Overview

Dataset statistics

Number of variables10
Number of observations27
Missing cells27
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory87.9 B

Variable types

Numeric3
Text4
Categorical3

Dataset

Description광주광역시 남구 관내의 민방위급수시설현황(시설명, 도로명주소, 지번주소, 용량, 설치년도, 용도 등) 정보를 제공합니다.
Author광주광역시 남구
URLhttps://www.data.go.kr/data/3033725/fileData.do

Alerts

비상발전기 보유 is highly overall correlated with 시설번호 and 1 other fieldsHigh correlation
용도 is highly overall correlated with 비상발전기 보유High correlation
시설번호 is highly overall correlated with 비상발전기 보유 and 1 other fieldsHigh correlation
구역 is highly overall correlated with 시설번호High correlation
소재지도로명주소 has 3 (11.1%) missing valuesMissing
소재지지번주소 has 24 (88.9%) missing valuesMissing
시설번호 has unique valuesUnique
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:27:05.660473
Analysis finished2023-12-12 21:27:07.238502
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T06:27:07.323010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-13T06:27:07.452837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

시설명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T06:27:07.644507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.8888889
Min length3

Characters and Unicode

Total characters159
Distinct characters77
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

Unique27 ?
Unique (%)100.0%

Sample

1st row사직체육공원
2nd row호남신학대학
3rd row유호빌딩
4th row태화빌딩
5th row무진중학교
ValueCountFrequency (%)
사직체육공원 1
 
3.7%
봉선놀이터 1
 
3.7%
지석마을 1
 
3.7%
대촌동주민센터 1
 
3.7%
송암동주민센터 1
 
3.7%
삼익아파트 1
 
3.7%
광주대학교 1
 
3.7%
삼익세라믹2차아파트 1
 
3.7%
대주2차아파트 1
 
3.7%
동성중고등학교 1
 
3.7%
Other values (17) 17
63.0%
2023-12-13T06:27:07.997694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 11
 
6.9%
교 8
 
5.0%
동 7
 
4.4%
대 6
 
3.8%
등 5
 
3.1%
중 5
 
3.1%
주 5
 
3.1%
아 4
 
2.5%
원 4
 
2.5%
터 4
 
2.5%
Other values (67) 100
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
98.7%
Decimal Number 2
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 11
 
7.0%
교 8
 
5.1%
동 7
 
4.5%
대 6
 
3.8%
등 5
 
3.2%
중 5
 
3.2%
주 5
 
3.2%
아 4
 
2.5%
원 4
 
2.5%
터 4
 
2.5%
Other values (66) 98
62.4%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 157
98.7%
Common 2
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 11
 
7.0%
교 8
 
5.1%
동 7
 
4.5%
대 6
 
3.8%
등 5
 
3.2%
중 5
 
3.2%
주 5
 
3.2%
아 4
 
2.5%
원 4
 
2.5%
터 4
 
2.5%
Other values (66) 98
62.4%
Common
ValueCountFrequency (%)
2 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157
98.7%
ASCII 2
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 11
 
7.0%
교 8
 
5.1%
동 7
 
4.5%
대 6
 
3.8%
등 5
 
3.2%
중 5
 
3.2%
주 5
 
3.2%
아 4
 
2.5%
원 4
 
2.5%
터 4
 
2.5%
Other values (66) 98
62.4%
ASCII
ValueCountFrequency (%)
2 2
100.0%

소재지도로명주소
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing3
Missing (%)11.1%
Memory size348.0 B
2023-12-13T06:27:08.216410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23.5
Mean length22.166667
Min length19

Characters and Unicode

Total characters532
Distinct characters62
Distinct categories6 ?
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 (%)100.0%

Sample

1st row광주광역시 남구 사직길 4-9(양림동)
2nd row광주광역시 남구 제중로 77(양림동)
3rd row광주광역시 남구 사직길 10(사동)
4th row광주광역시 남구 제중로 112번길 8(서동)
5th row광주광역시 남구 중앙로 7(월산동)
ValueCountFrequency (%)
광주광역시 24
24.7%
남구 24
24.7%
제중로 2
 
