Overview

Dataset statistics

Number of variables6
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory57.9 B

Variable types

Numeric5
Text1

Dataset

Description전라남도_여수시_상수도 보급현황(급수인구) 여수시 27개 읍면동 인구수, 면적, 읍면동 별 급수 인구수와 비율에 대한 내용입니다.
URLhttps://www.data.go.kr/data/15048962/fileData.do

Alerts

순번 is highly overall correlated with 보급률High correlation
총인구(명) is highly overall correlated with 급수인구(명)High correlation
총면적(제곱킬로미터) is highly overall correlated with 보급률High correlation
급수인구(명) is highly overall correlated with 총인구(명)High correlation
보급률 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 has unique valuesUnique
읍면동 has unique valuesUnique
총인구(명) has unique valuesUnique
급수인구(명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:04:46.464271
Analysis finished2023-12-12 23:04:49.090488
Duration2.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T08:04:49.163712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-13T08:04:49.305838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

읍면동
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T08:04:49.519182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9259259
Min length2

Characters and Unicode

Total characters79
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row돌산읍
2nd row소라면
3rd row율촌면
4th row화양면
5th row남면
ValueCountFrequency (%)
돌산읍 1
 
3.7%
국동 1
 
3.7%
삼일동 1
 
3.7%
주삼동 1
 
3.7%
여천동 1
 
3.7%
시전동 1
 
3.7%
쌍봉동 1
 
3.7%
만덕동 1
 
3.7%
둔덕동 1
 
3.7%
미평동 1
 
3.7%
Other values (17) 17
63.0%
2023-12-13T08:04:50.130964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
26.6%
면 6
 
7.6%
삼 3
 
3.8%
화 2
 
2.5%
산 2
 
2.5%
여 2
 
2.5%
덕 2
 
2.5%
문 2
 
2.5%
서 2
 
2.5%
시 1
 
1.3%
Other values (36) 36
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
26.6%
면 6
 
7.6%
삼 3
 
3.8%
화 2
 
2.5%
산 2
 
2.5%
여 2
 
2.5%
덕 2
 
2.5%
문 2
 
2.5%
서 2
 
2.5%
시 1
 
1.3%
Other values (36) 36
45.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
26.6%
면 6
 
7.6%
삼 3
 
3.8%
화 2
 
2.5%
산 2
 
2.5%
여 2
 
2.5%
덕 2
 
2.5%
문 2
 
2.5%
서 2
 
2.5%
시 1
 
1.3%
Other values (36) 36
45.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
26.6%
면 6
 
7.6%
삼 3
 
3.8%
화 2
 
2.5%
산 2
 
2.5%
여 2
 
2.5%
덕 2
 
2.5%
문 2
 
2.5%
서 2
 
2.5%
시 1
 
1.3%
Other values (36) 36
45.6%

총인구(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10104.667
Minimum1134
Maximum42399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T08:04:50.274715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1134
5-th percentile2034.3
Q13568
median6259
Q311773.5
95-th percentile28994.5
Maximum42399
Range41265
Interquartile range (IQR)8205.5

Descriptive statistics

Standard deviation9872.3968
Coefficient of variation (CV)0.97701361
Kurtosis3.7094054
Mean10104.667
Median Absolute Deviation (MAD)3971
Skewness1.8789367
Sum272826
Variance97464219
MonotonicityNot monotonic
2023-12-13T08:04:50.379346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12534 1
 
3.7%
21991 1
 
3.7%
1134 1
 
3.7%
2139 1
 
3.7%
7862 1
 
3.7%
19996 1
 
3.7%
42399 1
 
3.7%
31996 1
 
3.7%
10230 1
 
3.7%
9756 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1134 1
3.7%
2010 1
3.7%
2091 1
3.7%
2139 1
3.7%
2714 1
3.7%
2811 1
3.7%
3534 1
3.7%
3602 1
3.7%
3979 1
3.7%
4273 1
3.7%
ValueCountFrequency (%)
42399 1
3.7%
31996 1
3.7%
21991 1
3.7%
19996 1
3.7%
19563 1
3.7%
16771 1
3.7%
12534 1
3.7%
11013 1
3.7%
10545 1
3.7%
10230 1
3.7%

총면적(제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.868148
Minimum0.43
Maximum72.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T08:04:50.498963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.43
5-th percentile0.548
Q12.365
median6.79
Q326.955
95-th percentile71.457
Maximum72.63
Range72.2
Interquartile range (IQR)24.59

Descriptive statistics

Standard deviation24.593177
Coefficient of variation (CV)1.3034229
Kurtosis0.37856191
Mean18.868148
Median Absolute Deviation (MAD)5.44
Skewness1.3580574
Sum509.44
Variance604.82436
MonotonicityNot monotonic
2023-12-13T08:04:50.614767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11.53 2
 
7.4%
72.03 1
 
3.7%
2.36 1
 
3.7%
72.63 1
 
3.7%
7.53 1
 
3.7%
6.06 1
 
3.7%
7.29 1
 
3.7%
13.96 1
 
3.7%
4.08 1
 
3.7%
3.09 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0.43 1
3.7%
0.5 1
3.7%
0.66 1
3.7%
1.2 1
3.7%
1.35 1
3.7%
2.24 1
3.7%
2.36 1
3.7%
2.37 1
3.7%
3.09 1
3.7%
3.19 1
3.7%
ValueCountFrequency (%)
72.63 1
3.7%
72.03 1
3.7%
70.12 1
3.7%
60.53 1
3.7%
48.03 1
3.7%
42.55 1
3.7%
27.55 1
3.7%
26.36 1
3.7%
13.96 1
3.7%
11.53 2
7.4%

