Overview

Dataset statistics

Number of variables12
Number of observations47
Missing cells72
Missing cells (%)12.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory102.7 B

Variable types

Numeric3
Categorical3
Text4
Boolean1
DateTime1

Dataset

Description전북특별자치도 유원시설업 분류별 현황(시군명, 시설명, 도로명주소, 대표자, 전화번호, 검사기구수, 비검기구수, 자료출처, 공개여부 등)
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055598/fileData.do

Alerts

자료출처 has constant value ""Constant
공개여부 has constant value ""Constant
작성일 has constant value ""Constant
갱신주기(년) has constant value ""Constant
검사기구수 is highly overall correlated with 비검기구수High correlation
비검기구수 is highly overall correlated with 검사기구수High correlation
전화번호 has 31 (66.0%) missing valuesMissing
검사기구수 has 30 (63.8%) missing valuesMissing
비검기구수 has 11 (23.4%) missing valuesMissing
순번 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 10:29:27.254690
Analysis finished2024-03-14 10:29:31.095069
Duration3.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size551.0 B
2024-03-14T19:29:31.313607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3
Q112.5
median24
Q335.5
95-th percentile44.7
Maximum47
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.711309
Coefficient of variation (CV)0.57130455
Kurtosis-1.2
Mean24
Median Absolute Deviation (MAD)12
Skewness0
Sum1128
Variance188
MonotonicityStrictly increasing
2024-03-14T19:29:31.739336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 1
 
2.1%
2 1
 
2.1%
27 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%
38 1
2.1%

시군명
Categorical

Distinct10
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size504.0 B
전주시
19 
무주군
6 
군산시
4 
익산시
4 
김제시
3 
Other values (5)
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row전주시
2nd row전주시
3rd row전주시
4th row군산시
5th row군산시

Common Values

ValueCountFrequency (%)
전주시 19
40.4%
무주군 6
 
12.8%
군산시 4
 
8.5%
익산시 4
 
8.5%
김제시 3
 
6.4%
부안군 3
 
6.4%
정읍시 3
 
6.4%
남원시 2
 
4.3%
고창군 2
 
4.3%
임실군 1
 
2.1%

Length

2024-03-14T19:29:32.233237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:29:32.592712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전주시 19
40.4%
무주군 6
 
12.8%
군산시 4
 
8.5%
익산시 4
 
8.5%
김제시 3
 
6.4%
부안군 3
 
6.4%
정읍시 3
 
6.4%
남원시 2
 
4.3%
고창군 2
 
4.3%
임실군 1
 
2.1%

시설명
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size504.0 B
2024-03-14T19:29:33.588605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.5319149
Min length4

Characters and Unicode

Total characters307
Distinct characters144
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)100.0%

Sample

1st row전주드림랜드
2nd row전주한옥레일바이크
3rd row전주노리존
4th row금강랜드
5th row군산야외수영장
ValueCountFrequency (%)
전주드림랜드 1
 
1.7%
야구 1
 
1.7%
쁘띠키즈 1
 
1.7%
키즈레이싱파크 1
 
1.7%
점핑스쿨 1
 
1.7%
헬로방방 1
 
1.7%
대박뽑기 1
 
1.7%
화인방방 1
 
1.7%
방방월드 1
 
1.7%
애플트리 1
 
1.7%
Other values (49) 49
83.1%
2024-03-14T19:29:34.985055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
드 12
 
3.9%
12
 
3.9%
방 12
 
3.9%
랜 9
 
2.9%
이 8
 
2.6%
스 7
 
2.3%
즈 7
 
2.3%
키 7
 
2.3%
팡 6
 
2.0%
아 6
 
2.0%
Other values (134) 221
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 293
95.4%
Space Separator 12
 
3.9%
Other Symbol 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
드 12
 
4.1%
방 12
 
4.1%
랜 9
 
3.1%
이 8
 
2.7%
스 7
 
2.4%
즈 7
 
2.4%
키 7
 
2.4%
팡 6
 
2.0%
아 6
 
2.0%
점 6
 
2.0%
Other values (132) 213
72.7%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 295
96.1%
Common 12
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
드 12
 
