Overview

Dataset statistics

Number of variables8
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory71.0 B

Variable types

Text6
Numeric1
Categorical1

Alerts

주소 has unique valuesUnique
전화 has unique valuesUnique

Reproduction

Analysis started2024-03-14 02:25:08.125030
Analysis finished2024-03-14 02:25:08.714361
Duration0.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:25:08.808402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.6363636
Min length1

Characters and Unicode

Total characters58
Distinct characters33
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

Unique13 ?
Unique (%)59.1%

Sample

1st row계
2nd row-
3rd row전주
4th row전주
5th row전주
ValueCountFrequency (%)
전주 3
 
11.5%
익산 2
 
7.7%
청소년 2
 
7.7%
자활 2
 
7.7%
지원관 2
 
7.7%
군산 2
 
7.7%
임실 1
 
3.8%
무주 1
 
3.8%
부안 1
 
3.8%
고창 1
 
3.8%
Other values (9) 9
34.6%
2024-03-14T11:25:09.076781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 5
 
8.6%
산 4
 
6.9%
4
 
6.9%
전 3
 
5.2%
원 3
 
5.2%
자 2
 
3.4%
창 2
 
3.4%
관 2
 
3.4%
활 2
 
3.4%
지 2
 
3.4%
Other values (23) 29
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53
91.4%
Space Separator 4
 
6.9%
Dash Punctuation 1
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 5
 
9.4%
산 4
 
7.5%
전 3
 
5.7%
원 3
 
5.7%
자 2
 
3.8%
창 2
 
3.8%
관 2
 
3.8%
활 2
 
3.8%
지 2
 
3.8%
년 2
 
3.8%
Other values (21) 26
49.1%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53
91.4%
Common 5
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 5
 
9.4%
산 4
 
7.5%
전 3
 
5.7%
원 3
 
5.7%
자 2
 
3.8%
창 2
 
3.8%
관 2
 
3.8%
활 2
 
3.8%
지 2
 
3.8%
년 2
 
3.8%
Other values (21) 26
49.1%
Common
ValueCountFrequency (%)
4
80.0%
- 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53
91.4%
ASCII 5
 
8.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 5
 
9.4%
산 4
 
7.5%
전 3
 
5.7%
원 3
 
5.7%
자 2
 
3.8%
창 2
 
3.8%
관 2
 
3.8%
활 2
 
3.8%
지 2
 
3.8%
년 2
 
3.8%
Other values (21) 26
49.1%
ASCII
ValueCountFrequency (%)
4
80.0%
- 1
 
20.0%

센터명
Text

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:25:09.218907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.8181818
Min length2

Characters and Unicode

Total characters62
Distinct characters37
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

Unique18 ?
Unique (%)81.8%

Sample

1st row19개소
2nd row전북 광역
3rd row전주
4th row전주 덕진
5th row전주 생명
ValueCountFrequency (%)
전주 4
 
14.8%
군산 2
 
7.4%
익산 2
 
7.4%
정읍 2
 
7.4%
진안 1
 
3.7%
19개소 1
 
3.7%
김제 1
 
3.7%
고창 1
 
3.7%
순창 1
 
3.7%
임실 1
 
3.7%
Other values (11) 11
40.7%
2024-03-14T11:25:09.464663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 6
 
9.7%
전 5
 
8.1%
5
 
8.1%
산 4
 
6.5%
광 2
 
3.2%
정 2
 
3.2%
읍 2
 
3.2%
진 2
 
3.2%
안 2
 
3.2%
군 2
 
3.2%
Other values (27) 30
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55
88.7%
Space Separator 5
 
8.1%
Decimal Number 2
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 6
 
10.9%
전 5
 
9.1%
산 4
 
7.3%
광 2
 
3.6%
정 2
 
3.6%
읍 2
 
3.6%
진 2
 
3.6%
안 2
 
3.6%
군 2
 
3.6%
창 2
 
3.6%
Other values (24) 26
47.3%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
9 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55
88.7%
Common 7
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 6
 
