Overview

Dataset statistics

Number of variables10
Number of observations33
Missing cells13
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory88.0 B

Variable types

Text5
Categorical1
Numeric4

Dataset

Description충청북도의 테니스장 현황을 시설명, 소재지, 소재지도로명주소, 소재지지번주소, 면적(제곱미터), 코트수(면), 수용인원, 준공년도, 관리기관, 전화번호 등 정보로 제공합니다.
Author충청북도
URLhttps://www.data.go.kr/data/15071052/fileData.do

Alerts

면적(제곱미터) is highly overall correlated with 코트수(면)High correlation
코트수(면) is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
수용인원 is highly overall correlated with 코트수(면)High correlation
소재지도로명주소 has 2 (6.1%) missing valuesMissing
코트수(면) has 1 (3.0%) missing valuesMissing
수용인원 has 10 (30.3%) missing valuesMissing
시설명 has unique valuesUnique
면적(제곱미터) has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:29:24.102625
Analysis finished2023-12-12 01:29:27.154704
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T10:29:27.294791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length9.030303
Min length5

Characters and Unicode

Total characters298
Distinct characters82
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

Unique33 ?
Unique (%)100.0%

Sample

1st row청주정구장
2nd row국제테니스장
3rd row공설테니스장
4th row강내생활체육공원테니스장
5th row탄금테니스장
ValueCountFrequency (%)
테니스장 6
 
14.3%
청주정구장 1
 
2.4%
광혜원 1
 
2.4%
소프트테니스장 1
 
2.4%
영동실외테니스장 1
 
2.4%
영동실내테니스장 1
 
2.4%
영동군민소프트테니스장 1
 
2.4%
증평테니스장 1
 
2.4%
반탄테니스장 1
 
2.4%
진전종합스포츠타운 1
 
2.4%
Other values (27) 27
64.3%
2023-12-12T10:29:27.632045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 33
 
11.1%
스 31
 
10.4%
테 30
 
10.1%
니 30
 
10.1%
공 14
 
4.7%
원 12
 
4.0%
체 10
 
3.4%
육 10
 
3.4%
9
 
3.0%
천 6
 
2.0%
Other values (72) 113
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 289
97.0%
Space Separator 9
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 33
 
11.4%
스 31
 
10.7%
테 30
 
10.4%
니 30
 
10.4%
공 14
 
4.8%
원 12
 
4.2%
체 10
 
3.5%
육 10
 
3.5%
천 6
 
2.1%
생 6
 
2.1%
Other values (71) 107
37.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 289
97.0%
Common 9
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 33
 
11.4%
스 31
 
10.7%
테 30
 
10.4%
니 30
 
10.4%
공 14
 
4.8%
원 12
 
4.2%
체 10
 
3.5%
육 10
 
3.5%
천 6
 
2.1%
생 6
 
2.1%
Other values (71) 107
37.0%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 289
97.0%
ASCII 9
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 33
 
11.4%
스 31
 
10.7%
테 30
 
10.4%
니 30
 
10.4%
공 14
 
4.8%
원 12
 
4.2%
체 10
 
3.5%
육 10
 
3.5%
천 6
 
2.1%
생 6
 
2.1%
Other values (71) 107
37.0%
ASCII
ValueCountFrequency (%)
9
100.0%

소재지
Categorical

Distinct10
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size396.0 B
음성군
6 
충주시
5 
제천시
5 
청주시
4 
옥천군
3 
Other values (5)
10 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
음성군 6
18.2%
충주시 5
15.2%
제천시 5
15.2%
청주시 4
12.1%
옥천군 3
9.1%
영동군 3
9.1%
증평군 2
 
6.1%
진천군 2
 
6.1%
단양군 2
 
6.1%
보은군 1
 
3.0%

Length

2023-12-12T10:29:27.779495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:29:27.921022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
음성군 6
18.2%
충주시 5
15.2%
제천시 5
15.2%
청주시 4
12.1%
옥천군 3
9.1%
영동군 3
9.1%
증평군 2
 
6.1%
진천군 2
 
6.1%
단양군 2
 
6.1%
보은군 1
 
3.0%

소재지도로명주소
Text

MISSING 

Distinct27
Distinct (%)87.1%
Missing2
Missing (%)6.1%
Memory size396.0 B
2023-12-12T10:29:28.173153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length17.741935
Min length1

Characters and Unicode

Total characters550
Distinct characters80
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

