Overview

Dataset statistics

Number of variables13
Number of observations77
Missing cells48
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory109.7 B

Variable types

Numeric3
Categorical6
Text2
Unsupported2

Dataset

Description민방위비상급수시설목록177월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202919

Alerts

시설구분 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
수중모터 (hp) is highly overall correlated with 연번 and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 연번 and 7 other fieldsHigh correlation
활용 (음용,생활) is highly overall correlated with 시군명High correlation
개방여부 is highly overall correlated with 시군명High correlation
부대시설 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
일일 생산량(톤) is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
설치년도 is highly overall correlated with 시군명High correlation
시군명 is highly imbalanced (90.0%)Imbalance
시설구분 is highly imbalanced (55.8%)Imbalance
수중모터 (hp) is highly imbalanced (60.7%)Imbalance
연번 has 1 (1.3%) missing valuesMissing
명 칭 has 1 (1.3%) missing valuesMissing
일일 생산량(톤) has 1 (1.3%) missing valuesMissing
설치년도 has 1 (1.3%) missing valuesMissing
심도(m) has 7 (9.1%) missing valuesMissing
자가발전기 (kw) has 37 (48.1%) missing valuesMissing
심도(m) is an unsupported type, check if it needs cleaning or further analysisUnsupported
자가발전기 (kw) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 02:42:19.334062
Analysis finished2024-03-14 02:42:21.063008
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct76
Distinct (%)100.0%
Missing1
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean38.5
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T11:42:21.124403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.75
Q119.75
median38.5
Q357.25
95-th percentile72.25
Maximum76
Range75
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation22.083176
Coefficient of variation (CV)0.57358899
Kurtosis-1.2
Mean38.5
Median Absolute Deviation (MAD)19
Skewness0
Sum2926
Variance487.66667
MonotonicityStrictly increasing
2024-03-14T11:42:21.252162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.3%
50 1
 
1.3%
57 1
 
1.3%
56 1
 
1.3%
55 1
 
1.3%
54 1
 
1.3%
53 1
 
1.3%
52 1
 
1.3%
51 1
 
1.3%
49 1
 
1.3%
Other values (66) 66
85.7%
ValueCountFrequency (%)
1 1
1.3%
2 1
1.3%
3 1
1.3%
4 1
1.3%
5 1
1.3%
6 1
1.3%
7 1
1.3%
8 1
1.3%
9 1
1.3%
10 1
1.3%
ValueCountFrequency (%)
76 1
1.3%
75 1
1.3%
74 1
1.3%
73 1
1.3%
72 1
1.3%
71 1
1.3%
70 1
1.3%
69 1
1.3%
68 1
1.3%
67 1
1.3%

시군명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size748.0 B
전주시
76 
<NA>
 
1

Length

Max length4
Median length3
Mean length3.012987
Min length3

Unique

Unique1 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 76
98.7%
<NA> 1
 
1.3%

Length

2024-03-14T11:42:21.389314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:42:21.470855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전주시 76
98.7%
na 1
 
1.3%

시설구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size748.0 B
공공지정
63 
정부지원
10 
자치단체
 
3
<NA>
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
공공지정 63
81.8%
정부지원 10
 
13.0%
자치단체 3
 
3.9%
<NA> 1
 
1.3%

Length

2024-03-14T11:42:21.547679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:42:21.632136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공지정 63
81.8%
정부지원 10
 
13.0%
자치단체 3
 
3.9%
na 1
 
1.3%

명 칭
Text

MISSING 

Distinct75
Distinct (%)98.7%
Missing1
Missing (%)1.3%
Memory size748.0 B
2024-03-14T11:42:21.813881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.1973684
Min length3

Characters and Unicode

Total characters471
Distinct characters146
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)97.4%

Sample

1st row서곡어린이공원
2nd row삼천약수공원
3rd row평화1공원
4th row평화주공3단지
5th row풍년주택
ValueCountFrequency (%)
전주콩나물영농조합 2
 