10.9%
전 5
 
9.1%
산 4
 
7.3%
광 2
 
3.6%
정 2
 
3.6%
읍 2
 
3.6%
진 2
 
3.6%
안 2
 
3.6%
군 2
 
3.6%
창 2
 
3.6%
Other values (24) 26
47.3%
Common
ValueCountFrequency (%)
5
71.4%
1 1
 
14.3%
9 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55
88.7%
ASCII 7
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 6
 
10.9%
전 5
 
9.1%
산 4
 
7.3%
광 2
 
3.6%
정 2
 
3.6%
읍 2
 
3.6%
진 2
 
3.6%
안 2
 
3.6%
군 2
 
3.6%
창 2
 
3.6%
Other values (24) 26
47.3%
ASCII
ValueCountFrequency (%)
5
71.4%
1 1
 
14.3%
9 1
 
14.3%

센터장
Text

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:25:09.615626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8636364
Min length1

Characters and Unicode

Total characters63
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

Unique18 ?
Unique (%)81.8%

Sample

1st row-
2nd row공석
3rd row허종현
4th row박준홍
5th row조용희
ValueCountFrequency (%)
허종현 2
 
9.1%
김복례 2
 
9.1%
1
 
4.5%
이승재 1
 
4.5%
제춘홍 1
 
4.5%
한승연 1
 
4.5%
김종수 1
 
4.5%
서정일 1
 
4.5%
오용식 1
 
4.5%
최우영 1
 
4.5%
Other values (10) 10
45.5%
2024-03-14T11:25:09.891958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 5
 
7.9%
진 3
 
4.8%
종 3
 
4.8%
허 2
 
3.2%
현 2
 
3.2%
복 2
 
3.2%
례 2
 
3.2%
승 2
 
3.2%
홍 2
 
3.2%
최 2
 
3.2%
Other values (37) 38
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 62
98.4%
Dash Punctuation 1
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 5
 
8.1%
진 3
 
4.8%
종 3
 
4.8%
허 2
 
3.2%
현 2
 
3.2%
복 2
 
3.2%
례 2
 
3.2%
승 2
 
3.2%
홍 2
 
3.2%
최 2
 
3.2%
Other values (36) 37
59.7%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 62
98.4%
Common 1
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 5
 
8.1%
진 3
 
4.8%
종 3
 
4.8%
허 2
 
3.2%
현 2
 
3.2%
복 2
 
3.2%
례 2
 
3.2%
승 2
 
3.2%
홍 2
 
3.2%
최 2
 
3.2%
Other values (36) 37
59.7%
Common
ValueCountFrequency (%)
- 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 62
98.4%
ASCII 1
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 5
 
8.1%
진 3
 
4.8%
종 3
 
4.8%
허 2
 
3.2%
현 2
 
3.2%
복 2
 
3.2%
례 2
 
3.2%
승 2
 
3.2%
홍 2
 
3.2%
최 2
 
3.2%
Other values (36) 37
59.7%
ASCII
ValueCountFrequency (%)
- 1
100.0%

현원
Real number (ℝ)

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.545455
Minimum2
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-14T11:25:09.983148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q15
median5.5
Q36
95-th percentile10.8
Maximum116
Range114
Interquartile range (IQR)1

Descriptive statistics

Standard deviation23.621474
Coefficient of variation (CV)2.2399673
Kurtosis21.710383
Mean10.545455
Median Absolute Deviation (MAD)0.5
Skewness4.6464028
Sum232
Variance557.97403
MonotonicityNot monotonic
2024-03-14T11:25:10.069686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 8
36.4%
6 6
27.3%
7 3
 