Unique24 ?
Unique (%)77.4%

Sample

1st row충청북도 청주시 흥덕구 대신로 157
2nd row충청북도 청주시 상당구 호미로 242
3rd row충청북도 청주시 청원구 오창읍 오창대로 197
4th row충청북도 청주시 흥덕구 강내면 석화사인길 13-51
5th row충청북도 충주시 낙수당2길 8
ValueCountFrequency (%)
충청북도 28
 
20.7%
음성군 5
 
3.7%
제천시 5
 
3.7%
충주시 4
 
3.0%
청주시 4
 
3.0%
옥천군 3
 
2.2%
3
 
2.2%
옥천읍 3
 
2.2%
영동군 3
 
2.2%
영동읍 3
 
2.2%
Other values (66) 74
54.8%
2023-12-12T10:29:28.583508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
104
18.9%
청 34
 
6.2%
충 32
 
5.8%
북 30
 
5.5%
도 29
 
5.3%
로 22
 
4.0%
1 17
 
3.1%
군 16
 
2.9%
읍 15
 
2.7%
천 15
 
2.7%
Other values (70) 236
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 363
66.0%
Space Separator 104
 
18.9%
Decimal Number 78
 
14.2%
Dash Punctuation 5
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 34
 
9.4%
충 32
 
8.8%
북 30
 
8.3%
도 29
 
8.0%
로 22
 
6.1%
군 16
 
4.4%
읍 15
 
4.1%
천 15
 
4.1%
시 14
 
3.9%
성 13
 
3.6%
Other values (58) 143
39.4%
Decimal Number
ValueCountFrequency (%)
1 17
21.8%
3 12
15.4%
4 9
11.5%
7 7
9.0%
5 7
9.0%
2 7
9.0%
0 7
9.0%
6 4
 
5.1%
8 4
 
5.1%
9 4
 
5.1%
Space Separator
ValueCountFrequency (%)
104
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 363
66.0%
Common 187
34.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 34
 
9.4%
충 32
 
8.8%
북 30
 
8.3%
도 29
 
8.0%
로 22
 
6.1%
군 16
 
4.4%
읍 15
 
4.1%
천 15
 
4.1%
시 14
 
3.9%
성 13
 
3.6%
Other values (58) 143
39.4%
Common
ValueCountFrequency (%)
104
55.6%
1 17
 
9.1%
3 12
 
6.4%
4 9
 
4.8%
7 7
 
3.7%
5 7
 
3.7%
2 7
 
3.7%
0 7
 
3.7%
- 5
 
2.7%
6 4
 
2.1%
Other values (2) 8
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 363
66.0%
ASCII 187
34.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
104
55.6%
1 17
 
9.1%
3 12
 
6.4%
4 9
 
4.8%
7 7
 
3.7%
5 7
 
3.7%
2 7
 
3.7%
0 7
 
3.7%
- 5
 
2.7%
6 4
 
2.1%
Other values (2) 8
 
4.3%
Hangul
ValueCountFrequency (%)
청 34
 
9.4%
충 32
 
8.8%
북 30
 
8.3%
도 29
 
8.0%
로 22
 
6.1%
군 16
 
4.4%
읍 15
 
4.1%
천 15
 
4.1%
시 14
 
3.9%
성 13
 
3.6%
Other values (58) 143
39.4%

소재지지번주소
Text

Distinct31
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T10:29:28.863018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length20.757576
Min length15

Characters and Unicode

Total characters685
Distinct characters86
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

Unique29 ?
Unique (%)87.9%

Sample

1st row충청북도 청주시 흥덕구 송정동 산140-61
2nd row충청북도 청주시 상당구 금천동 327
3rd row충청북도 청주시 청원구 오창읍 구룡리 375
4th row충청북도 청주시 흥덕구 강내면 탑연리 259-1
5th row충청북도 충주시 칠금동 509-79
ValueCountFrequency (%)
충청북도 32
 
19.6%
음성군 6
 
3.7%
제천시 5
 
3.1%
충주시 5
 
3.1%
청주시 4
 
2.5%
옥천읍 3
 
1.8%
옥천군 3
 
1.8%
영동군 3
 
1.8%
영동읍 3
 
1.8%
단양읍 2
 
1.2%
Other values (84) 97
59.5%
2023-12-12T10:29:29.303538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
131
19.1%
충 37
 