4.1%
방 12
 
4.1%
랜 9
 
3.1%
이 8
 
2.7%
스 7
 
2.4%
즈 7
 
2.4%
키 7
 
2.4%
팡 6
 
2.0%
아 6
 
2.0%
점 6
 
2.0%
Other values (133) 215
72.9%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 293
95.4%
ASCII 12
 
3.9%
None 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
드 12
 
4.1%
방 12
 
4.1%
랜 9
 
3.1%
이 8
 
2.7%
스 7
 
2.4%
즈 7
 
2.4%
키 7
 
2.4%
팡 6
 
2.0%
아 6
 
2.0%
점 6
 
2.0%
Other values (132) 213
72.7%
ASCII
ValueCountFrequency (%)
12
100.0%
None
ValueCountFrequency (%)
㈜ 2
100.0%

도로명주소
Text

Distinct44
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size504.0 B
2024-03-14T19:29:36.037805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length15.425532
Min length10

Characters and Unicode

Total characters725
Distinct characters109
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)91.5%

Sample

1st row전주시 덕진구 소리로 104
2nd row전주시 덕진구 동부대로 420
3rd row전주시 완산구 객사3길 34
4th row군산시 성산면 철새로 53
5th row군산시 해망로 546-10
ValueCountFrequency (%)
전주시 19
 
10.1%
완산구 17
 
9.0%
무주군 6
 
3.2%
설천면 5
 
2.7%
익산시 4
 
2.1%
군산시 4
 
2.1%
2층 4
 
2.1%
185 4
 
2.1%
만선로 4
 
2.1%
부안군 3
 
1.6%
Other values (103) 118
62.8%
2024-03-14T19:29:37.317091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
141
19.4%
산 36
 
5.0%
로 35
 
4.8%
시 35
 
4.8%
3 29
 
4.0%
1 29
 
4.0%
주 27
 
3.7%
전 21
 
2.9%
구 20
 
2.8%
완 17
 
2.3%
Other values (99) 335
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 424
58.5%
Decimal Number 147
 
20.3%
Space Separator 141
 
19.4%
Other Punctuation 8
 
1.1%
Dash Punctuation 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 36
 
8.5%
로 35
 
8.3%
시 35
 
8.3%
주 27
 
6.4%
전 21
 
5.0%
구 20
 
4.7%
완 17
 
4.0%
군 16
 
3.8%
면 12
 
2.8%
길 11
 
2.6%
Other values (85) 194
45.8%
Decimal Number
ValueCountFrequency (%)
3 29
19.7%
1 29
19.7%
8 17
11.6%
2 15
10.2%
5 14
9.5%
4 13
8.8%
7 9
 
6.1%
6 8
 
5.4%
0 8
 
5.4%
9 5
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
. 1
 
12.5%
Space Separator
ValueCountFrequency (%)
141
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 424
58.5%
Common 301
41.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 36
 
8.5%
로 35
 
8.3%
시 35
 
8.3%
주 27
 
6.4%
전 21
 
5.0%
구 20
 
4.7%
완 17
 
4.0%
군 16
 
3.8%
면 12
 
2.8%
길 11
 
2.6%
Other values (85) 194
45.8%
Common
ValueCountFrequency (%)
141
46.8%
3 29
 
9.6%
1 29
 
9.6%
8 17
 
5.6%
2 15
 
5.0%
5 14
 
4.7%
4 13
 
4.3%
7 9
 
3.0%
6 8
 
2.7%
0 8
 
2.7%
Other values (4) 18
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 424
58.5%
ASCII 301
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141
46.8%
3 29
 
9.6%
1 29
 
9.6%
8 17
 
5.6%
2 15
 
5.0%
5 14
 
4.7%
4 13
 
4.3%
7 9
 
3.0%
6 8
 
2.7%
0 8
 
2.7%
Other values (4) 18
 
6.0%
Hangul
ValueCountFrequency (%)
산 36
 
8.5%
로 35
 
8.3%
시 35
 
8.3%
주 27
 
6.4%
전 21
 
5.0%
구 20
 
4.7%
완 17
 
4.0%
군 16
 
3.8%
면 12
 
2.8%
길 11
 
2.6%
Other values (85) 194
45.8%

대표자
Text

Distinct45
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size504.0 B
2024-03-14T19:29:38.122533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1914894
Min length2