급수인구(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9844
Minimum1134
Maximum42399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T08:04:50.728326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1134
5-th percentile1689.9
Q13568
median5629
Q311731
95-th percentile28396
Maximum42399
Range41265
Interquartile range (IQR)8163

Descriptive statistics

Standard deviation9793.1912
Coefficient of variation (CV)0.9948386
Kurtosis4.1132044
Mean9844
Median Absolute Deviation (MAD)3699
Skewness1.9451378
Sum265788
Variance95906594
MonotonicityNot monotonic
2023-12-13T08:04:50.822976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12449 1
 
3.7%
18259 1
 
3.7%
1134 1
 
3.7%
2139 1
 
3.7%
7862 1
 
3.7%
19996 1
 
3.7%
42399 1
 
3.7%
31996 1
 
3.7%
10230 1
 
3.7%
9576 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1134 1
3.7%
1587 1
3.7%
1930 1
3.7%
2010 1
3.7%
2139 1
3.7%
2811 1
3.7%
3534 1
3.7%
3602 1
3.7%
3979 1
3.7%
4273 1
3.7%
ValueCountFrequency (%)
42399 1
3.7%
31996 1
3.7%
19996 1
3.7%
19563 1
3.7%
18259 1
3.7%
16771 1
3.7%
12449 1
3.7%
11013 1
3.7%
10545 1
3.7%
10230 1
3.7%

보급률
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.266667
Minimum71.1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T08:04:50.919019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71.1
5-th percentile76.29
Q199.65
median100
Q3100
95-th percentile100
Maximum100
Range28.9
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation8.5090902
Coefficient of variation (CV)0.088390826
Kurtosis3.4637516
Mean96.266667
Median Absolute Deviation (MAD)0
Skewness-2.1968646
Sum2599.2
Variance72.404615
MonotonicityNot monotonic
2023-12-13T08:04:51.049955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
100.0 20
74.1%
99.3 1
 
3.7%
83.0 1
 
3.7%
94.5 1
 
3.7%
77.2 1
 
3.7%
71.1 1
 
3.7%
75.9 1
 
3.7%
98.2 1
 
3.7%
ValueCountFrequency (%)
71.1 1
 
3.7%
75.9 1
 
3.7%
77.2 1
 
3.7%
83.0 1
 
3.7%
94.5 1
 
3.7%
98.2 1
 
3.7%
99.3 1
 
3.7%
100.0 20
74.1%
ValueCountFrequency (%)
100.0 20
74.1%
99.3 1
 
3.7%
98.2 1
 
3.7%
94.5 1
 
3.7%
83.0 1
 
3.7%
77.2 1
 
3.7%
75.9 1
 
3.7%
71.1 1
 
3.7%

Interactions

2023-12-13T08:04:48.417182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:46.659558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.096014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.460715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.021610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.501465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:46.789939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.163020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.571462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.087179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.594664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:46.856116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.223184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.652855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.179372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.699049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:46.932917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.305524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.754354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.267262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.786547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.022407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.379224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:47.895302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.338653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:04:51.117923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번읍면동총인구(명)총면적(제곱킬로미터)급수인구(명)보급률
순번1.0001.0000.0990.4890.3340.000
읍면동1.0001.0001.0001.0001.0001.000
총인구(명)0.0991.0001.0000.0000.9840.316
총면적(제곱킬로미터)0.4891.0000.0001.0000.0000.916
급수인구(명)0.3341.0000.9840.0001.0000.000
보급률0.0001.0000.3160.9160.0001.000
2023-12-13T08:04:51.223059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번총인구(명)총면적(제곱킬로미터)급수인구(명)보급률
순번1.0000.209-0.1540.2570.621
총인구(명)0.2091.000-0.0310.9930.059
총면적(제곱킬로미터)-0.154-0.0311.000-0.063-0.582
급수인구(명)0.2570.993-0.0631.0000.142
보급률0.6210.059-0.5820.1421.000

Missing values

2023-12-13T08:04:48.923069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:04:49.038667image/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

순번읍면동총인구(명)총면적(제곱킬로미터)급수인구(명)보급률
01돌산읍1253472.031244999.3
12소라면2199160.531825983.0
23율촌면593148.03560694.5
34화양면625970.12483177.2
45남면271442.55193071.1
56화정면209126.36158775.9
67삼산면201027.552010100.0
78동문동42730.664273100.0
89한려동28111.22811100.0
910중앙동39790.53979100.0
순번읍면동총인구(명)총면적(제곱킬로미터)급수인구(명)보급률
1718문수동195632.2419563100.0
1819미평동110133.0911013100.0
1920둔덕동97564.08957698.2
2021만덕동1023013.9610230100.0
2122쌍봉동319967.2931996100.0
2223시전동423996.0642399100.0
2324여천동199967.5319996100.0
2425주삼동786211.537862100.0
2526삼일동213972.632139100.0
2627묘도동113411.531134100.0