Overview

Dataset statistics

Number of variables5
Number of observations22
Missing cells5
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory50.0 B

Variable types

Text1
Numeric4

Dataset

Description전라남도 공공장소 및 관광지에 구축된 와이파이로 2019년, 2020년, 2021, 2022년 시군 별로 현황을 조회할 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15106035/fileData.do

Alerts

2019 is highly overall correlated with 2020High correlation
2020 is highly overall correlated with 2019 and 1 other fieldsHigh correlation
2021 is highly overall correlated with 2022High correlation
2022 is highly overall correlated with 2020 and 1 other fieldsHigh correlation
2019 has 2 (9.1%) missing valuesMissing
2020 has 1 (4.5%) missing valuesMissing
2021 has 1 (4.5%) missing valuesMissing
2022 has 1 (4.5%) missing valuesMissing
시군 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:28:09.666676
Analysis finished2023-12-12 14:28:11.768929
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T23:28:11.923814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters35
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

Unique22 ?
Unique (%)100.0%

Sample

1st row목포시
2nd row여수시
3rd row순천시
4th row나주시
5th row광양시
ValueCountFrequency (%)
목포시 1
 
4.5%
여수시 1
 
4.5%
진도군 1
 
4.5%
완도군 1
 
4.5%
장성군 1
 
4.5%
영광군 1
 
4.5%
함평군 1
 
4.5%
무안군 1
 
4.5%
영암군 1
 
4.5%
해남군 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T23:28:12.280104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
양 2
 
3.0%
도 2
 
3.0%
진 2
 
3.0%
순 2
 
3.0%
흥 2
 
3.0%
안 2
 
3.0%
광 2
 
3.0%
Other values (25) 27
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
양 2
 
3.0%
도 2
 
3.0%
진 2
 
3.0%
순 2
 
3.0%
흥 2
 
3.0%
안 2
 
3.0%
광 2
 
3.0%
Other values (25) 27
40.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
양 2
 
3.0%
도 2
 
3.0%
진 2
 
3.0%
순 2
 
3.0%
흥 2
 
3.0%
안 2
 
3.0%
광 2
 
3.0%
Other values (25) 27
40.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 17
25.8%
시 5
 
7.6%
성 3
 
4.5%
양 2
 
3.0%
도 2
 
3.0%
진 2
 
3.0%
순 2
 
3.0%
흥 2
 
3.0%
안 2
 
3.0%
광 2
 
3.0%
Other values (25) 27
40.9%

2019
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)85.0%
Missing2
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean45.2
Minimum1
Maximum275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:28:12.441049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q19.5
median24
Q341.25
95-th percentile166.7
Maximum275
Range274
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation67.361241
Coefficient of variation (CV)1.490293
Kurtosis7.0509824
Mean45.2
Median Absolute Deviation (MAD)15
Skewness2.5948334
Sum904
Variance4537.5368
MonotonicityNot monotonic
2023-12-12T23:28:12.588088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
51 2
 
9.1%
2 2
 
9.1%
10 2
 
9.1%
7 1
 
4.5%
36 1
 
4.5%
1 1
 
4.5%
38 1
 
4.5%
8 1
 
4.5%
161 1
 
4.5%
275 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 2
 
9.1%
ValueCountFrequency (%)
1 1
4.5%
2 2
9.1%
7 1
4.5%
8 1
4.5%
10 2
9.1%
12 1
4.5%
20 1
4.5%
23 1
4.5%
25 1
4.5%
26 1
4.5%
ValueCountFrequency (%)
275 1
4.5%
161 1
4.5%
119 1
4.5%
51 2
9.1%
38 1
4.5%
36 1
4.5%
27 1
4.5%
26 1
4.5%
25 1
4.5%
23 1
4.5%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)95.2%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean30.428571
Minimum5
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:28:12.728972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q113
median19
Q337
95-th percentile85
Maximum133
Range128
Interquartile range (IQR)24

Descriptive statistics

Standard deviation30.981561
Coefficient of variation (CV)1.0181734
Kurtosis5.5274238
Mean30.428571
Median Absolute Deviation (MAD)9
Skewness2.2649839
Sum639
Variance959.85714
MonotonicityNot monotonic
2023-12-12T23:28:12.872156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
17 2
 