5.4%
청 37
 
5.4%
북 32
 
4.7%
도 32
 
4.7%
리 24
 
3.5%
1 23
 
3.4%
2 19
 
2.8%
읍 19
 
2.8%
군 18
 
2.6%
Other values (76) 313
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 421
61.5%
Space Separator 131
 
19.1%
Decimal Number 115
 
16.8%
Dash Punctuation 18
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
충 37
 
8.8%
청 37
 
8.8%
북 32
 
7.6%
도 32
 
7.6%
리 24
 
5.7%
읍 19
 
4.5%
군 18
 
4.3%
천 16
 
3.8%
동 15
 
3.6%
시 14
 
3.3%
Other values (64) 177
42.0%
Decimal Number
ValueCountFrequency (%)
1 23
20.0%
2 19
16.5%
9 13
11.3%
3 11
9.6%
4 10
8.7%
5 10
8.7%
0 10
8.7%
7 8
 
7.0%
8 6
 
5.2%
6 5
 
4.3%
Space Separator
ValueCountFrequency (%)
131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 421
61.5%
Common 264
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
충 37
 
8.8%
청 37
 
8.8%
북 32
 
7.6%
도 32
 
7.6%
리 24
 
5.7%
읍 19
 
4.5%
군 18
 
4.3%
천 16
 
3.8%
동 15
 
3.6%
시 14
 
3.3%
Other values (64) 177
42.0%
Common
ValueCountFrequency (%)
131
49.6%
1 23
 
8.7%
2 19
 
7.2%
- 18
 
6.8%
9 13
 
4.9%
3 11
 
4.2%
4 10
 
3.8%
5 10
 
3.8%
0 10
 
3.8%
7 8
 
3.0%
Other values (2) 11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 421
61.5%
ASCII 264
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131
49.6%
1 23
 
8.7%
2 19
 
7.2%
- 18
 
6.8%
9 13
 
4.9%
3 11
 
4.2%
4 10
 
3.8%
5 10
 
3.8%
0 10
 
3.8%
7 8
 
3.0%
Other values (2) 11
 
4.2%
Hangul
ValueCountFrequency (%)
충 37
 
8.8%
청 37
 
8.8%
북 32
 
7.6%
도 32
 
7.6%
리 24
 
5.7%
읍 19
 
4.5%
군 18
 
4.3%
천 16
 
3.8%
동 15
 
3.6%
시 14
 
3.3%
Other values (64) 177
42.0%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4593.6061
Minimum9
Maximum51454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T10:29:29.445468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile251.8
Q11295
median2412
Q33750
95-th percentile14319.8
Maximum51454
Range51445
Interquartile range (IQR)2455

Descriptive statistics

Standard deviation9091.4184
Coefficient of variation (CV)1.9791463
Kurtosis23.498754
Mean4593.6061
Median Absolute Deviation (MAD)1152
Skewness4.6402754
Sum151589
Variance82653889
MonotonicityNot monotonic
2023-12-12T10:29:29.587842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7038 1
 
3.0%
2740 1
 
3.0%
2998 1
 
3.0%
2192 1
 
3.0%
2800 1
 
3.0%
2435 1
 
3.0%
3237 1
 
3.0%
1260 1
 
3.0%
2553 1
 
3.0%
12121 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
9 1
3.0%
238 1
3.0%
261 1
3.0%
756 1
3.0%
1100 1
3.0%
1199 1
3.0%
1221 1
3.0%
1260 1
3.0%
1295 1
3.0%
1380 1
3.0%
ValueCountFrequency (%)
51454 1
3.0%
17618 1
3.0%
12121 1
3.0%
7038 1
3.0%
4800 1
3.0%
4520 1
3.0%
4290 1
3.0%
4267 1
3.0%
3750 1
3.0%
3300 1
3.0%

코트수(면)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)37.5%
Missing1
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean5.59375
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T10:29:29.736161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q36.25
95-th percentile14.7
Maximum22
Range21
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation4.6825646
Coefficient of variation (CV)0.83710652
Kurtosis4.8752764
Mean5.59375
Median Absolute Deviation (MAD)2
Skewness2.0796783
Sum179
Variance21.926411
MonotonicityNot monotonic
2023-12-12T10:29:29.849134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 9
27.3%
4 8
24.2%
6 5
15.2%
9 2
 
