Overview

Dataset statistics

Number of variables11
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory95.1 B

Variable types

Numeric5
Categorical3
Text2
DateTime1

Dataset

Description광주광역시 광산구에 설치 되어있는 민방위 급수시설의 현황(구분, 시설명, 주소, 생산용량, 지정연도 등) 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15044857/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 연번High correlation
행정동구분 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
비고 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:03:25.376852
Analysis finished2023-12-12 15:03:28.601246
Duration3.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.5
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T00:03:28.673808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.15
Q116.75
median32.5
Q348.25
95-th percentile60.85
Maximum64
Range63
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.618987
Coefficient of variation (CV)0.5728919
Kurtosis-1.2
Mean32.5
Median Absolute Deviation (MAD)16
Skewness0
Sum2080
Variance346.66667
MonotonicityStrictly increasing
2023-12-13T00:03:28.837774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
34 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
43 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
64 1
1.6%
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size644.0 B
공공시설
42 
정부지원
15 
민간시설
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공시설 42
65.6%
정부지원 15
 
23.4%
민간시설 7
 
10.9%

Length

2023-12-13T00:03:28.987582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:03:29.113051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공시설 42
65.6%
정부지원 15
 
23.4%
민간시설 7
 
10.9%

시설명
Text

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T00:03:29.346916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.265625
Min length4

Characters and Unicode

Total characters401
Distinct characters120
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)96.9%

Sample

1st row광산구청
2nd row송정공원(궁도장)
3rd row소촌동 민방위교육장
4th row뉴밀레니엄상가
5th row빛고을국민체육시설
ValueCountFrequency (%)
한두레농산물센터 2
 
3.0%
대우자동차운전학원 1
 
1.5%
운남중학교 1
 
1.5%
운남초등학교 1
 
1.5%
송정초등학교 1
 
1.5%
송정중앙초등학교 1
 
1.5%
어룡초등학교 1
 
1.5%
어등초등학교 1
 
1.5%
송우초등학교 1
 
1.5%
월곡중학교 1
 
1.5%
Other values (55) 55
83.3%
2023-12-13T00:03:29.749099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 43
 
10.7%
학 42
 
10.5%
등 27
 
6.7%
초 22
 
5.5%
중 13
 
3.2%
남 9
 
2.2%
산 8
 
2.0%
정 8
 
2.0%
송 7
 
1.7%
공 7
 
1.7%
Other values (110) 215
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 386
96.3%
Uppercase Letter 6
 
1.5%
Decimal Number 3
 
0.7%
Space Separator 2
 
0.5%
Open Punctuation 2
 
0.5%
Close Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 43
 
11.1%
학 42
 
10.9%
등 27
 
7.0%
초 22
 
5.7%
중 13
 
3.4%
남 9
 
2.3%
산 8
 
2.1%
정 8
 
2.1%
송 7
 
1.8%
공 7
 
1.8%
Other values (101) 200
51.8%
Uppercase Letter
ValueCountFrequency (%)
P 2
33.3%
A 2
33.3%
T 2
33.3%
Decimal Number
ValueCountFrequency (%)
5 1
33.3%
2 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 386
96.3%
Common 9
 
2.2%
Latin 6
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 43
 
11.1%
학 42
 
10.9%
등 27
 
7.0%
초 22
 
5.7%
중 13
 
3.4%
남 9
 
2.3%
산 8
 
2.1%
정 8
 
2.1%
송 7
 
1.8%
공 7
 
1.8%
Other values (101) 200
51.8%
Common
ValueCountFrequency (%)
2
22.2%
( 2
22.2%
) 2
22.2%
5 1
11.1%
2 1
11.1%
1 1
11.1%
Latin
ValueCountFrequency (%)
P 2
33.3%
A 2
33.3%
T 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 386
96.3%
ASCII 15
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 43
 
11.1%
학 42
 
10.9%
등 27
 
7.0%
초 22
 
5.7%
중 13
 
3.4%
남 9
 
2.3%
산 8
 
2.1%
정 8
 
2.1%
송 7
 
1.8%
공 7
 
1.8%
Other values (101) 200
51.8%
ASCII
ValueCountFrequency (%)
2
13.3%
( 2
13.3%
) 2
13.3%
P 2
13.3%
A 2
13.3%
T 2
13.3%
5 1
6.7%
2 1
6.7%
1 1
6.7%