2.6%
아중하인교공원 1
 
1.3%
우석고등학교 1
 
1.3%
전주소년원 1
 
1.3%
전주용소초등학교 1
 
1.3%
전주솔빛중학교 1
 
1.3%
전북대학교 1
 
1.3%
인후초등학교 1
 
1.3%
농협도지회 1
 
1.3%
대우초원아파트 1
 
1.3%
Other values (66) 66
85.7%
2024-03-14T11:42:22.122356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 29
 
6.2%
학 24
 
5.1%
전 23
 
4.9%
주 23
 
4.9%
등 17
 
3.6%
고 12
 
2.5%
원 11
 
2.3%
파 11
 
2.3%
아 11
 
2.3%
초 11
 
2.3%
Other values (136) 299
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 460
97.7%
Decimal Number 6
 
1.3%
Other Symbol 2
 
0.4%
Space Separator 1
 
0.2%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 29
 
6.3%
학 24
 
5.2%
전 23
 
5.0%
주 23
 
5.0%
등 17
 
3.7%
고 12
 
2.6%
원 11
 
2.4%
파 11
 
2.4%
아 11
 
2.4%
초 11
 
2.4%
Other values (129) 288
62.6%
Decimal Number
ValueCountFrequency (%)
1 4
66.7%
3 1
 
16.7%
6 1
 
16.7%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 462
98.1%
Common 9
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 29
 
6.3%
학 24
 
5.2%
전 23
 
5.0%
주 23
 
5.0%
등 17
 
3.7%
고 12
 
2.6%
원 11
 
2.4%
파 11
 
2.4%
아 11
 
2.4%
초 11
 
2.4%
Other values (130) 290
62.8%
Common
ValueCountFrequency (%)
1 4
44.4%
3 1
 
11.1%
1
 
11.1%
) 1
 
11.1%
( 1
 
11.1%
6 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 460
97.7%
ASCII 9
 
1.9%
None 2
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 29
 
6.3%
학 24
 
5.2%
전 23
 
5.0%
주 23
 
5.0%
등 17
 
3.7%
고 12
 
2.6%
원 11
 
2.4%
파 11
 
2.4%
아 11
 
2.4%
초 11
 
2.4%
Other values (129) 288
62.6%
ASCII
ValueCountFrequency (%)
1 4
44.4%
3 1
 
11.1%
1
 
11.1%
) 1
 
11.1%
( 1
 
11.1%
6 1
 
11.1%
None
ValueCountFrequency (%)
㈜ 2
100.0%

위 치
Text

Distinct76
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size748.0 B
2024-03-14T11:42:22.418066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.4155844
Min length2

Characters and Unicode

Total characters648
Distinct characters109
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)97.4%

Sample

1st row완산구 서곡로 22
2nd row완산구 거마서로 54
3rd row완산구 평화16길16
4th row완산구 덕적골2길 10
5th row완산구 장승배기로 292-11
ValueCountFrequency (%)
완산구 14
 
8.3%
백제대로 5
 
3.0%
장승배기로 3
 
1.8%
안덕원로 3
 
1.8%
천잠로 3
 
1.8%
22 3
 
1.8%
10 3
 
1.8%
38 3
 
1.8%
20 3
 
1.8%
서곡로 2
 
1.2%
Other values (116) 126
75.0%
2024-03-14T11:42:22.871735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93
 
14.4%
로 50
 
7.7%
2 43
 
6.6%
1 40
 
6.2%
3 29
 
4.5%
길 27
 
4.2%
5 25
 
3.9%
구 18
 
2.8%
산 17
 
2.6%
7 15
 
2.3%
Other values (99) 291
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 327
50.5%
Decimal Number 216
33.3%
Space Separator 94
 
14.5%
Dash Punctuation 9
 
1.4%
Other Punctuation 1
 
0.2%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 50
 
15.3%
길 27
 
8.3%
구 18
 
5.5%
산 17
 
5.2%
완 14
 
4.3%
대 11
 
3.4%
천 9
 
2.8%
기 7
 
2.1%
덕 6
 
1.8%
서 6
 
1.8%
Other values (84) 162
49.5%
Decimal Number
ValueCountFrequency (%)
2 43
19.9%
1 40
18.5%
3 29
13.4%
5 25
11.6%
7 15
 