13.6%
2 2
 
9.1%
116 1
 
4.5%
11 1
 
4.5%
4 1
 
4.5%
ValueCountFrequency (%)
2 2
 
9.1%
4 1
 
4.5%
5 8
36.4%
6 6
27.3%
7 3
 
13.6%
11 1
 
4.5%
116 1
 
4.5%
ValueCountFrequency (%)
116 1
 
4.5%
11 1
 
4.5%
7 3
 
13.6%
6 6
27.3%
5 8
36.4%
4 1
 
4.5%
2 2
 
9.1%

주소
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:25:10.298716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length30.5
Mean length26.727273
Min length1

Characters and Unicode

Total characters588
Distinct characters114
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row-
2nd row전주시 덕진구 금암동 760-1
3rd row전주시 완산구 현무1길 21-15 3층 (경원동3가 179-9)
4th row전주시 완산구 팔달로 212-3 (경원동3가 33-9)
5th row전주시 덕진구 견훤왕궁로 277 YMCA 3층 (금암동 1600-6)
ValueCountFrequency (%)
3층 5
 
4.0%
전주시 5
 
4.0%
2층 4
 
3.2%
완산구 3
 
2.4%
덕진구 2
 
1.6%
금암동 2
 
1.6%
현무1길 2
 
1.6%
21-15 2
 
1.6%
경원동3가 2
 
1.6%
군산시 2
 
1.6%
Other values (95) 97
77.0%
2024-03-14T11:25:10.756392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114
 
19.4%
1 34
 
5.8%
2 23
 
3.9%
- 21
 
3.6%
3 19
 
3.2%
동 17
 
2.9%
( 17
 
2.9%
) 17
 
2.9%
시 14
 
2.4%
4 13
 
2.2%
Other values (104) 299
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 272
46.3%
Decimal Number 140
23.8%
Space Separator 114
19.4%
Dash Punctuation 21
 
3.6%
Open Punctuation 17
 
2.9%
Close Punctuation 17
 
2.9%
Uppercase Letter 6
 
1.0%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
 
6.2%
시 14
 
5.1%
로 13
 
4.8%
군 11
 
4.0%
읍 10
 
3.7%
층 10
 
3.7%
주 8
 
2.9%
리 8
 
2.9%
산 8
 
2.9%
길 8
 
2.9%
Other values (84) 165
60.7%
Decimal Number
ValueCountFrequency (%)
1 34
24.3%
2 23
16.4%
3 19
13.6%
4 13
 
9.3%
5 11
 
7.9%
0 9
 
6.4%
7 8
 
5.7%
6 8
 
5.7%
9 8
 
5.7%
8 7
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
S 1
16.7%
C 1
16.7%
M 1
16.7%
Y 1
16.7%
Space Separator
ValueCountFrequency (%)
114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 310
52.7%
Hangul 272
46.3%
Latin 6
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
 
6.2%
시 14
 
5.1%
로 13
 
4.8%
군 11
 
4.0%
읍 10
 
3.7%
층 10
 
3.7%
주 8
 
2.9%
리 8
 
2.9%
산 8
 
2.9%
길 8
 
2.9%
Other values (84) 165
60.7%
Common
ValueCountFrequency (%)
114
36.8%
1 34
 
11.0%
2 23
 
7.4%
- 21
 
6.8%
3 19
 
6.1%
( 17
 
5.5%
) 17
 
5.5%
4 13
 
4.2%
5 11
 
3.5%
0 9
 
2.9%
Other values (5) 32
 
10.3%
Latin
ValueCountFrequency (%)
A 2
33.3%
S 1
16.7%
C 1
16.7%
M 1
16.7%
Y 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 316
53.7%
Hangul 272
46.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114
36.1%
1 34
 
10.8%
2 23
 
7.3%
- 21
 
6.6%
3 19
 
6.0%
( 17
 
5.4%
) 17
 
5.4%
4 13
 
4.1%
5 11
 
3.5%
0 9
 
2.8%
Other values (10) 38
 
12.0%
Hangul
ValueCountFrequency (%)
동 17
 
6.2%
시 14
 
5.1%
로 13
 
4.8%
군 11
 
4.0%
읍 10
 
3.7%
층 10
 
3.7%
주 8
 
2.9%
리 8
 
2.9%
산 8
 
2.9%
길 8
 
2.9%
Other values (84) 165
60.7%

전화
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:25:10.935910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.6818182
Min length1

