Overview

Dataset statistics

Number of variables10
Number of observations22
Missing cells20
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory88.8 B

Variable types

Text6
Categorical1
Numeric3

Dataset

Description충청북도 내에 소재하고 있는 수영장 시설에 대한 현황 정보입니다. (시설명, 소재지, 소재지도로명주소, 소재지지번주소, 면적, 수영조면적, 경기장규격, 준공년도, 관리기관, 전화번호 등의 컬럼 정보를 제공합니다.)
Author충청북도
URLhttps://www.data.go.kr/data/15071060/fileData.do

Alerts

소재지지번주소 has 1 (4.5%) missing valuesMissing
면적(제곱면적) has 1 (4.5%) missing valuesMissing
수영조면적(제곱미터) has 8 (36.4%) missing valuesMissing
경기장규격(미터) has 8 (36.4%) missing valuesMissing
준공년도 has 2 (9.1%) missing valuesMissing

Reproduction

Analysis started2024-03-14 23:22:21.740574
Analysis finished2024-03-14 23:22:25.259086
Duration3.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T08:22:25.967606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11.5
Mean length9.1818182
Min length6

Characters and Unicode

Total characters202
Distinct characters71
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)90.9%

Sample

1st row청주실내수영장
2nd row내수국민체육센터 수영장
3rd row영운국민체육센터
4th row충북 곰두리체육관 수영장
5th row충북 체육회관 수영장
ValueCountFrequency (%)
국민체육센터 5
 
14.3%
수영장 4
 
11.4%
맹동혁신 2
 
5.7%
충북 2
 
5.7%
청주실내수영장 1
 
2.9%
제천국민체육센터 1
 
2.9%
물놀이장 1
 
2.9%
혁신도시 1
 
2.9%
음성근로자복지관 1
 
2.9%
반다비 1
 
2.9%
Other values (16) 16
45.7%
2024-03-15T08:22:26.823223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
센 15
 
7.4%
체 15
 
7.4%
육 15
 
7.4%
터 15
 
7.4%
13
 
6.4%
국 13
 
6.4%
민 13
 
6.4%
장 7
 
3.5%
수 7
 
3.5%
영 6
 
3.0%
Other values (61) 83
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
93.6%
Space Separator 13
 
6.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 15
 
7.9%
체 15
 
7.9%
육 15
 
7.9%
터 15
 
7.9%
국 13
 
6.9%
민 13
 
6.9%
장 7
 
3.7%
수 7
 
3.7%
영 6
 
3.2%
주 4
 
2.1%
Other values (60) 79
41.8%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 189
93.6%
Common 13
 
6.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 15
 
7.9%
체 15
 
7.9%
육 15
 
7.9%
터 15
 
7.9%
국 13
 
6.9%
민 13
 
6.9%
장 7
 
3.7%
수 7
 
3.7%
영 6
 
3.2%
주 4
 
2.1%
Other values (60) 79
41.8%
Common
ValueCountFrequency (%)
13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 189
93.6%
ASCII 13
 
6.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
센 15
 
7.9%
체 15
 
7.9%
육 15
 
7.9%
터 15
 
7.9%
국 13
 
6.9%
민 13
 
6.9%
장 7
 
3.7%
수 7
 
3.7%
영 6
 
3.2%
주 4
 
2.1%
Other values (60) 79
41.8%
ASCII
ValueCountFrequency (%)
13
100.0%

소재지
Categorical

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Memory size304.0 B
청주시
6 
충주시
4 
음성군
4 
제천시
2 
단양군
2 
Other values (4)
4 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique4 ?
Unique (%)18.2%

Sample

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

Common Values

ValueCountFrequency (%)
청주시 6
27.3%
충주시 4
18.2%
음성군 4
18.2%
제천시 2
 
9.1%
단양군 2
 
9.1%
보은군 1
 
4.5%
옥천군 1
 
4.5%
증평군 1
 
4.5%
진천군 1
 
4.5%

Length

2024-03-15T08:22:27.044597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:22:27.260961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청주시 6
27.3%
충주시 4
18.2%
음성군 4
18.2%
제천시 2
 