행정동구분
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size644.0 B
월곡동
8 
월계동
8 
운남동
6 
우산동
5 
수완동
5 
Other values (18)
32 

Length

Max length3
Median length3
Mean length2.96875
Min length2

Unique

Unique10 ?
Unique (%)15.6%

Sample

1st row송정동
2nd row소촌동
3rd row소촌동
4th row우산동
5th row우산동

Common Values

ValueCountFrequency (%)
월곡동 8
12.5%
월계동 8
12.5%
운남동 6
 
9.4%
우산동 5
 
7.8%
수완동 5
 
7.8%
신창동 5
 
7.8%
소촌동 4
 
6.2%
송정동 3
 
4.7%
장덕동 2
 
3.1%
하남동 2
 
3.1%
Other values (13) 16
25.0%

Length

2023-12-13T00:03:29.939366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
월곡동 8
12.5%
월계동 8
12.5%
운남동 6
 
9.4%
우산동 5
 
7.8%
수완동 5
 
7.8%
신창동 5
 
7.8%
소촌동 4
 
6.2%
송정동 3
 
4.7%
도산동 2
 
3.1%
산정동 2
 
3.1%
Other values (13) 16
25.0%

주소
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T00:03:30.239064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length24.265625
Min length16

Characters and Unicode

Total characters1553
Distinct characters87
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

Unique64 ?
Unique (%)100.0%

Sample

1st row광주광역시 광산구 광산로 29번길 15(송정동)
2nd row광주광역시 광산구 금봉로 22-41(소촌동)
3rd row광주광역시 광산구 소촌로 85번길 14-9(소촌동)
4th row광주광역시 광산구 용아로 230(우산동)
5th row광주광역시 광산구 무진대로211번길 28(우산동)
ValueCountFrequency (%)
광주광역시 64
24.3%
광산구 64
24.3%
월곡산정로 5
 
1.9%
왕버들로 3
 
1.1%
207(수완동 2
 
0.8%
26(신창동 2
 
0.8%
목련로 2
 
0.8%
하남대로 2
 
0.8%
첨단중앙로181번길 2
 
0.8%
월계동 2
 
0.8%
Other values (110) 115
43.7%
2023-12-13T00:03:31.048026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
199
 
12.8%
광 194
 
12.5%
산 83
 
5.3%
역 64
 
4.1%
시 64
 
4.1%
구 64
 
4.1%
주 64
 
4.1%
동 60
 
3.9%
( 58
 
3.7%
) 58
 
3.7%
Other values (77) 645
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 983
63.3%
Decimal Number 242
 
15.6%
Space Separator 199
 
12.8%
Open Punctuation 58
 
3.7%
Close Punctuation 58
 
3.7%
Dash Punctuation 13
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 194
19.7%
산 83
 
8.4%
역 64
 
6.5%
시 64
 
6.5%
구 64
 
6.5%
주 64
 
6.5%
동 60
 
6.1%
로 57
 
5.8%
길 34
 
3.5%
번 28
 
2.8%
Other values (63) 271
27.6%
Decimal Number
ValueCountFrequency (%)
1 49
20.2%
2 42
17.4%
0 25
10.3%
8 22
9.1%
3 22
9.1%
7 19
 
7.9%
4 17
 
7.0%
5 17
 
7.0%
6 16
 
6.6%
9 13
 
5.4%
Space Separator
ValueCountFrequency (%)
199
100.0%
Open Punctuation
ValueCountFrequency (%)
( 58
100.0%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 983
63.3%
Common 570
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 194
19.7%
산 83
 
8.4%
역 64
 
6.5%
시 64
 
6.5%
구 64
 
6.5%
주 64
 
6.5%
동 60
 
6.1%
로 57
 
5.8%
길 34
 
3.5%
번 28
 
2.8%
Other values (63) 271
27.6%
Common
ValueCountFrequency (%)
199
34.9%
( 58
 
10.2%
) 58
 
10.2%
1 49
 
8.6%
2 42
 
7.4%
0 25
 
4.4%
8 22
 
3.9%
3 22
 
3.9%
7 19
 
3.3%
4 17
 
3.0%
Other values (4) 59
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 983
63.3%
ASCII 570
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
199
34.9%
( 58
 