Characters and Unicode

Total characters169
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

Unique22 ?
Unique (%)100.0%

Sample

1st row-
2nd row226-0388
3rd row283-9766
4th row232-8383
5th row272-2845
ValueCountFrequency (%)
1
 
4.5%
226-0388 1
 
4.5%
288-9005 1
 
4.5%
583-0045 1
 
4.5%
562-2014 1
 
4.5%
653-0921 1
 
4.5%
642-4840 1
 
4.5%
352-7179 1
 
4.5%
324-2710 1
 
4.5%
432-9005 1
 
4.5%
Other values (12) 12
54.5%
2024-03-14T11:25:11.224281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 24
14.2%
4 24
14.2%
- 22
13.0%
3 19
11.2%
0 16
9.5%
5 13
7.7%
8 12
7.1%
9 10
5.9%
7 10
5.9%
1 10
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147
87.0%
Dash Punctuation 22
 
13.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 24
16.3%
4 24
16.3%
3 19
12.9%
0 16
10.9%
5 13
8.8%
8 12
8.2%
9 10
6.8%
7 10
6.8%
1 10
6.8%
6 9
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 24
14.2%
4 24
14.2%
- 22
13.0%
3 19
11.2%
0 16
9.5%
5 13
7.7%
8 12
7.1%
9 10
5.9%
7 10
5.9%
1 10
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 24
14.2%
4 24
14.2%
- 22
13.0%
3 19
11.2%
0 16
9.5%
5 13
7.7%
8 12
7.1%
9 10
5.9%
7 10
5.9%
1 10
5.9%

지정일자
Text

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-03-14T11:25:11.376146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.6818182
Min length1

Characters and Unicode

Total characters191
Distinct characters12
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)54.5%

Sample

1st row-
2nd row2008.10.1
3rd row1998.9.17
4th row2000.8.24
5th row2001.7. 1
ValueCountFrequency (%)
1 11
33.3%
2001.7 7
21.2%
2000.8.24 3
 
9.1%
1
 
3.0%
2008.10.1 1
 
3.0%
1998.9.17 1
 
3.0%
2003.8 1
 
3.0%
2001.12.31 1
 
3.0%
2001.5.19 1
 
3.0%
2003.4.30 1
 
3.0%
Other values (5) 5
15.2%
2024-03-14T11:25:11.582223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 45
23.6%
. 42
22.0%
1 31
16.2%
2 27
14.1%
11
 
5.8%
7 10
 
5.2%
8 7
 
3.7%
3 5
 
2.6%
4 4
 
2.1%
9 4
 
2.1%
Other values (2) 5
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137
71.7%
Other Punctuation 42
 
22.0%
Space Separator 11
 
5.8%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45
32.8%
1 31
22.6%
2 27
19.7%
7 10
 
7.3%
8 7
 
5.1%
3 5
 
3.6%
4 4
 
2.9%
9 4
 
2.9%
5 4
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 42
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45
23.6%
. 42
22.0%
1 31
16.2%
2 27
14.1%
11
 
5.8%
7 10
 
5.2%
8 7
 
3.7%
3 5
 
2.6%
4 4
 
2.1%
9 4
 
2.1%
Other values (2) 5
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45
23.6%
. 42
22.0%
1 31
16.2%
2 27
14.1%
11
 
5.8%
7 10
 
5.2%
8 7
 
3.7%
3 5
 
2.6%
4 4
 
2.1%
9 4
 
2.1%
Other values (2) 5
 
2.6%

규모유형
Categorical

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
표준
8 
기본
5 
-
4 
확대
4 
표준
1 