9.1%
단양군 2
 
9.1%
보은군 1
 
4.5%
옥천군 1
 
4.5%
증평군 1
 
4.5%
진천군 1
 
4.5%

소재지도로명주소
Text

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T08:22:28.034067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length25.5
Mean length20.681818
Min length15

Characters and Unicode

Total characters455
Distinct characters82
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)90.9%

Sample

1st row충청북도 청주시 서원구 흥덕로 69
2nd row충청북도 청주시 청원구 내수읍 청암로 91-4
3rd row충청북도 청주시 상당구 수영로101번길 63
4th row충청북도 청주시 청원구 토성로 21
5th row충청북도 청주시 상당구 단재로317번길 59-9
ValueCountFrequency (%)
충청북도 22
 
20.8%
청주시 6
 
5.7%
음성군 5
 
4.7%
충주시 4
 
3.8%
맹동면 3
 
2.8%
학예로 2
 
1.9%
제천시 2
 
1.9%
청원구 2
 
1.9%
상당구 2
 
1.9%
14 2
 
1.9%
Other values (55) 56
52.8%
2024-03-15T08:22:29.056355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86
18.9%
청 33
 
7.3%
충 26
 
5.7%
북 22
 
4.8%
도 22
 
4.8%
1 19
 
4.2%
로 17
 
3.7%
시 13
 
2.9%
3 11
 
2.4%
군 11
 
2.4%
Other values (72) 195
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 292
64.2%
Space Separator 86
 
18.9%
Decimal Number 69
 
15.2%
Dash Punctuation 8
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 33
 
11.3%
충 26
 
8.9%
북 22
 
7.5%
도 22
 
7.5%
로 17
 
5.8%
시 13
 
4.5%
군 11
 
3.8%
주 10
 
3.4%
성 8
 
2.7%
읍 7
 
2.4%
Other values (60) 123
42.1%
Decimal Number
ValueCountFrequency (%)
1 19
27.5%
3 11
15.9%
9 9
13.0%
6 7
 
10.1%
8 5
 
7.2%
4 5
 
7.2%
0 4
 
5.8%
2 4
 
5.8%
5 3
 
4.3%
7 2
 
2.9%
Space Separator
ValueCountFrequency (%)
86
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 292
64.2%
Common 163
35.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 33
 
11.3%
충 26
 
8.9%
북 22
 
7.5%
도 22
 
7.5%
로 17
 
5.8%
시 13
 
4.5%
군 11
 
3.8%
주 10
 
3.4%
성 8
 
2.7%
읍 7
 
2.4%
Other values (60) 123
42.1%
Common
ValueCountFrequency (%)
86
52.8%
1 19
 
11.7%
3 11
 
6.7%
9 9
 
5.5%
- 8
 
4.9%
6 7
 
4.3%
8 5
 
3.1%
4 5
 
3.1%
0 4
 
2.5%
2 4
 
2.5%
Other values (2) 5
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 292
64.2%
ASCII 163
35.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
86
52.8%
1 19
 
11.7%
3 11
 
6.7%
9 9
 
5.5%
- 8
 
4.9%
6 7
 
4.3%
8 5
 
3.1%
4 5
 
3.1%
0 4
 
2.5%
2 4
 
2.5%
Other values (2) 5
 
3.1%
Hangul
ValueCountFrequency (%)
청 33
 
11.3%
충 26
 
8.9%
북 22
 
7.5%
도 22
 
7.5%
로 17
 
5.8%
시 13
 
4.5%
군 11
 
3.8%
주 10
 
3.4%
성 8
 
2.7%
읍 7
 
2.4%
Other values (60) 123
42.1%

소재지지번주소
Text

MISSING 

Distinct20
Distinct (%)95.2%
Missing1
Missing (%)4.5%
Memory size304.0 B
2024-03-15T08:22:29.860293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length20.190476
Min length15