2.1%
사직길 2
 
2.1%
효덕로 1
 
1.0%
9(주월동 1
 
1.0%
오방로 1
 
1.0%
16-10(봉선동 1
 
1.0%
봉선로144번길 1
 
1.0%
8(봉선동 1
 
1.0%
Other values (39) 39
40.2%
2023-12-13T06:27:08.571324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
 
13.7%
광 48
 
9.0%
주 27
 
5.1%
남 25
 
4.7%
구 25
 
4.7%
역 24
 
4.5%
시 24
 
4.5%
( 24
 
4.5%
동 24
 
4.5%
) 24
 
4.5%
Other values (52) 214
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 333
62.6%
Decimal Number 74
 
13.9%
Space Separator 73
 
13.7%
Open Punctuation 24
 
4.5%
Close Punctuation 24
 
4.5%
Dash Punctuation 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 48
14.4%
주 27
 
8.1%
남 25
 
7.5%
구 25
 
7.5%
역 24
 
7.2%
시 24
 
7.2%
동 24
 
7.2%
로 21
 
6.3%
월 13
 
3.9%
길 11
 
3.3%
Other values (38) 91
27.3%
Decimal Number
ValueCountFrequency (%)
1 14
18.9%
4 11
14.9%
7 11
14.9%
6 9
12.2%
2 7
9.5%
5 6
8.1%
8 5
 
6.8%
9 5
 
6.8%
0 4
 
5.4%
3 2
 
2.7%
Space Separator
ValueCountFrequency (%)
73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 333
62.6%
Common 199
37.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 48
14.4%
주 27
 
8.1%
남 25
 
7.5%
구 25
 
7.5%
역 24
 
7.2%
시 24
 
7.2%
동 24
 
7.2%
로 21
 
6.3%
월 13
 
3.9%
길 11
 
3.3%
Other values (38) 91
27.3%
Common
ValueCountFrequency (%)
73
36.7%
( 24
 
12.1%
) 24
 
12.1%
1 14
 
7.0%
4 11
 
5.5%
7 11
 
5.5%
6 9
 
4.5%
2 7
 
3.5%
5 6
 
3.0%
8 5
 
2.5%
Other values (4) 15
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 333
62.6%
ASCII 199
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
73
36.7%
( 24
 
12.1%
) 24
 
12.1%
1 14
 
7.0%
4 11
 
5.5%
7 11
 
5.5%
6 9
 
4.5%
2 7
 
3.5%
5 6
 
3.0%
8 5
 
2.5%
Other values (4) 15
 
7.5%
Hangul
ValueCountFrequency (%)
광 48
14.4%
주 27
 
8.1%
남 25
 
7.5%
구 25
 
7.5%
역 24
 
7.2%
시 24
 
7.2%
동 24
 
7.2%
로 21
 
6.3%
월 13
 
3.9%
길 11
 
3.3%
Other values (38) 91
27.3%

소재지지번주소
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing24
Missing (%)88.9%
Memory size348.0 B
2023-12-13T06:27:08.749760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length17
Min length16

Characters and Unicode

Total characters51
Distinct characters23
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

Unique3 ?
Unique (%)100.0%

Sample

1st row광주광역시 남구 봉선동 517
2nd row광주광역시 남구 진월동 산188
3rd row광주광역시 남구 지석동 293-1
ValueCountFrequency (%)
광주광역시 3
25.0%
남구 3
25.0%
봉선동 1
 
8.3%
517 1
 
8.3%
진월동 1
 
8.3%
산188 1
 
8.3%
지석동 1
 
8.3%
293-1 1
 
8.3%
2023-12-13T06:27:09.072429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
17.6%
광 6
11.8%
구 3
 
5.9%
주 3
 
5.9%
동 3
 
5.9%
1 3
 
5.9%
남 3
 
5.9%
시 3
 
5.9%
역 3
 
5.9%
8 2
 
3.9%
Other values (13) 13
25.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31
60.8%
Decimal Number 10
 