6.1%
8 1
 
3.0%
18 1
 
3.0%
3 1
 
3.0%
22 1
 
3.0%
1 1
 
3.0%
7 1
 
3.0%
Other values (2) 2
 
6.1%
ValueCountFrequency (%)
1 1
 
3.0%
2 9
27.3%
3 1
 
3.0%
4 8
24.2%
6 5
15.2%
7 1
 
3.0%
8 1
 
3.0%
9 2
 
6.1%
10 1
 
3.0%
12 1
 
3.0%
ValueCountFrequency (%)
22 1
 
3.0%
18 1
 
3.0%
12 1
 
3.0%
10 1
 
3.0%
9 2
 
6.1%
8 1
 
3.0%
7 1
 
3.0%
6 5
15.2%
4 8
24.2%
3 1
 
3.0%

수용인원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)43.5%
Missing10
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean369.82609
Minimum50
Maximum1350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T10:29:29.965465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q1100
median200
Q3300
95-th percentile1341
Maximum1350
Range1300
Interquartile range (IQR)200

Descriptive statistics

Standard deviation431.09666
Coefficient of variation (CV)1.165674
Kurtosis1.2206971
Mean369.82609
Median Absolute Deviation (MAD)100
Skewness1.6165828
Sum8506
Variance185844.33
MonotonicityNot monotonic
2023-12-12T10:29:30.139253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
300 5
15.2%
100 5
15.2%
50 3
 
9.1%
150 2
 
6.1%
1350 2
 
6.1%
200 2
 
6.1%
1000 1
 
3.0%
1260 1
 
3.0%
96 1
 
3.0%
600 1
 
3.0%
(Missing) 10
30.3%
ValueCountFrequency (%)
50 3
9.1%
96 1
 
3.0%
100 5
15.2%
150 2
 
6.1%
200 2
 
6.1%
300 5
15.2%
600 1
 
3.0%
1000 1
 
3.0%
1260 1
 
3.0%
1350 2
 
6.1%
ValueCountFrequency (%)
1350 2
 
6.1%
1260 1
 
3.0%
1000 1
 
3.0%
600 1
 
3.0%
300 5
15.2%
200 2
 
6.1%
150 2
 
6.1%
100 5
15.2%
96 1
 
3.0%
50 3
9.1%

준공년도
Real number (ℝ)

Distinct20
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.3939
Minimum1992
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T10:29:30.294296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1996.2
Q12005
median2010
Q32016
95-th percentile2018.8
Maximum2022
Range30
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3948436
Coefficient of variation (CV)0.0036801363
Kurtosis0.16647887
Mean2009.3939
Median Absolute Deviation (MAD)6
Skewness-0.63270499
Sum66310
Variance54.683712
MonotonicityNot monotonic
2023-12-12T10:29:30.419322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2012 4
 
12.1%
2010 4
 
12.1%
2016 3
 
9.1%
1992 2
 
6.1%
2017 2
 
6.1%
2008 2
 
6.1%
2011 2
 
6.1%
2018 2
 
6.1%
2013 1
 
3.0%
2004 1
 
3.0%
Other values (10) 10
30.3%
ValueCountFrequency (%)
1992 2
6.1%
1999 1
3.0%
2000 1
3.0%
2001 1
3.0%
2002 1
3.0%
2003 1
3.0%
2004 1
3.0%
2005 1
3.0%
2006 1
3.0%
2007 1
3.0%
ValueCountFrequency (%)
2022 1
 
3.0%
2020 1
 
3.0%
2018 2
6.1%
2017 2
6.1%
2016 3
9.1%
2013 1
 
3.0%
2012 4
12.1%
2011 2
6.1%
2010 4
12.1%
2008 2
6.1%

관리기관
Text

Distinct18
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T10:29:30.655126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length8
Min length3

Characters and Unicode

Total characters264
Distinct characters51
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

Unique10 ?
Unique (%)30.3%

Sample

1st row청주시소프트테니스협회
2nd row청주시시설관리공단
3rd row오창읍
4th row강내면
5th row충주테니스협회
ValueCountFrequency (%)
시설관리사업소 6
14.6%
체육진흥과 3
 
7.3%
영동군시설사업소 3
 
7.3%
충주시 3
 
7.3%
제천시시설관리사업소 3
 
7.3%
옥천군체육사업소 3
 
7.3%
단양군청 2
 
4.9%
진천군 2
 
4.9%
체육진흥지원단 2
 
4.9%
증평군 2
 
4.9%
Other values (11) 12
29.3%
2023-12-12T10:29:31.043860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 25
 