Characters and Unicode

Total characters424
Distinct characters69
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row충청북도 청주시 서원구 사직1동 788
2nd row충청북도 청주시 청원구 내수읍 학평리 141-28
3rd row충청북도 청주시 상당구 영운동 163
4th row충청북도 청주시 청원구 사천동 623-5
5th row충청북도 청주시 상당구 방서동38-7
ValueCountFrequency (%)
충청북도 21
21.0%
청주시 6
 
6.0%
음성군 5
 
5.0%
맹동면 3
 
3.0%
동성리 3
 
3.0%
충주시 3
 
3.0%
제천시 2
 
2.0%
상당구 2
 
2.0%
청원구 2
 
2.0%
486 2
 
2.0%
Other values (50) 51
51.0%
2024-03-15T08:22:30.839034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
82
19.3%
청 29
 
6.8%
충 24
 
5.7%
도 22
 
5.2%
북 21
 
5.0%
동 15
 
3.5%
2 14
 
3.3%
리 12
 
2.8%
시 11
 
2.6%
성 10
 
2.4%
Other values (59) 184
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 262
61.8%
Space Separator 82
 
19.3%
Decimal Number 71
 
16.7%
Dash Punctuation 9
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 29
 
11.1%
충 24
 
9.2%
도 22
 
8.4%
북 21
 
8.0%
동 15
 
5.7%
리 12
 
4.6%
시 11
 
4.2%
성 10
 
3.8%
군 9
 
3.4%
주 9
 
3.4%
Other values (47) 100
38.2%
Decimal Number
ValueCountFrequency (%)
2 14
19.7%
8 10
14.1%
6 10
14.1%
1 8
11.3%
4 8
11.3%
3 7
9.9%
7 5
 
7.0%
5 5
 
7.0%
9 2
 
2.8%
0 2
 
2.8%
Space Separator
ValueCountFrequency (%)
82
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 262
61.8%
Common 162
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 29
 
11.1%
충 24
 
9.2%
도 22
 
8.4%
북 21
 
8.0%
동 15
 
5.7%
리 12
 
4.6%
시 11
 
4.2%
성 10
 
3.8%
군 9
 
3.4%
주 9
 
3.4%
Other values (47) 100
38.2%
Common
ValueCountFrequency (%)
82
50.6%
2 14
 
8.6%
8 10
 
6.2%
6 10
 
6.2%
- 9
 
5.6%
1 8
 
4.9%
4 8
 
4.9%
3 7
 
4.3%
7 5
 
3.1%
5 5
 
3.1%
Other values (2) 4
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 262
61.8%
ASCII 162
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82
50.6%
2 14
 
8.6%
8 10
 
6.2%
6 10
 
6.2%
- 9
 
5.6%
1 8
 
4.9%
4 8
 
4.9%
3 7
 
4.3%
7 5
 
3.1%
5 5
 
3.1%
Other values (2) 4
 
2.5%
Hangul
ValueCountFrequency (%)
청 29
 
11.1%
충 24
 
9.2%
도 22
 
8.4%
북 21
 
8.0%
동 15
 
5.7%
리 12
 
4.6%
시 11
 
4.2%
성 10
 
3.8%
군 9
 
3.4%
주 9
 
3.4%
Other values (47) 100
38.2%

면적(제곱면적)
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)95.2%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean4560.9524
Minimum741
Maximum24277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T08:22:31.137668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum741
5-th percentile874
Q11910
median3795
Q34949
95-th percentile7951
Maximum24277
Range23536
Interquartile range (IQR)3039

Descriptive statistics

Standard deviation4943.7563
Coefficient of variation (CV)1.0839307
Kurtosis13.690142
Mean4560.9524
Median Absolute Deviation (MAD)1716
Skewness3.4189372
Sum95780
Variance24440726
MonotonicityNot monotonic
2024-03-15T08:22:31.353482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4949 2
 