10.2%
) 58
 
10.2%
1 49
 
8.6%
2 42
 
7.4%
0 25
 
4.4%
8 22
 
3.9%
3 22
 
3.9%
7 19
 
3.3%
4 17
 
3.0%
Other values (4) 59
 
10.4%
Hangul
ValueCountFrequency (%)
광 194
19.7%
산 83
 
8.4%
역 64
 
6.5%
시 64
 
6.5%
구 64
 
6.5%
주 64
 
6.5%
동 60
 
6.1%
로 57
 
5.8%
길 34
 
3.5%
번 28
 
2.8%
Other values (63) 271
27.6%

1일생산용량(톤)
Real number (ℝ)

Distinct15
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.23438
Minimum100
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T00:03:31.186180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median120
Q3200
95-th percentile338.25
Maximum390
Range290
Interquartile range (IQR)100

Descriptive statistics

Standard deviation80.091365
Coefficient of variation (CV)0.49983884
Kurtosis0.62916831
Mean160.23438
Median Absolute Deviation (MAD)20
Skewness1.2947293
Sum10255
Variance6414.6267
MonotonicityNot monotonic
2023-12-13T00:03:31.309714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100 30
46.9%
150 7
 
10.9%
300 4
 
6.2%
200 4
 
6.2%
120 4
 
6.2%
250 3
 
4.7%
180 3
 
4.7%
350 2
 
3.1%
345 1
 
1.6%
170 1
 
1.6%
Other values (5) 5
 
7.8%
ValueCountFrequency (%)
100 30
46.9%
120 4
 
6.2%
140 1
 
1.6%
150 7
 
10.9%
170 1
 
1.6%
180 3
 
4.7%
190 1
 
1.6%
200 4
 
6.2%
220 1
 
1.6%
250 3
 
4.7%
ValueCountFrequency (%)
390 1
 
1.6%
350 2
3.1%
345 1
 
1.6%
300 4
6.2%
280 1
 
1.6%
250 3
4.7%
220 1
 
1.6%
200 4
6.2%
190 1
 
1.6%
180 3
4.7%

지정연도
Real number (ℝ)

Distinct24
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.0781
Minimum1985
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T00:03:31.449208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1988.15
Q11993.75
median1997
Q32002
95-th percentile2011.4
Maximum2019
Range34
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation7.0762394
Coefficient of variation (CV)0.0035415229
Kurtosis0.28581555
Mean1998.0781
Median Absolute Deviation (MAD)4.5
Skewness0.58004628
Sum127877
Variance50.073165
MonotonicityNot monotonic
2023-12-13T00:03:31.586847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1997 9
14.1%
1994 6
 
9.4%
1995 5
 
7.8%
2001 4
 
6.2%
2002 4
 
6.2%
1992 3
 
4.7%
1993 3
 
4.7%
2008 3
 
4.7%
1991 3
 
4.7%
2003 3
 
4.7%
Other values (14) 21
32.8%
ValueCountFrequency (%)
1985 2
 
3.1%
1986 1
 
1.6%
1988 1
 
1.6%
1989 1
 
1.6%
1990 2
 
3.1%
1991 3
4.7%
1992 3
4.7%
1993 3
4.7%
1994 6
9.4%
1995 5
7.8%
ValueCountFrequency (%)
2019 1
 
1.6%
2013 1
 
1.6%
2012 2
3.1%
2008 3
4.7%
2007 3
4.7%
2005 2
3.1%
2003 3
4.7%
2002 4
6.2%
2001 4
6.2%
2000 1
 
1.6%

비고
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size644.0 B
음용수
34 
생활용수
30 

Length

Max length4
Median length3
Mean length3.46875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
음용수 34
53.1%
생활용수 30
46.9%

Length

2023-12-13T00:03:31.725363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:03:31.838851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
음용수 34
53.1%
생활용수 30
46.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.179046
Minimum35.126992
Maximum35.222949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T00:03:31.968514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.126992
5-th percentile35.139775
Q135.163597
median35.177618
Q335.19796
95-th percentile35.222061
Maximum35.222949
Range0.095957265
Interquartile range (IQR)0.03436298