6.9%
0 14
 
6.5%
6 14
 
6.5%
4 14
 
6.5%
9 12
 
5.6%
8 10
 
4.6%
Space Separator
ValueCountFrequency (%)
93
98.9%
  1
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 327
50.5%
Common 320
49.4%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 50
 
15.3%
길 27
 
8.3%
구 18
 
5.5%
산 17
 
5.2%
완 14
 
4.3%
대 11
 
3.4%
천 9
 
2.8%
기 7
 
2.1%
덕 6
 
1.8%
서 6
 
1.8%
Other values (84) 162
49.5%
Common
ValueCountFrequency (%)
93
29.1%
2 43
13.4%
1 40
12.5%
3 29
 
9.1%
5 25
 
7.8%
7 15
 
4.7%
0 14
 
4.4%
6 14
 
4.4%
4 14
 
4.4%
9 12
 
3.8%
Other values (4) 21
 
6.6%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 327
50.5%
ASCII 320
49.4%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
93
29.1%
2 43
13.4%
1 40
12.5%
3 29
 
9.1%
5 25
 
7.8%
7 15
 
4.7%
0 14
 
4.4%
6 14
 
4.4%
4 14
 
4.4%
9 12
 
3.8%
Other values (4) 21
 
6.6%
Hangul
ValueCountFrequency (%)
로 50
 
15.3%
길 27
 
8.3%
구 18
 
5.5%
산 17
 
5.2%
완 14
 
4.3%
대 11
 
3.4%
천 9
 
2.8%
기 7
 
2.1%
덕 6
 
1.8%
서 6
 
1.8%
Other values (84) 162
49.5%
None
ValueCountFrequency (%)
  1
100.0%

일일 생산량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)42.1%
Missing1
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean204.59211
Minimum80
Maximum2084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T11:42:22.979182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile100
Q1114
median150
Q3200
95-th percentile428.25
Maximum2084
Range2004
Interquartile range (IQR)86

Descriptive statistics

Standard deviation243.59788
Coefficient of variation (CV)1.1906514
Kurtosis48.50553
Mean204.59211
Median Absolute Deviation (MAD)41.5
Skewness6.4810425
Sum15549
Variance59339.925
MonotonicityNot monotonic
2024-03-14T11:42:23.079593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
150 14
18.2%
100 11
14.3%
200 8
 
10.4%
110 4
 
5.2%
120 4
 
5.2%
130 3
 
3.9%
180 3
 
3.9%
230 2
 
2.6%
205 2
 
2.6%
114 2
 
2.6%
Other values (22) 23
29.9%
ValueCountFrequency (%)
80 1
 
1.3%
90 2
 
2.6%
100 11
14.3%
110 4
 
5.2%
114 2
 
2.6%
115 1
 
1.3%
120 4
 
5.2%
130 3
 
3.9%
140 1
 
1.3%
150 14
18.2%
ValueCountFrequency (%)
2084 1
1.3%
752 1
1.3%
500 1
1.3%
483 1
1.3%
410 1
1.3%
400 1
1.3%
340 1
1.3%
320 1
1.3%
306 1
1.3%
260 1
1.3%

설치년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)31.6%
Missing1
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean2002.8421
Minimum1982
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T11:42:23.170545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile1989.75
Q12000
median2004
Q32007
95-th percentile2012
Maximum2014
Range32
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.9532777
Coefficient of variation (CV)0.0034717054
Kurtosis0.8576062
Mean2002.8421
Median Absolute Deviation (MAD)3.5
Skewness-0.92766802
Sum152216
Variance48.34807
MonotonicityNot monotonic
2024-03-14T11:42:23.259207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2004 10
13.0%
2003 9
11.7%
2006 8
 