9.1%
3795 1
 
4.5%
2079 1
 
4.5%
741 1
 
4.5%
874 1
 
4.5%
5206 1
 
4.5%
1777 1
 
4.5%
1910 1
 
4.5%
3070 1
 
4.5%
7951 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
741 1
4.5%
874 1
4.5%
980 1
4.5%
1532 1
4.5%
1777 1
4.5%
1910 1
4.5%
2079 1
4.5%
2561 1
4.5%
3070 1
4.5%
3670 1
4.5%
ValueCountFrequency (%)
24277 1
4.5%
7951 1
4.5%
7223 1
4.5%
5598 1
4.5%
5206 1
4.5%
4949 2
9.1%
4856 1
4.5%
3965 1
4.5%
3817 1
4.5%
3795 1
4.5%

수영조면적(제곱미터)
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)78.6%
Missing8
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean758.28571
Minimum300
Maximum2113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T08:22:31.689783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile300
Q1348
median450
Q31052.25
95-th percentile1879.65
Maximum2113
Range1813
Interquartile range (IQR)704.25

Descriptive statistics

Standard deviation594.43515
Coefficient of variation (CV)0.78391975
Kurtosis0.71812875
Mean758.28571
Median Absolute Deviation (MAD)137.5
Skewness1.3219396
Sum10616
Variance353353.14
MonotonicityNot monotonic
2024-03-15T08:22:32.063233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
300 2
 
9.1%
500 2
 
9.1%
348 2
 
9.1%
1250 1
 
4.5%
2113 1
 
4.5%
1754 1
 
4.5%
375 1
 
4.5%
1053 1
 
4.5%
400 1
 
4.5%
325 1
 
4.5%
(Missing) 8
36.4%
ValueCountFrequency (%)
300 2
9.1%
325 1
4.5%
348 2
9.1%
375 1
4.5%
400 1
4.5%
500 2
9.1%
1050 1
4.5%
1053 1
4.5%
1250 1
4.5%
1754 1
4.5%
ValueCountFrequency (%)
2113 1
4.5%
1754 1
4.5%
1250 1
4.5%
1053 1
4.5%
1050 1
4.5%
500 2
9.1%
400 1
4.5%
375 1
4.5%
348 2
9.1%
325 1
4.5%

경기장규격(미터)
Text

MISSING 

Distinct11
Distinct (%)78.6%
Missing8
Missing (%)36.4%
Memory size304.0 B
2024-03-15T08:22:32.663922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0714286
Min length7

Characters and Unicode

Total characters99
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)64.3%

Sample

1st row50×25×10
2nd row25×10×6
3rd row25×20×8
4th row25×20×8
5th row25×20×6
ValueCountFrequency (%)
25×20×8 3
21.4%
25×20×6 2
14.3%
50×25×10 1
 
7.1%
25×10×6 1
 
7.1%
25×15×8 1
 
7.1%
50x25x8 1
 
7.1%
25×12×6 1
 
7.1%
25×15×6 1
 
7.1%
25×16×8 1
 
7.1%
25×13×6 1
 
7.1%
2024-03-15T08:22:33.665053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
× 26
26.3%
2 20
20.2%
5 18
18.2%
0 10
 
10.1%
1 8
 
8.1%
8 7
 
7.1%
6 7
 
7.1%
X 2
 
2.0%
3 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71
71.7%
Math Symbol 26
 
26.3%
Uppercase Letter 2
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 20
28.2%
5 18
25.4%
0 10
14.1%
1 8
 
11.3%
8 7
 
9.9%
6 7
 
9.9%
3 1
 
1.4%
Math Symbol
ValueCountFrequency (%)
× 26
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97
98.0%
Latin 2
 
2.0%

Most frequent character per script

Common
ValueCountFrequency (%)
× 26
26.8%
2 20
20.6%
5 18
18.6%
0 10
 
10.3%
1 8
 
8.2%
8 7
 
7.2%
6 7
 
7.2%
3 1
 
1.0%
Latin
ValueCountFrequency (%)
X 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
73.7%
None 26
 