Descriptive statistics

Standard deviation0.026023067
Coefficient of variation (CV)0.00073973204
Kurtosis-0.7828407
Mean35.179046
Median Absolute Deviation (MAD)0.017613173
Skewness0.050528459
Sum2251.4589
Variance0.00067720003
MonotonicityNot monotonic
2023-12-13T00:03:32.118506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1994097509577 2
 
3.1%
35.1396474365046 1
 
1.6%
35.1776044736701 1
 
1.6%
35.1315977759523 1
 
1.6%
35.1478478065259 1
 
1.6%
35.1551258516602 1
 
1.6%
35.1655005737145 1
 
1.6%
35.1592339837763 1
 
1.6%
35.1667178989531 1
 
1.6%
35.1674046662212 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
35.1269919387554 1
1.6%
35.1304629973544 1
1.6%
35.1315977759523 1
1.6%
35.1396474365046 1
1.6%
35.140501036363 1
1.6%
35.1413723115678 1
1.6%
35.1458893281603 1
1.6%
35.1478478065259 1
1.6%
35.148242286973 1
1.6%
35.1499676556862 1
1.6%
ValueCountFrequency (%)
35.2229492035674 1
1.6%
35.2227412370622 1
1.6%
35.2225617788653 1
1.6%
35.2222280689038 1
1.6%
35.2211120632111 1
1.6%
35.2198792707267 1
1.6%
35.2195984826775 1
1.6%
35.2187462604298 1
1.6%
35.2145064376122 1
1.6%
35.2136003556556 1
1.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.81065
Minimum126.72091
Maximum126.84619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T00:03:32.258977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72091
5-th percentile126.75576
Q1126.79856
median126.81326
Q3126.83336
95-th percentile126.84203
Maximum126.84619
Range0.12527908
Interquartile range (IQR)0.034795601

Descriptive statistics

Standard deviation0.026543879
Coefficient of variation (CV)0.00020931901
Kurtosis1.6075865
Mean126.81065
Median Absolute Deviation (MAD)0.016288691
Skewness-1.1797286
Sum8115.8814
Variance0.00070457752
MonotonicityNot monotonic
2023-12-13T00:03:32.420636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.833358689083 2
 
3.1%
126.79364427408 1
 
1.6%
126.819284843722 1
 
1.6%
126.796222900481 1
 
1.6%
126.795011027558 1
 
1.6%
126.793432011503 1
 
1.6%
126.807783827659 1
 
1.6%
126.810593508363 1
 
1.6%
126.81340318381 1
 
1.6%
126.805061186235 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
126.720909545223 1
1.6%
126.74479017612 1
1.6%
126.746825700017 1
1.6%
126.755047565907 1
1.6%
126.759772747738 1
1.6%
126.765040345608 1
1.6%
126.774826099666 1
1.6%
126.788096984597 1
1.6%
126.79088222594 1
1.6%
126.793062024296 1
1.6%
ValueCountFrequency (%)
126.846188623007 1
1.6%
126.844251438999 1
1.6%
126.843113822031 1
1.6%
126.842258172223 1
1.6%
126.84076814963 1
1.6%
126.84072373004 1
1.6%
126.839325135641 1
1.6%
126.837847089251 1
1.6%
126.837571787039 1
1.6%
126.837267149416 1
1.6%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
Minimum2022-12-31 00:00:00
Maximum2022-12-31 00:00:00
2023-12-13T00:03:32.543455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:32.630587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T00:03:27.823986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:25.844282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.280405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.793659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.345549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.934495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:25.931971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.393242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.942798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.440037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:28.035809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.011427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.477096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.051082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.525607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:28.140980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.095830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.564858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.150104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.618588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:28.228556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.177753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:26.666088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.234827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:27.715946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:03:32.721304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분시설명행정동구분주소1일생산용량(톤)지정연도비고위도경도
연번1.0000.8791.0000.8211.0000.4630.5530.9950.6530.735
구분0.8791.0001.0000.4961.0000.6670.0000.2620.1670.238
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동구분0.8210.4961.0001.0001.0000.0000.6790.4060.9490.964
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1일생산용량(톤)0.4630.6671.0000.0001.0001.0000.0000.2610.6190.189
지정연도0.5530.0001.0000.6791.0000.0001.0000.3580.5410.783
비고0.9950.2621.0000.4061.0000.2610.3581.0000.0000.000
위도0.6530.1671.0000.9491.0000.6190.5410.0001.0000.739
경도0.7350.2381.0000.9641.0000.1890.7830.0000.7391.000
2023-12-13T00:03:32.853726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고행정동구분구분
비고1.0000.2790.421
행정동구분0.2791.0000.234
구분0.4210.2341.000
2023-12-13T00:03:32.954549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번1일생산용량(톤)지정연도위도경도구분행정동구분비고
연번1.0000.079-0.1360.1370.1360.7660.4120.874
1일생산용량(톤)0.0791.000-0.469-0.0780.0850.3580.0000.241
지정연도-0.136-0.4691.0000.3530.3530.0000.3440.248
위도0.137-0.0780.3531.0000.6650.0820.6600.000
경도0.1360.0850.3530.6651.0000.1290.7090.000
구분0.7660.3580.0000.0820.1291.0000.2340.421
행정동구분0.4120.0000.3440.6600.7090.2341.0000.279
비고0.8740.2410.2480.0000.0000.4210.2791.000