Characters and Unicode

Total characters150
Distinct characters65
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)91.5%

Sample

1st row김*권
2nd row권*현,권*기
3rd row시*진
4th row이*국
5th row군산시장
ValueCountFrequency (%)
전*식 2
 
4.3%
최*이 2
 
4.3%
임*구 1
 
2.1%
박*자 1
 
2.1%
이*희 1
 
2.1%
김*경 1
 
2.1%
송*옥 1
 
2.1%
조*정 1
 
2.1%
은 1
 
2.1%
김*용 1
 
2.1%
Other values (35) 35
74.5%
2024-03-14T19:29:39.320079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 43
28.7%
이 9
 
6.0%
김 7
 
4.7%
안 3
 
2.0%
식 3
 
2.0%
최 3
 
2.0%
정 3
 
2.0%
시 3
 
2.0%
군 3
 
2.0%
권 3
 
2.0%
Other values (55) 70
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105
70.0%
Other Punctuation 44
29.3%
Other Symbol 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 9
 
8.6%
김 7
 
6.7%
안 3
 
2.9%
식 3
 
2.9%
최 3
 
2.9%
정 3
 
2.9%
시 3
 
2.9%
군 3
 
2.9%
권 3
 
2.9%
장 3
 
2.9%
Other values (52) 65
61.9%
Other Punctuation
ValueCountFrequency (%)
* 43
97.7%
, 1
 
2.3%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106
70.7%
Common 44
29.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 9
 
8.5%
김 7
 
6.6%
안 3
 
2.8%
식 3
 
2.8%
최 3
 
2.8%
정 3
 
2.8%
시 3
 
2.8%
군 3
 
2.8%
권 3
 
2.8%
장 3
 
2.8%
Other values (53) 66
62.3%
Common
ValueCountFrequency (%)
* 43
97.7%
, 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 105
70.0%
ASCII 44
29.3%
None 1
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 43
97.7%
, 1
 
2.3%
Hangul
ValueCountFrequency (%)
이 9
 
8.6%
김 7
 
6.7%
안 3
 
2.9%
식 3
 
2.9%
최 3
 
2.9%
정 3
 
2.9%
시 3
 
2.9%
군 3
 
2.9%
권 3
 
2.9%
장 3
 
2.9%
Other values (52) 65
61.9%
None
ValueCountFrequency (%)
㈜ 1
100.0%

전화번호
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing31
Missing (%)66.0%
Memory size504.0 B
2024-03-14T19:29:39.954832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.875
Min length9

Characters and Unicode

Total characters190
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

Unique16 ?
Unique (%)100.0%

Sample

1st row063-275-4900
2nd row063-453-1525
3rd row063-632-6070
4th row063-548-4401
5th row1800-5266
ValueCountFrequency (%)
063-322-7752 1
 
6.2%
063-453-1525 1
 
6.2%
063-632-6070 1
 
6.2%
063-548-4401 1
 
6.2%
1800-5266 1
 
6.2%
063-320-9000 1
 
6.2%
063-322-0702 1
 
6.2%
063-560-7500 1
 
6.2%
063-275-4900 1
 
6.2%
063-580-8702 1
 
6.2%
Other values (6) 6
37.5%
2024-03-14T19:29:41.145070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 48
25.3%
- 31
16.3%
6 23
12.1%
3 22
11.6%
2 17
 
8.9%
7 13
 
6.8%
5 13
 
6.8%
4 9
 
4.7%
8 6
 
3.2%
9 5
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159
83.7%
Dash Punctuation 31
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48
30.2%
6 23
14.5%
3 22
13.8%
2 17
 
10.7%
7 13
 
8.2%
5 13
 
8.2%
4 9
 
5.7%
8 6
 
3.8%
9 5
 
3.1%
1 3
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48
25.3%
- 31
16.3%
6 23
12.1%
3 22
11.6%
2 17
 