26.3%

Most frequent character per block

None
ValueCountFrequency (%)
× 26
100.0%
ASCII
ValueCountFrequency (%)
2 20
27.4%
5 18
24.7%
0 10
13.7%
1 8
 
11.0%
8 7
 
9.6%
6 7
 
9.6%
X 2
 
2.7%
3 1
 
1.4%

준공년도
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)75.0%
Missing2
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean2009.6
Minimum1990
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-15T08:22:34.031904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1994.75
Q12004.75
median2008.5
Q32019.75
95-th percentile2023.05
Maximum2024
Range34
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.9652026
Coefficient of variation (CV)0.0049587991
Kurtosis-0.79287729
Mean2009.6
Median Absolute Deviation (MAD)7
Skewness-0.091392859
Sum40192
Variance99.305263
MonotonicityNot monotonic
2024-03-15T08:22:34.414564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2005 3
13.6%
2022 3
13.6%
1999 2
 
9.1%
1990 1
 
4.5%
1995 1
 
4.5%
2009 1
 
4.5%
2019 1
 
4.5%
2010 1
 
4.5%
2023 1
 
4.5%
2024 1
 
4.5%
Other values (5) 5
22.7%
(Missing) 2
 
9.1%
ValueCountFrequency (%)
1990 1
 
4.5%
1995 1
 
4.5%
1999 2
9.1%
2004 1
 
4.5%
2005 3
13.6%
2006 1
 
4.5%
2008 1
 
4.5%
2009 1
 
4.5%
2010 1
 
4.5%
2012 1
 
4.5%
ValueCountFrequency (%)
2024 1
 
4.5%
2023 1
 
4.5%
2022 3
13.6%
2019 1
 
4.5%
2013 1
 
4.5%
2012 1
 
4.5%
2010 1
 
4.5%
2009 1
 
4.5%
2008 1
 
4.5%
2006 1
 
4.5%

관리기관
Text

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T08:22:35.225138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9.5
Mean length8.2727273
Min length3

Characters and Unicode

Total characters182
Distinct characters47
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)50.0%

Sample

1st row청주시시설관리공단
2nd row학교법인 주성학원
3rd row청주시시설관리공단
4th row충북사회복지개발회
5th row충북체육회
ValueCountFrequency (%)
충주시시설관리공단 4
 
14.3%
시설관리사업소 4
 
14.3%
청주시시설관리공단 3
 
10.7%
옥천군 1
 
3.6%
단양군청 1
 
3.6%
경제과 1
 
3.6%
음성군 1
 
3.6%
진천군체육진흥지원단 1
 
3.6%
체육진흥과 1
 
3.6%
증평군 1
 
3.6%
Other values (10) 10
35.7%
2024-03-15T08:22:36.315551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 21
 
11.5%
설 12
 
6.6%
관 12
 
6.6%
리 12
 
6.6%
단 9
 
4.9%
주 8
 
4.4%
공 7
 
3.8%
사 7
 
3.8%
업 7
 
3.8%
충 6
 
3.3%
Other values (37) 81
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 176
96.7%
Space Separator 6
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 21
 
11.9%
설 12
 
6.8%
관 12
 
6.8%
리 12
 
6.8%
단 9
 
5.1%
주 8
 
4.5%
공 7
 
4.0%
사 7
 
4.0%
업 7
 
4.0%
충 6
 
3.4%
Other values (36) 75
42.6%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 176
96.7%
Common 6
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 21
 
11.9%
설 12
 
6.8%
관 12
 
6.8%
리 12
 
6.8%
단 9
 
5.1%
주 8
 
4.5%
공 7
 
4.0%
사 7
 
4.0%
업 7
 
4.0%
충 6
 
3.4%
Other values (36) 75
42.6%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 176
96.7%
ASCII 6
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 21
 