9.1%
19 1
 
4.5%
18 1
 
4.5%
7 1
 
4.5%
11 1
 
4.5%
10 1
 
4.5%
30 1
 
4.5%
5 1
 
4.5%
37 1
 
4.5%
13 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
5 1
4.5%
6 1
4.5%
7 1
4.5%
10 1
4.5%
11 1
4.5%
13 1
4.5%
15 1
4.5%
17 2
9.1%
18 1
4.5%
19 1
4.5%
ValueCountFrequency (%)
133 1
4.5%
85 1
4.5%
68 1
4.5%
44 1
4.5%
38 1
4.5%
37 1
4.5%
30 1
4.5%
25 1
4.5%
21 1
4.5%
20 1
4.5%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)90.5%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean53.52381
Minimum4
Maximum493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:28:13.023371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q114
median25
Q340
95-th percentile118
Maximum493
Range489
Interquartile range (IQR)26

Descriptive statistics

Standard deviation104.41964
Coefficient of variation (CV)1.9509008
Kurtosis17.712513
Mean53.52381
Median Absolute Deviation (MAD)13
Skewness4.0993587
Sum1124
Variance10903.462
MonotonicityNot monotonic
2023-12-12T23:28:13.196584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
25 2
 
9.1%
20 2
 
9.1%
493 1
 
4.5%
118 1
 
4.5%
14 1
 
4.5%
27 1
 
4.5%
36 1
 
4.5%
10 1
 
4.5%
50 1
 
4.5%
48 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
4 1
4.5%
8 1
4.5%
10 1
4.5%
11 1
4.5%
12 1
4.5%
14 1
4.5%
16 1
4.5%
20 2
9.1%
24 1
4.5%
25 2
9.1%
ValueCountFrequency (%)
493 1
4.5%
118 1
4.5%
91 1
4.5%
50 1
4.5%
48 1
4.5%
40 1
4.5%
36 1
4.5%
32 1
4.5%
27 1
4.5%
25 2
9.1%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)76.2%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean24.047619
Minimum3
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T23:28:13.334572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median15
Q331
95-th percentile116
Maximum119
Range116
Interquartile range (IQR)26

Descriptive statistics

Standard deviation32.854948
Coefficient of variation (CV)1.3662454
Kurtosis5.4266252
Mean24.047619
Median Absolute Deviation (MAD)11
Skewness2.4422377
Sum505
Variance1079.4476
MonotonicityNot monotonic
2023-12-12T23:28:13.451036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 4
18.2%
16 2
 
9.1%
7 2
 
9.1%
116 1
 
4.5%
33 1
 
4.5%
17 1
 
4.5%
10 1
 
4.5%
11 1
 
4.5%
36 1
 
4.5%
4 1
 
4.5%
Other values (6) 6
27.3%
ValueCountFrequency (%)
3 4
18.2%
4 1
 
4.5%
5 1
 
4.5%
7 2
9.1%
10 1
 
4.5%
11 1
 
4.5%
15 1
 
4.5%
16 2
9.1%
17 1
 
4.5%
18 1
 
4.5%
ValueCountFrequency (%)
119 1
4.5%
116 1
4.5%
36 1
4.5%
33 1
4.5%
32 1
4.5%
31 1
4.5%
18 1
4.5%
17 1
4.5%
16 2
9.1%
15 1
4.5%

Interactions

2023-12-12T23:28:11.014572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:09.848308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.228432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.561857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:11.114407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:09.942972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.309453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.680164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:11.219432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.039960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.388225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.777972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:11.336106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.128564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.464314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:28:10.865447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:28:13.539361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군2019202020212022
시군1.0001.0001.0001.0001.000
20191.0001.0000.7420.8060.699
20201.0000.7421.0000.8420.742
20211.0000.8060.8421.0000.623
20221.0000.6990.7420.6231.000
2023-12-12T23:28:13.655926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019202020212022
20191.0000.6630.2030.449
20200.6631.0000.1840.504
20210.2030.1841.0000.564
20220.4490.5040.5641.000

Missing values

2023-12-12T23:28:11.471595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:28:11.579492image/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.
2023-12-12T23:28:11.702580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군2019202020212022
0목포시16119493<NA>
1여수시275133118116
2순천시256891119
3나주시23254032
4광양시119443231
5담양군<NA><NA>113
6곡성군106<NA>3
7구례군2015125
8고흥군2621818
9보성군1285247
시군2019202020212022
12강진군8182033
13해남군38174836
14영암군10135016
15무안군<NA>37257
16함평군51171011
17영광군15363
18장성군36302716
19완도군2102510
20진도군2112017
21신안군77143