Missing values

2023-12-13T00:03:28.352051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:03:28.536279image/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

연번구분시설명행정동구분주소1일생산용량(톤)지정연도비고위도경도데이터기준일자
01정부지원광산구청송정동광주광역시 광산구 광산로 29번길 15(송정동)3501988음용수35.139647126.7936442022-12-31
12정부지원송정공원(궁도장)소촌동광주광역시 광산구 금봉로 22-41(소촌동)1001999음용수35.15019126.8008322022-12-31
23정부지원소촌동 민방위교육장소촌동광주광역시 광산구 소촌로 85번길 14-9(소촌동)3501990음용수35.152662126.7908822022-12-31
34정부지원뉴밀레니엄상가우산동광주광역시 광산구 용아로 230(우산동)1002001음용수35.163628126.8021382022-12-31
45정부지원빛고을국민체육시설우산동광주광역시 광산구 무진대로211번길 28(우산동)1002005음용수35.163503126.8044632022-12-31
56정부지원한성1차아파트월곡동광주광역시 광산구 월곡산정로 108(월곡동)1002000음용수35.165536126.8153092022-12-31
67정부지원반석교회산정동광주광역시 광산구 여대길 220(산정동)1002002음용수35.165783126.7999612022-12-31
78정부지원산정공원월곡동광주광역시 광산구 산정공원로 27-5(월곡동)1001994음용수35.169245126.8065722022-12-31
89정부지원첨단청소년수련관쌍암동광주광역시 광산구 첨단중앙로182번길 39(쌍암동)1001998음용수35.222228126.8442512022-12-31
910정부지원임곡동주민센터임곡동광주광역시 광산구 고봉로 788(임곡동)1501997음용수35.219598126.744792022-12-31
연번구분시설명행정동구분주소1일생산용량(톤)지정연도비고위도경도데이터기준일자
5455공공시설임곡초등학교임곡동광주광역시 광산구 하림길 26(임곡동)1001994생활용수35.221112126.7468262022-12-31
5556공공시설더하기센터(본량중)남산동광주광역시 광산구 용진로 303(남산동)1001991생활용수35.180489126.720912022-12-31
5657공공시설평동초등학교옥동광주광역시 광산구 평동로741 (옥동)1201993생활용수35.126992126.7550482022-12-31
5758민간시설하남금호타운월곡동광주광역시 광산구 월곡산정로 80(월곡동)3901992생활용수35.165775126.8111792022-12-31
5859민간시설한성2차아파트월곡동광주광역시 광산구 월곡산정로 96-21(월곡동)1901991생활용수35.164395126.8131142022-12-31
5960민간시설한두레농산물센터수완동광주광역시 광산구 왕버들로 207(수완동) 1호공1502007생활용수35.19941126.8333592022-12-31
6061민간시설한두레농산물센터수완동광주광역시 광산구 왕버들로 207(수완동) 2호공1502007생활용수35.19941126.8333592022-12-31
6162민간시설건영APT월계광주광역시 광산구 첨단중앙로181번길 104(월계)1501999생활용수35.218746126.8289042022-12-31
6263정부지원광산문화예술회관송정동광주광역시 광산구 광산로 68번길 13(송정동)3001989생활용수35.140501126.7992322022-12-31
6364정부지원운남주공5단지운남동광주광역시 광산구 하남대로 248-10(운남동)1402008음용수35.179062126.81892022-12-31