11.9%
설 12
 
6.8%
관 12
 
6.8%
리 12
 
6.8%
단 9
 
5.1%
주 8
 
4.5%
공 7
 
4.0%
사 7
 
4.0%
업 7
 
4.0%
충 6
 
3.4%
Other values (36) 75
42.6%
ASCII
ValueCountFrequency (%)
6
100.0%

전화번호
Text

Distinct18
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-15T08:22:37.090054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters264
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

Unique15 ?
Unique (%)68.2%

Sample

1st row043-270-8513
2nd row043-218-6566
3rd row043-270-8513
4th row043-216-0031
5th row043-297-1071
ValueCountFrequency (%)
043-870-7834 3
 
13.6%
043-270-8513 2
 
9.1%
043-871-5917 2
 
9.1%
043-540-3742 1
 
4.5%
043-730-4971 1
 
4.5%
043-871-2464 1
 
4.5%
043-871-3632 1
 
4.5%
043-872-1130 1
 
4.5%
043-539-3801 1
 
4.5%
043-835-4922 1
 
4.5%
Other values (8) 8
36.4%
2024-03-15T08:22:38.458477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 44
16.7%
4 38
14.4%
0 37
14.0%
3 36
13.6%
7 23
8.7%
8 19
7.2%
1 19
7.2%
5 15
 
5.7%
2 14
 
5.3%
6 11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 220
83.3%
Dash Punctuation 44
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 38
17.3%
0 37
16.8%
3 36
16.4%
7 23
10.5%
8 19
8.6%
1 19
8.6%
5 15
 
6.8%
2 14
 
6.4%
6 11
 
5.0%
9 8
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 44
16.7%
4 38
14.4%
0 37
14.0%
3 36
13.6%
7 23
8.7%
8 19
7.2%
1 19
7.2%
5 15
 
5.7%
2 14
 
5.3%
6 11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 44
16.7%
4 38
14.4%
0 37
14.0%
3 36
13.6%
7 23
8.7%
8 19
7.2%
1 19
7.2%
5 15
 
5.7%
2 14
 
5.3%
6 11
 
4.2%

Interactions

2024-03-15T08:22:23.771645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:22:22.371142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:22:22.977733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:22:24.020545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:22:22.524024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:22:23.247952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:22:24.288756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:22:22.718830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:22:23.507577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:22:38.749428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명소재지소재지도로명주소소재지지번주소면적(제곱면적)수영조면적(제곱미터)경기장규격(미터)준공년도관리기관전화번호
시설명1.0000.9251.0001.0001.0001.0001.0001.0001.0001.000
소재지0.9251.0000.9250.9260.4830.6450.9360.0000.9770.985
소재지도로명주소1.0000.9251.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0000.9261.0001.0001.0001.0001.0001.0001.0001.000
면적(제곱면적)1.0000.4831.0001.0001.0000.8020.6580.5950.6240.000
수영조면적(제곱미터)1.0000.6451.0001.0000.8021.0000.9330.5850.9271.000
경기장규격(미터)1.0000.9361.0001.0000.6580.9331.0000.0000.9931.000
준공년도1.0000.0001.0001.0000.5950.5850.0001.0000.7870.722
관리기관1.0000.9771.0001.0000.6240.9270.9930.7871.0000.990
전화번호1.0000.9851.0001.0000.0001.0001.0000.7220.9901.000
2024-03-15T08:22:39.093747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱면적)수영조면적(제곱미터)준공년도소재지
면적(제곱면적)1.0000.2070.0820.214
수영조면적(제곱미터)0.2071.000-0.1510.000
준공년도0.082-0.1511.0000.000
소재지0.2140.0000.0001.000

Missing values

2024-03-15T08:22:24.649511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:22:24.921266image/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.
2024-03-15T08:22:25.131466image/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