8.9%
7 13
 
6.8%
5 13
 
6.8%
4 9
 
4.7%
8 6
 
3.2%
9 5
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48
25.3%
- 31
16.3%
6 23
12.1%
3 22
11.6%
2 17
 
8.9%
7 13
 
6.8%
5 13
 
6.8%
4 9
 
4.7%
8 6
 
3.2%
9 5
 
2.6%

검사기구수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)58.8%
Missing30
Missing (%)63.8%
Infinite0
Infinite (%)0.0%
Mean7.8235294
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size551.0 B
2024-03-14T19:29:41.502443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile24.8
Maximum60
Range59
Interquartile range (IQR)7

Descriptive statistics

Standard deviation14.112208
Coefficient of variation (CV)1.803816
Kurtosis13.404252
Mean7.8235294
Median Absolute Deviation (MAD)2
Skewness3.5364906
Sum133
Variance199.15441
MonotonicityNot monotonic
2024-03-14T19:29:41.855901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 5
 
10.6%
3 4
 
8.5%
11 1
 
2.1%
60 1
 
2.1%
16 1
 
2.1%
10 1
 
2.1%
8 1
 
2.1%
2 1
 
2.1%
4 1
 
2.1%
5 1
 
2.1%
(Missing) 30
63.8%
ValueCountFrequency (%)
1 5
10.6%
2 1
 
2.1%
3 4
8.5%
4 1
 
2.1%
5 1
 
2.1%
8 1
 
2.1%
10 1
 
2.1%
11 1
 
2.1%
16 1
 
2.1%
60 1
 
2.1%
ValueCountFrequency (%)
60 1
 
2.1%
16 1
 
2.1%
11 1
 
2.1%
10 1
 
2.1%
8 1
 
2.1%
5 1
 
2.1%
4 1
 
2.1%
3 4
8.5%
2 1
 
2.1%
1 5
10.6%

비검기구수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)30.6%
Missing11
Missing (%)23.4%
Infinite0
Infinite (%)0.0%
Mean6
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size551.0 B
2024-03-14T19:29:42.210231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q35
95-th percentile20
Maximum48
Range47
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.8561076
Coefficient of variation (CV)1.6426846
Kurtosis11.73919
Mean6
Median Absolute Deviation (MAD)1
Skewness3.3616168
Sum216
Variance97.142857
MonotonicityNot monotonic
2024-03-14T19:29:42.579044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 12
25.5%
1 7
14.9%
3 4
 
8.5%
5 3
 
6.4%
4 3
 
6.4%
12 2
 
4.3%
38 1
 
2.1%
13 1
 
2.1%
14 1
 
2.1%
9 1
 
2.1%
(Missing) 11
23.4%
ValueCountFrequency (%)
1 7
14.9%
2 12
25.5%
3 4
 
8.5%
4 3
 
6.4%
5 3
 
6.4%
9 1
 
2.1%
12 2
 
4.3%
13 1
 
2.1%
14 1
 
2.1%
38 1
 
2.1%
ValueCountFrequency (%)
48 1
 
2.1%
38 1
 
2.1%
14 1
 
2.1%
13 1
 
2.1%
12 2
 
4.3%
9 1
 
2.1%
5 3
 
6.4%
4 3
 
6.4%
3 4
 
8.5%
2 12
25.5%

자료출처
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size504.0 B
관광총괄과
47 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관광총괄과 47
100.0%

Length

2024-03-14T19:29:42.964448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:29:43.247121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광총괄과 47
100.0%

공개여부
Boolean

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size175.0 B
True
47 
ValueCountFrequency (%)
True 47
100.0%
2024-03-14T19:29:43.478738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

작성일
Date

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size504.0 B
Minimum2016-12-31 00:00:00
Maximum2016-12-31 00:00:00
2024-03-14T19:29:43.725978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:44.017508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

갱신주기(년)
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size504.0 B
1
47 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 47
100.0%

Length

2024-03-14T19:29:44.364941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:29:44.647238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 47
100.0%