9.5%
업 16
 
6.1%
소 16
 
6.1%
사 15
 
5.7%
설 14
 
5.3%
군 13
 
4.9%
관 11
 
4.2%
리 11
 
4.2%
천 10
 
3.8%
체 10
 
3.8%
Other values (41) 123
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 255
96.6%
Space Separator 9
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 25
 
9.8%
업 16
 
6.3%
소 16
 
6.3%
사 15
 
5.9%
설 14
 
5.5%
군 13
 
5.1%
관 11
 
4.3%
리 11
 
4.3%
천 10
 
3.9%
체 10
 
3.9%
Other values (40) 114
44.7%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 255
96.6%
Common 9
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 25
 
9.8%
업 16
 
6.3%
소 16
 
6.3%
사 15
 
5.9%
설 14
 
5.5%
군 13
 
5.1%
관 11
 
4.3%
리 11
 
4.3%
천 10
 
3.9%
체 10
 
3.9%
Other values (40) 114
44.7%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 255
96.6%
ASCII 9
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 25
 
9.8%
업 16
 
6.3%
소 16
 
6.3%
사 15
 
5.9%
설 14
 
5.5%
군 13
 
5.1%
관 11
 
4.3%
리 11
 
4.3%
천 10
 
3.9%
체 10
 
3.9%
Other values (40) 114
44.7%
ASCII
ValueCountFrequency (%)
9
100.0%

전화번호
Text

Distinct22
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T10:29:31.371163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters396
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 (%)45.5%

Sample

1st row043-271-9189
2nd row043-270-8513
3rd row043-201-8621
4th row043-201-7575
5th row043-850-3905
ValueCountFrequency (%)
043-740-5995 3
 
9.1%
043-641-5592 3
 
9.1%
043-730-4883 3
 
9.1%
043-850-3905 3
 
9.1%
043-641-5597 2
 
6.1%
043-835-4923 2
 
6.1%
043-420-3105 2
 
6.1%
043-270-8513 1
 
3.0%
043-870-7944 1
 
3.0%
043-850-3903 1
 
3.0%
Other values (12) 12
36.4%
2023-12-12T10:29:31.861499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 66
16.7%
0 57
14.4%
3 54
13.6%
4 53
13.4%
5 40
10.1%
9 31
7.8%
1 26
 
6.6%
7 24
 
6.1%
8 22
 
5.6%
2 15
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
83.3%
Dash Punctuation 66
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57
17.3%
3 54
16.4%
4 53
16.1%
5 40
12.1%
9 31
9.4%
1 26
7.9%
7 24
7.3%
8 22
 
6.7%
2 15
 
4.5%
6 8
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 66
16.7%
0 57
14.4%
3 54
13.6%
4 53
13.4%
5 40
10.1%
9 31
7.8%
1 26
 
6.6%
7 24
 
6.1%
8 22
 
5.6%
2 15
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 66
16.7%
0 57
14.4%
3 54
13.6%
4 53
13.4%
5 40
10.1%
9 31
7.8%
1 26
 
6.6%
7 24
 
6.1%
8 22
 
5.6%
2 15
 
3.8%

Interactions

2023-12-12T10:29:26.125661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:24.662423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:25.137694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:25.602427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:26.212957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:24.785189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:25.268400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:25.750208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:26.589880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:24.892743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:25.374884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:25.880908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:26.695612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:25.020323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:25.501335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:29:26.009552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:29:32.002434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명소재지소재지도로명주소소재지지번주소면적(제곱미터)코트수(면)수용인원준공년도관리기관전화번호
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0000.9711.0000.0000.3600.6140.0001.0001.000
소재지도로명주소1.0000.9711.0001.0001.0000.9500.0000.8150.9920.948
소재지지번주소1.0001.0001.0001.0001.0000.9420.0000.8381.0000.968
면적(제곱미터)1.0000.0001.0001.0001.0000.9860.6010.3281.0000.478
코트수(면)1.0000.3600.9500.9420.9861.0000.7530.0000.9080.406
수용인원1.0000.6140.0000.0000.6010.7531.0000.0000.8350.982
준공년도1.0000.0000.8150.8380.3280.0000.0001.0000.0000.000
관리기관1.0001.0000.9921.0001.0000.9080.8350.0001.0000.969
전화번호1.0001.0000.9480.9680.4780.4060.9820.0000.9691.000
2023-12-12T10:29:32.177604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)코트수(면)수용인원준공년도소재지
면적(제곱미터)1.0000.5340.254-0.0950.000
코트수(면)0.5341.0000.761-0.1940.136
수용인원0.2540.7611.0000.0840.342
준공년도-0.095-0.1940.0841.0000.000
소재지0.0000.1360.3420.0001.000