19.6%
Space Separator 9
 
17.6%
Dash Punctuation 1
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 6
19.4%
구 3
9.7%
주 3
9.7%
동 3
9.7%
남 3
9.7%
시 3
9.7%
역 3
9.7%
석 1
 
3.2%
지 1
 
3.2%
월 1
 
3.2%
Other values (4) 4
12.9%
Decimal Number
ValueCountFrequency (%)
1 3
30.0%
8 2
20.0%
3 1
 
10.0%
9 1
 
10.0%
2 1
 
10.0%
7 1
 
10.0%
5 1
 
10.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31
60.8%
Common 20
39.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 6
19.4%
구 3
9.7%
주 3
9.7%
동 3
9.7%
남 3
9.7%
시 3
9.7%
역 3
9.7%
석 1
 
3.2%
지 1
 
3.2%
월 1
 
3.2%
Other values (4) 4
12.9%
Common
ValueCountFrequency (%)
9
45.0%
1 3
 
15.0%
8 2
 
10.0%
3 1
 
5.0%
9 1
 
5.0%
2 1
 
5.0%
7 1
 
5.0%
5 1
 
5.0%
- 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31
60.8%
ASCII 20
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9
45.0%
1 3
 
15.0%
8 2
 
10.0%
3 1
 
5.0%
9 1
 
5.0%
2 1
 
5.0%
7 1
 
5.0%
5 1
 
5.0%
- 1
 
5.0%
Hangul
ValueCountFrequency (%)
광 6
19.4%
구 3
9.7%
주 3
9.7%
동 3
9.7%
남 3
9.7%
시 3
9.7%
역 3
9.7%
석 1
 
3.2%
지 1
 
3.2%
월 1
 
3.2%
Other values (4) 4
12.9%

용량
Real number (ℝ)

Distinct19
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.11111
Minimum50
Maximum700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T06:27:09.218199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile56.3
Q1100
median123
Q3200
95-th percentile503.5
Maximum700
Range650
Interquartile range (IQR)100

Descriptive statistics

Standard deviation154.15136
Coefficient of variation (CV)0.83727352
Kurtosis4.5977095
Mean184.11111
Median Absolute Deviation (MAD)27
Skewness2.1841285
Sum4971
Variance23762.641
MonotonicityNot monotonic
2023-12-13T06:27:09.341220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
100 4
14.8%
120 3
 
11.1%
200 2
 
7.4%
150 2
 
7.4%
50 2
 
7.4%
130 1
 
3.7%
210 1
 
3.7%
505 1
 
3.7%
288 1
 
3.7%
71 1
 
3.7%
Other values (9) 9
33.3%
ValueCountFrequency (%)
50 2
7.4%
71 1
 
3.7%
80 1
 
3.7%
100 4
14.8%
110 1
 
3.7%
115 1
 
3.7%
120 3
11.1%
123 1
 
3.7%
130 1
 
3.7%
134 1
 
3.7%
ValueCountFrequency (%)
700 1
3.7%
505 1
3.7%
500 1
3.7%
300 1
3.7%
288 1
3.7%
210 1
3.7%
200 2
7.4%
150 2
7.4%
145 1
3.7%
134 1
3.7%

인원
Text

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T06:27:09.502186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1111111
Min length4

Characters and Unicode

Total characters138
Distinct characters10
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 (%)59.3%

Sample

1st row8,000
2nd row4,800
3rd row4,000
4th row4,600
5th row28,000
ValueCountFrequency (%)
4,000 4
 
14.8%
4,800 3
 
11.1%
8,000 2
 
7.4%
6,000 2
 
7.4%
2000 1
 
3.7%
4,400 1
 
3.7%
2,000 1
 
3.7%
12,000 1
 
3.7%
5,800 1
 
3.7%
3,200 1
 
3.7%
Other values (10) 10
37.0%
2023-12-13T06:27:09.803703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 65
47.1%
, 25
 
18.1%
4 13
 
9.4%
2 12
 
8.7%
8 9
 
6.5%
6 4
 
2.9%
5 4
 
2.9%
1 3
 
2.2%
3 2
 
1.4%
9 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113
81.9%
Other Punctuation 25
 
