사용량(세제곱미터)1.0001.0001.0001.0001.0001.000
유수율(퍼센트)0.8661.0001.0001.0001.0000.856
누수량(세제곱미터)1.0001.0001.0001.0001.0001.000
누수율(퍼센트)0.8601.0001.0000.8561.0001.000
2023-12-12T22:21:39.333645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도유수율(퍼센트)누수율(퍼센트)
연도1.0001.000-0.535
유수율(퍼센트)1.0001.000-0.534
누수율(퍼센트)-0.535-0.5341.000

Missing values

2023-12-12T22:21:36.731982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:21:36.833739image/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

연도생산량(세제곱미터)사용량(세제곱미터)유수율(퍼센트)누수량(세제곱미터)누수율(퍼센트)
01999145,883,381109,106,23674.7920,184,78113.84
12000147,544,961114,252,23377.4417,843,64612.09
22001146,197,504115,616,79979.115,826,29610.83
32002148,709,883117,629,20879.116,029,36110.78
42003149,489,539118,849,26479.515,745,47110.53
52004154,817,147123,839,90480.014,760,2279.53
62005156,149,283125,727,01480.5213,771,8868.82
72006158,251,693128,808,10181.413,727,9278.67
82007164,598,216134,444,28381.6814,834,8429.01
92008165,309,123135,600,32782.0322,990,67013.91
연도생산량(세제곱미터)사용량(세제곱미터)유수율(퍼센트)누수량(세제곱미터)누수율(퍼센트)
132012172,043,510145,416,75784.5219,278,58211.21
142013174,178,846147,795,37284.8519,425,51411.15
152014171,970,090146,416,10385.1418,310,25410.65
162015173,271,063148,894,66285.9317,528,26110.12
172016178,468,448153,839,80286.217,586,6759.85
182017178,558,718154,815,46586.716,547,7929.27
192018182,942,051160,409,53887.6815,300,1458.36
202019180,010,648158,781,50588.2113,810,3877.67
212020176,827,727160,640,29690.859,158,0645.18
222021179,933,079163,484,29690.869,341,4265.19