Length

Max length3
Median length2
Mean length1.8636364
Min length1

Unique

Unique1 ?
Unique (%)4.5%

Sample

1st row-
2nd row-
3rd row확대
4th row확대
5th row확대

Common Values

ValueCountFrequency (%)
표준 8
36.4%
기본 5
22.7%
- 4
18.2%
확대 4
18.2%
표준 1
 
4.5%

Length

2024-03-14T11:25:11.694786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:25:11.793667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
표준 9
40.9%
기본 5
22.7%
4
18.2%
확대 4
18.2%

Interactions

2024-03-14T11:25:08.456686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:25:11.869287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군센터명센터장현원주소전화지정일자규모유형
시군1.0000.9480.9481.0001.0001.0000.9281.000
센터명0.9481.0001.0001.0001.0001.0000.7310.767
센터장0.9481.0001.0001.0001.0001.0000.7310.767
현원1.0001.0001.0001.0001.0001.0001.0000.163
주소1.0001.0001.0001.0001.0001.0001.0001.000
전화1.0001.0001.0001.0001.0001.0001.0001.000
지정일자0.9280.7310.7311.0001.0001.0001.0000.929
규모유형1.0000.7670.7670.1631.0001.0000.9291.000
2024-03-14T11:25:11.968210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현원규모유형
현원1.0000.158
규모유형0.1581.000

Missing values

2024-03-14T11:25:08.556795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:25:08.671135image/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

시군센터명센터장현원주소전화지정일자규모유형
0계19개소-116----
1-전북 광역공석11전주시 덕진구 금암동 760-1226-03882008.10.1-
2전주전주허종현7전주시 완산구 현무1길 21-15 3층 (경원동3가 179-9)283-97661998.9.17확대
3전주전주 덕진박준홍7전주시 완산구 팔달로 212-3 (경원동3가 33-9)232-83832000.8.24확대
4전주전주 생명조용희7전주시 덕진구 견훤왕궁로 277 YMCA 3층 (금암동 1600-6)272-28452001.7. 1확대
5군산군산황인걸6군산시 해망로204 한국빌딩4층 (장미동 18-11)463-97312001.7. 1표준
6군산군산 한마음진성호6군산시 대학로 248 3층 (나운동 744-3)446-41242001.7. 1표준
7익산익산최갑선6익산시 평동로1길 12-35 (평화동 33-19)841-10402001.7. 1표준
8익산익산 원광김동옥6익산시 익산대로 180 구 전북은행 (창인동1가 21-1)842-14592001.7. 1표준
9정읍정읍김복례5정읍시 정읍사로 527 유정빌딩 3층 (시기동 81-14)533-03992001.7. 1표준
시군센터명센터장현원주소전화지정일자규모유형
12완주완주김진왕6완주군 상관면 상관소양로 46 (신리 286-5)232-24432003.8. 1표준
13진안진안최우영5진안군 진안읍 진무로 975 (단양리 294)432-90052001.12.31기본
14무주무주오용식4무주군 무주읍 단천로 154 2층 (읍내리 124-2)324-27102001.5.19기본
15장수장수서정일5장수군 장수읍 중동길 7 (장수리 483-1)352-71792003.4.30기본
16임실임실김종수5임실군 임실읍 운수로 50 삼일빌딩 3층 (이도리 264)642-48402002.7. 1기본
17순창순창한승연5순창군 순창읍 교성2길 31 (교성리 61)653-09212002.1. 1기본
18고창고창제춘홍5고창군 고창읍 월곡14길 고창군민종합복지회관 2층 (월곡리 620)562-20142001.7. 1표준
19부안부안장헌진5부안군 행안면 월륜길 5 현대자동차A/S 2층 (진동리 189-3)583-00452001.5.23표준
20청소년 자활 지원관전주허종현2전주시 완산구 현무1길 21-15288-90052001.8. 1-
21청소년 자활 지원관정읍김복례2정읍시 상동 중앙로 3-1 2층537-71422005.7.15-