Interactions

2024-03-14T19:29:29.209911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:27.946439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:28.477130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:29.441146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:28.173612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:28.704644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:29.697543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:28.343515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:29:28.953941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:29:44.826342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명시설명도로명주소대표자전화번호검사기구수비검기구수
순번1.0000.8571.0000.8800.9681.0000.1000.294
시군명0.8571.0001.0001.0000.8461.0000.0000.000
시설명1.0001.0001.0001.0001.0001.0001.0001.000
도로명주소0.8801.0001.0001.0000.9641.0001.0001.000
대표자0.9680.8461.0000.9641.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.000
검사기구수0.1000.0001.0001.0001.0001.0001.0001.000
비검기구수0.2940.0001.0001.0001.0001.0001.0001.000
2024-03-14T19:29:45.113266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번검사기구수비검기구수시군명
순번1.000-0.465-0.3160.426
검사기구수-0.4651.0000.8970.000
비검기구수-0.3160.8971.0000.000
시군명0.4260.0000.0001.000

Missing values

2024-03-14T19:29:30.054644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:29:30.581948image/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.
2024-03-14T19:29:30.940330image/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

순번시군명시설명도로명주소대표자전화번호검사기구수비검기구수자료출처공개여부작성일갱신주기(년)
01전주시전주드림랜드전주시 덕진구 소리로 104김*권063-275-4900113관광총괄과Y2016-12-311
12전주시전주한옥레일바이크전주시 덕진구 동부대로 420권*현,권*기<NA>60<NA>관광총괄과Y2016-12-311
23전주시전주노리존전주시 완산구 객사3길 34시*진<NA>1<NA>관광총괄과Y2016-12-311
34군산시금강랜드군산시 성산면 철새로 53이*국063-453-15251638관광총괄과Y2016-12-311
45군산시군산야외수영장군산시 해망로 546-10군산시장<NA>3<NA>관광총괄과Y2016-12-311
56익산시익산놀이터 디스코팡팡익산시 무왕로 9길 3윤*순<NA>1<NA>관광총괄과Y2016-12-311
67남원시남원랜드남원시 양림길 58-13오*태063-632-6070105관광총괄과Y2016-12-311
78김제시모악랜드김제시 금산면 모악로 476-39최*환063-548-44018<NA>관광총괄과Y2016-12-311
89김제시오투아일랜드김제시 승암길 13양*두1800-52662<NA>관광총괄과Y2016-12-311
910무주군무주덕유산리조트무주군 설천면 만선로 185이*근063-320-90003<NA>관광총괄과Y2016-12-311
순번시군명시설명도로명주소대표자전화번호검사기구수비검기구수자료출처공개여부작성일갱신주기(년)
3738익산시성당포구전통테마마을익산시 성당면 성당로 762안*일<NA><NA>1관광총괄과Y2016-12-311
3839정읍시진영 야구 연습장정읍시 수성로 33김*식<NA><NA>12관광총괄과Y2016-12-311
3940정읍시점프노리스포츠정읍시 샘골로 85, 2층최*이<NA><NA>2관광총괄과Y2016-12-311
4041남원시이벤트 대성렐탈남원시 운봉읍 용산리 718이*숙<NA><NA>1관광총괄과Y2016-12-311
4142김제시하우스방방김제시 금성8길 17박*규063-547-4063<NA>5관광총괄과Y2016-12-311
4243무주군유로번지점프무주군 설천면 만선로 185김*종<NA><NA>1관광총괄과Y2016-12-311
4344무주군미니기타무주군 설천면 만선로 185임*구<NA><NA>1관광총괄과Y2016-12-311
4445무주군어리다 함께 슬라이딩무주군 설천면 소천리 1268㈜문화나들이<NA><NA>1관광총괄과Y2016-12-311
4546임실군임실 버블방방임실군 임실읍 운수로 19이*희070-4799-0007<NA>2관광총괄과Y2016-12-311
4647부안군줄포만갯벌생태공원 해수풀장부안군 줄포면 생태공원로 38부안군수<NA><NA>2관광총괄과Y2016-12-311