10.4%
2011 5
 
6.5%
1996 4
 
5.2%
2005 4
 
5.2%
2010 4
 
5.2%
1994 4
 
5.2%
2000 3
 
3.9%
2007 3
 
3.9%
Other values (14) 22
28.6%
ValueCountFrequency (%)
1982 1
 
1.3%
1984 1
 
1.3%
1986 2
2.6%
1991 1
 
1.3%
1992 1
 
1.3%
1994 4
5.2%
1995 2
2.6%
1996 4
5.2%
1999 2
2.6%
2000 3
3.9%
ValueCountFrequency (%)
2014 2
 
2.6%
2013 1
 
1.3%
2012 2
 
2.6%
2011 5
6.5%
2010 4
5.2%
2009 2
 
2.6%
2008 1
 
1.3%
2007 3
 
3.9%
2006 8
10.4%
2005 4
5.2%

심도(m)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)9.1%
Memory size748.0 B

부대시설
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size748.0 B
-
33 
 
30 
<NA>
10 
자가
 
2
비상발전기
 
1

Length

Max length5
Median length4
Mean length1.8701299
Min length1

Unique

Unique2 ?
Unique (%)2.6%

Sample

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

Common Values

ValueCountFrequency (%)
- 33
42.9%
  30
39.0%
<NA> 10
 
13.0%
자가 2
 
2.6%
비상발전기 1
 
1.3%
-- 1
 
1.3%

Length

2024-03-14T11:42:23.392399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:42:23.516372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
34
72.3%
na 10
 
21.3%
자가 2
 
4.3%
비상발전기 1
 
2.1%

활용 (음용,생활)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size748.0 B
생활
44 
음용
32 
<NA>
 
1

Length

Max length4
Median length2
Mean length2.025974
Min length2

Unique

Unique1 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
생활 44
57.1%
음용 32
41.6%
<NA> 1
 
1.3%

Length

2024-03-14T11:42:23.646466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:42:23.735632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활 44
57.1%
음용 32
41.6%
na 1
 
1.3%

개방여부
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size748.0 B
○
49 
×
26 
비상시
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0649351
Min length1

Unique

Unique2 ?
Unique (%)2.6%

Sample

1st row○
2nd row○
3rd row○
4th row○
5th row○

Common Values

ValueCountFrequency (%)
○ 49
63.6%
× 26
33.8%
비상시 1
 
1.3%
<NA> 1
 
1.3%

Length

2024-03-14T11:42:23.822180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:42:23.910535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
○ 49
63.6%
× 26
33.8%
비상시 1
 
1.3%
na 1
 
1.3%

자가발전기 (kw)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)48.1%
Memory size748.0 B

수중모터 (hp)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size748.0 B
<NA>
65 
3.0
9 
7.5
 
2
8.0
 
1

Length

Max length4
Median length4
Mean length3.8441558
Min length3

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row3.0
2nd row3.0
3rd row8.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
<NA> 65
84.4%
3.0 9
 
11.7%
7.5 2
 
2.6%
8.0 1
 
1.3%

Length

2024-03-14T11:42:23.996374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:42:24.085743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 65
84.4%
3.0 9
 