Missing values

2023-12-12T10:29:26.811967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:29:26.975953image/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.
2023-12-12T10:29:27.095131image/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(제곱미터)코트수(면)수용인원준공년도관리기관전화번호
0청주정구장청주시충청북도 청주시 흥덕구 대신로 157충청북도 청주시 흥덕구 송정동 산140-61703883002012청주시소프트테니스협회043-271-9189
1국제테니스장청주시충청북도 청주시 상당구 호미로 242충청북도 청주시 상당구 금천동 327121211810002001청주시시설관리공단043-270-8513
2공설테니스장청주시충청북도 청주시 청원구 오창읍 오창대로 197충청북도 청주시 청원구 오창읍 구룡리 37545209<NA>2006오창읍043-201-8621
3강내생활체육공원테니스장청주시충청북도 청주시 흥덕구 강내면 석화사인길 13-51충청북도 청주시 흥덕구 강내면 탑연리 259-117333<NA>2012강내면043-201-7575
4탄금테니스장충주시충청북도 충주시 낙수당2길 8충청북도 충주시 칠금동 509-7917618223002010충주테니스협회043-850-3905
5장애인론볼및테니스장충주시충청북도 충주시 창현로 1400충청북도 충주시 용관동 54-2429023002010충주시043-850-3903
6클린에너지파크충주시충청북도 충주시 성종두담길 21충청북도 충주시 대소원면 두정리 222-82611<NA>2010충주시시설관리공단043-870-7944
7서충주생활체육공원 테니스장충주시충청북도 충주시 주덕읍 기업도시3로150충청북도 충주시 주덕읍 화곡리 11917562<NA>2018충주시043-850-3905
8충주 북부생활체육공원 테니스장충주시<NA>충청북도 충주시 엄정면 미내리 산7-151454<NA><NA>2022충주시 체육진흥과043-850-3905
9제천소프트테니스장제천시충청북도 제천시 숭의로 85충청북도 제천시 화산동 128-1182573001992제천시정구협회043-641-5597
시설명소재지소재지도로명주소소재지지번주소면적(제곱미터)코트수(면)수용인원준공년도관리기관전화번호
23진전종합스포츠타운 테니스장진천군충청북도 진천군 진천읍 원덕로 166충청북도 진천군 진천읍 신정리 산38-1323793002020진천군 체육진흥지원단043-539-7694
24광혜원 테니스장진천군<NA>광혜원면 광혜원리 669-812602<NA>2018진천군 체육진흥지원단043-539-7692
25음성테니스장음성군충청북도 음성군 음성읍 설성로 47충청북도 음성군 음성읍 읍내리 470274041002000시설관리사업소043-871-5911
26금왕체육공원테니스장음성군충청북도 음성군 금왕읍 무극로 370충청북도 음성군 금왕읍 금석리 98255341002008시설관리사업소043-871-5912
27전천후정구테니스장음성군충청북도 음성군 음성읍 설성로 47충청북도 음성군 음성읍 읍내리 470426766002010시설관리사업소043-871-5910
28삼성생활체육공원테니스장음성군충청북도 음성군 삼성면 금일로 992-34충청북도 음성군 삼성면 덕정리 393-2122121002011시설관리사업소043-871-5917
29대소생활체육공원 테니스장음성군충청북도 음성군 대소면 대성로 43충청북도 음성군 대소면 태생리 39015122<NA>2016시설관리사업소043-871-5915
30감곡생활체육공원 테니스장음성군-충청북도 음성군 감곡면 오향리 32016322<NA>2017시설관리사업소043-871-5919
31공설정구장단양군충청북도 단양군 단양읍 삼봉로 376충청북도 단양군 단양읍 별곡리 20480062002002단양군청 문화체육과043-420-3105
32단양테니스장단양군충청북도 단양군 단양읍 상진로 31충청북도 단양군 단양읍 상진리 산19-22164342002016단양군청 문화체육과043-420-3105