시설명소재지소재지도로명주소소재지지번주소면적(제곱면적)수영조면적(제곱미터)경기장규격(미터)준공년도관리기관전화번호
0청주실내수영장청주시충청북도 청주시 서원구 흥덕로 69충청북도 청주시 서원구 사직1동 7883795125050×25×101990청주시시설관리공단043-270-8513
1내수국민체육센터 수영장청주시충청북도 청주시 청원구 내수읍 청암로 91-4충청북도 청주시 청원구 내수읍 학평리 141-2898030025×10×62005학교법인 주성학원043-218-6566
2영운국민체육센터청주시충청북도 청주시 상당구 수영로101번길 63충청북도 청주시 상당구 영운동 1633965<NA><NA>2022청주시시설관리공단043-270-8513
3충북 곰두리체육관 수영장청주시충청북도 청주시 청원구 토성로 21충청북도 청주시 청원구 사천동 623-53817<NA><NA>1999충북사회복지개발회043-216-0031
4충북 체육회관 수영장청주시충청북도 청주시 상당구 단재로317번길 59-9충청북도 청주시 상당구 방서동38-74856<NA><NA>1995충북체육회043-297-1071
5청주 푸르미센터 수영장청주시충청북도 청주시 흥덕구 가로수로 969충청북도 청주시 흥덕구 휴암동 33824277<NA><NA>2009청주시시설관리공단043-870-7834
6충주 국민체육센터충주시충청북도 충주시 중원대로 3306충청북도 충주시 호암동 569256150025×20×82005충주시시설관리공단043-870-7834
7장애인형국민체육센터충주시충청북도 충주시 모시래길 105충청북도 충주시 호암동 967559850025×20×82019충주시시설관리공단043-870-7834
8클린에너지파크충주시충청북도 충주시 성종두담길 21충청북도 충주시 대소원면 두정리 222-8153234825×20×62010충주시시설관리공단043-870-7944
9서충주 국민체육센터충주시충청북도 충주시 중앙탑면 용전리 648<NA>367034825×20×62023충주시시설관리공단043-850-9276
시설명소재지소재지도로명주소소재지지번주소면적(제곱면적)수영조면적(제곱미터)경기장규격(미터)준공년도관리기관전화번호
12보은국민체육센터보은군충청북도 보은군 보은읍 군청길 38-1충청북도 보은군 보은읍 이평리 45-6795130025×12×62006보은군 스포츠산업과043-540-3742
13옥천국민체육센터옥천군충청북도 옥천군 옥천읍 동부로 39-1충청북도 옥천군 옥천읍 문정리 420-2307037525×15×62012옥천군 체육사업소043-730-4971
14증평국민체육센터증평군충청북도 증평군 증평읍 인삼로 23-1충청북도 증평군 증평읍 송산리 254-11910105325×20×82008증평군 체육진흥과043-835-4922
15진천국민체육센터진천군충청북도 진천읍 문화로 69-11충청북도 진천읍 교성리 74177740025×16×82013진천군체육진흥지원단043-539-3801
16반다비 국민체육센터음성군충청북도 음성군 음성읍 체육공원길 36충청북도 음성군 음성읍 신천리 3425206<NA><NA><NA>시설관리사업소043-872-1130
17음성근로자복지관음성군충청북도 음성군 대소면 대화3길 13-14충청북도 음성군 대소면 태생리 60587432525×13×62005음성군 경제과043-871-3632
18맹동혁신 국민체육센터음성군충청북도 음성군 맹동면 학예로 14충청북도 음성군 맹동면 동성리 4864949<NA><NA>2022시설관리사업소043-871-5917
19혁신도시 물놀이장음성군충청북도 음성군 맹동면 이수로 31충청북도 음성군 맹동면 동성리 228<NA><NA><NA><NA>시설관리사업소043-871-2464
20맹동혁신 국민체육센터단양군충청북도 음성군 맹동면 학예로 14충청북도 음성군 맹동면 동성리 4864949<NA><NA>2022시설관리사업소043-871-5917
21청소년수련관단양군충청북도 단양군 단양읍 삼봉로 187-18 단양군청소년수련관충청북도 단양군 단양읍 도전리 672-1741105050×21×82004단양군청 문화체육과043-420-2584