11.7%
7.5 2
 
2.6%
8.0 1
 
1.3%

Interactions

2024-03-14T11:42:20.362249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:42:19.819672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:42:20.045638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:42:20.452895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:42:19.896105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:42:20.118707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:42:20.528152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:42:19.976502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:42:20.228905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:42:24.146983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설구분명 칭위 치일일 생산량(톤)설치년도부대시설활용 (음용,생활)개방여부수중모터 (hp)
연번1.0000.6651.0001.0000.0000.6360.8440.3720.0000.916
시설구분0.6651.0001.0001.0000.0000.5260.4250.2110.5020.000
명 칭1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위 치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
일일\n생산량(톤)0.0000.0001.0001.0001.0000.4790.0000.1780.0000.370
설치년도0.6360.5261.0001.0000.4791.0000.3680.1820.3370.000
부대시설0.8440.4251.0001.0000.0000.3681.0000.0590.0000.000
활용\n(음용,생활)0.3720.2111.0001.0000.1780.1820.0591.0000.0000.000
개방여부0.0000.5021.0001.0000.0000.3370.0000.0001.0000.807
수중모터\n(hp)0.9160.0001.0001.0000.3700.0000.0000.0000.8071.000
2024-03-14T11:42:24.251314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분수중모터 (hp)시군명활용 (음용,생활)개방여부부대시설
시설구분1.0000.0001.0000.3430.2030.504
수중모터\n(hp)0.0001.0001.0000.0000.4580.000
시군명1.0001.0001.0001.0001.0001.000
활용\n(음용,생활)0.3430.0001.0001.0000.0000.064
개방여부0.2030.4581.0000.0001.0000.000
부대시설0.5040.0001.0000.0640.0001.000
2024-03-14T11:42:24.644166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번일일 생산량(톤)설치년도시군명시설구분부대시설활용 (음용,생활)개방여부수중모터 (hp)
연번1.0000.3750.0881.0000.4890.4740.2810.1180.637
일일\n생산량(톤)0.3751.000-0.2421.0000.0000.0000.2120.0000.548
설치년도0.088-0.2421.0001.0000.3540.1360.0000.2120.000
시군명1.0001.0001.0001.0001.0001.0001.0001.0001.000
시설구분0.4890.0000.3541.0001.0000.5040.3430.2030.000
부대시설0.4740.0000.1361.0000.5041.0000.0640.0000.000
활용\n(음용,생활)0.2810.2120.0001.0000.3430.0641.0000.0000.000
개방여부0.1180.0000.2121.0000.2030.0000.0001.0000.458
수중모터\n(hp)0.6370.5480.0001.0000.0000.0000.0000.4581.000

Missing values

2024-03-14T11:42:20.646100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:42:20.807900image/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.
2024-03-14T11:42:20.945100image/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

연번시군명시설구분명 칭위 치일일 생산량(톤)설치년도심도(m)부대시설활용 (음용,생활)개방여부자가발전기 (kw)수중모터 (hp)
01전주시정부지원서곡어린이공원완산구 서곡로 221002010106음용○3.0
12전주시정부지원삼천약수공원완산구 거마서로 54901996101음용○3.0
23전주시정부지원평화1공원완산구 평화16길162302004110음용○268.0
34전주시정부지원평화주공3단지완산구 덕적골2길 10901996161음용○3.0
45전주시정부지원풍년주택완산구 장승배기로 292-11801994152음용○3.0
56전주시정부지원기린연립완산구 견훤로 100-10160198676음용○103.0
67전주시자치단체전통문화센터완산구 전주천동로 201502004102<NA>생활×3503.0
78전주시자치단체정혜사완산구 외칠봉1길 222001995150<NA>음용○NaN3.0
89전주시공공지정삼천초등학교완산구 성지산로 381502004230-생활○<NA>
910전주시공공지정삼천탕완산구 백제대로 691502006110비상발전기생활×<NA>
연번시군명시설구분명 칭위 치일일 생산량(톤)설치년도심도(m)부대시설활용 (음용,생활)개방여부자가발전기 (kw)수중모터 (hp)
6768전주시공공지정전주콩나물영농조합수리재길 131802010NaN<NA>음용×NaN<NA>
6869전주시공공지정(유)영진기린대로 3584101994생활×NaN<NA>
6970전주시공공지정㈜전주주조신성길 25-311002010음용○NaN<NA>
7071전주시공공지정시온성교회공북5길 551002011생활○NaN<NA>
7172전주시자치단체전북교육문화회관안덕원로 711501999NaN<NA>생활×NaN<NA>
7273전주시공공지정㈜메가월드 스파동부대로 1229, B동 5층5002009생활×NaN<NA>
7374전주시공공지정전주교대서학로 5020841996NaN<NA>생활○NaN<NA>
7475전주시공공지정화산체육관백제대로 3107522009NaN<NA>생활○NaN<NA>
7576전주시공공지정전주비전대천잠로 2352431996NaN<NA>음용○NaN<NA>
76<NA><NA><NA><NA><NA><NA>NaN<NA><NA><NA>NaN<NA>