18.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65
57.5%
4 13
 
11.5%
2 12
 
10.6%
8 9
 
8.0%
6 4
 
3.5%
5 4
 
3.5%
1 3
 
2.7%
3 2
 
1.8%
9 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 138
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65
47.1%
, 25
 
18.1%
4 13
 
9.4%
2 12
 
8.7%
8 9
 
6.5%
6 4
 
2.9%
5 4
 
2.9%
1 3
 
2.2%
3 2
 
1.4%
9 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65
47.1%
, 25
 
18.1%
4 13
 
9.4%
2 12
 
8.7%
8 9
 
6.5%
6 4
 
2.9%
5 4
 
2.9%
1 3
 
2.2%
3 2
 
1.4%
9 1
 
0.7%

설치년도(지정년도)
Real number (ℝ)

Distinct17
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.5556
Minimum1982
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T06:27:09.944301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile1984.4
Q11995
median1999
Q32009
95-th percentile2012
Maximum2015
Range33
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.8026802
Coefficient of variation (CV)0.0044001179
Kurtosis-0.33608378
Mean2000.5556
Median Absolute Deviation (MAD)5
Skewness-0.29739152
Sum54015
Variance77.487179
MonotonicityNot monotonic
2023-12-13T06:27:10.050613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1999 3
11.1%
1995 3
11.1%
1998 2
 
7.4%
2010 2
 
7.4%
1982 2
 
7.4%
2009 2
 
7.4%
2003 2
 
7.4%
2012 2
 
7.4%
1996 1
 
3.7%
2001 1
 
3.7%
Other values (7) 7
25.9%
ValueCountFrequency (%)
1982 2
7.4%
1990 1
 
3.7%
1993 1
 
3.7%
1994 1
 
3.7%
1995 3
11.1%
1996 1
 
3.7%
1997 1
 
3.7%
1998 2
7.4%
1999 3
11.1%
2001 1
 
3.7%
ValueCountFrequency (%)
2015 1
 
3.7%
2012 2
7.4%
2011 1
 
3.7%
2010 2
7.4%
2009 2
7.4%
2008 1
 
3.7%
2003 2
7.4%
2001 1
 
3.7%
1999 3
11.1%
1998 2
7.4%

비상발전기 보유
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
<NA>
21 
20Kw
5 
23Kw
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row20Kw
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 21
77.8%
20Kw 5
 
18.5%
23Kw 1
 
3.7%

Length

2023-12-13T06:27:10.171624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:27:10.273977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
77.8%
20kw 5
 
18.5%
23kw 1
 
3.7%

용도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
생활용수
17 
음용수
10 

Length

Max length4
Median length4
Mean length3.6296296
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활용수
2nd row음용수
3rd row음용수
4th row생활용수
5th row생활용수

Common Values

ValueCountFrequency (%)
생활용수 17
63.0%
음용수 10
37.0%

Length

2023-12-13T06:27:10.372197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:27:10.452798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활용수 17
63.0%
음용수 10
37.0%

구역
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size348.0 B
5구역
9 
3구역
5 
4구역
5 
1구역
4 
2구역
4 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1구역
2nd row1구역
3rd row1구역
4th row1구역
5th row2구역

Common Values

ValueCountFrequency (%)
5구역 9
33.3%
3구역 5
18.5%
4구역 5
18.5%
1구역 4
14.8%
2구역 4
14.8%

Length

2023-12-13T06:27:10.532232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:27:10.615508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5구역 9
33.3%
3구역 5
18.5%
4구역 5
18.5%
1구역 4
14.8%
2구역 4
14.8%

Interactions

2023-12-13T06:27:06.607566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:27:06.087950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:27:06.339507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:27:06.697438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:27:06.168069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:27:06.412765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:27:06.807074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:27:06.252237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:27:06.492285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:27:10.695474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설번호시설명소재지도로명주소소재지지번주소용량인원설치년도(지정년도)비상발전기 보유용도구역
시설번호1.0001.0001.0001.0000.0000.8380.6961.0000.0000.995
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.000NaN1.0001.0001.000NaN1.0001.000
소재지지번주소1.0001.000NaN1.0001.0001.0001.000NaN1.0001.000
용량0.0001.0001.0001.0001.0001.0000.1500.0000.5480.000
인원0.8381.0001.0001.0001.0001.0000.0001.0000.0000.000
설치년도(지정년도)0.6961.0001.0001.0000.1500.0001.0000.0000.0000.000
비상발전기 보유1.0001.000NaNNaN0.0001.0000.0001.000NaN0.000
용도0.0001.0001.0001.0000.5480.0000.000NaN1.0000.000
구역0.9951.0001.0001.0000.0000.0000.0000.0000.0001.000
2023-12-13T06:27:10.798117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구역비상발전기 보유용도
구역1.0000.0000.000
비상발전기 보유0.0001.0001.000
용도0.0001.0001.000
2023-12-13T06:27:10.873762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설번호용량설치년도(지정년도)비상발전기 보유용도구역
시설번호1.000-0.1760.1241.0000.0000.731
용량-0.1761.000-0.3100.0000.3550.000
설치년도(지정년도)0.124-0.3101.0000.0000.0000.000
비상발전기 보유1.0000.0000.0001.0001.0000.000
용도0.0000.3550.0001.0001.0000.000
구역0.7310.0000.0000.0000.0001.000

Missing values

2023-12-13T06:27:06.941817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:27:07.080084image/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.
2023-12-13T06:27:07.183427image/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

시설번호시설명소재지도로명주소소재지지번주소용량인원설치년도(지정년도)비상발전기 보유용도구역
01사직체육공원광주광역시 남구 사직길 4-9(양림동)<NA>2008,000199620Kw생활용수1구역
12호남신학대학광주광역시 남구 제중로 77(양림동)<NA>1204,8002003<NA>음용수1구역
23유호빌딩광주광역시 남구 사직길 10(사동)<NA>1004,0001993<NA>음용수1구역
34태화빌딩광주광역시 남구 제중로 112번길 8(서동)<NA>1154,6002010<NA>생활용수1구역
45무진중학교광주광역시 남구 중앙로 7(월산동)<NA>70028,0001982<NA>생활용수2구역
56동신대한방병원광주광역시 남구 월산로 147(월산동)<NA>1204,8001999<NA>음용수2구역
67손정모정형외과광주광역시 남구 대남대로 288(월산동)<NA>1305,2001999<NA>생활용수2구역
78월산동주민센터광주광역시 남구 구성로 76번길9(월산동)<NA>2108,400201120Kw생활용수2구역
89원광대한방병원광주광역시 남구 회재로 1140-23(주월동)<NA>2008,0001995<NA>생활용수3구역
910빅스포광주광역시 남구 서문대로 745(주월동)<NA>50520,2002009<NA>생활용수3구역
시설번호시설명소재지도로명주소소재지지번주소용량인원설치년도(지정년도)비상발전기 보유용도구역
1718문성중고등학교<NA>광주광역시 남구 봉선동 5171345,3602009<NA>음용수4구역
1819동성중고등학교<NA>광주광역시 남구 진월동 산18850020,0001995<NA>생활용수5구역
1920대주2차아파트광주광역시 남구 서문대로678번길 7(진월동)<NA>803,2002015<NA>음용수5구역
2021삼익세라믹2차아파트광주광역시 남구 서문대로654번길 24(진월동)<NA>1455,800200120Kw생활용수5구역
2122광주대학교광주광역시 남구 효덕로 277(진월동)<NA>30012,0002008<NA>음용수5구역
2223삼익아파트광주광역시 남구 서문대로556번길 6(송하동)<NA>1004,0002003<NA>생활용수5구역
2324송암동주민센터광주광역시 남구 송암로 66(송하동)<NA>1004,000201020Kw생활용수5구역
2425대촌동주민센터광주광역시 남구 포충로 591(지석동)<NA>502,0001997<NA>생활용수5구역
2526지석마을<NA>광주광역시 남구 지석동 293-11506,000199823Kw생활용수5구역
2627무학초등학교광주광역시 남구 월지길 7(월성동)<NA>1234,9201998<NA>음용수5구역