Overview

Dataset statistics

Number of variables13
Number of observations249
Missing cells815
Missing cells (%)25.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 KiB
Average record size in memory110.5 B

Variable types

Numeric4
Categorical5
Text3
Boolean1

Dataset

Description전북특별자치도의 민방위 비상급수시설에 관한 정보입니다.항목 : 시군명, 시설구분, 명칭, 위치, 일일생산량, 설치년도, 심도(m), 부대시설, 활용(음용, 생활), 개방여부, 자가발전기, 수중모터(hp) 등
Author전북특별자치도
URLhttps://www.data.go.kr/data/15059824/fileData.do

Alerts

시군명 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
개방여부 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
수중모터 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
자가발전기 is highly overall correlated with 일일생산량(톤) and 5 other fieldsHigh correlation
연번 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
일일생산량(톤) is highly overall correlated with 자가발전기 and 1 other fieldsHigh correlation
설치년도 is highly overall correlated with 시군명High correlation
심도(미터) is highly overall correlated with 시군명High correlation
시설구분 is highly overall correlated with 자가발전기High correlation
활용(음용_생활) is highly overall correlated with 연번 and 2 other fieldsHigh correlation
자가발전기 is highly imbalanced (92.2%)Imbalance
수중모터 is highly imbalanced (83.6%)Imbalance
명 칭 has 6 (2.4%) missing valuesMissing
위 치 has 11 (4.4%) missing valuesMissing
설치년도 has 173 (69.5%) missing valuesMissing
심도(미터) has 206 (82.7%) missing valuesMissing
부대시설 has 246 (98.8%) missing valuesMissing
개방여부 has 173 (69.5%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 22:51:48.791607
Analysis finished2024-03-14 22:51:55.446682
Duration6.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct249
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125
Minimum1
Maximum249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T07:51:55.871282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.4
Q163
median125
Q3187
95-th percentile236.6
Maximum249
Range248
Interquartile range (IQR)124

Descriptive statistics

Standard deviation72.024301
Coefficient of variation (CV)0.57619441
Kurtosis-1.2
Mean125
Median Absolute Deviation (MAD)62
Skewness0
Sum31125
Variance5187.5
MonotonicityStrictly increasing
2024-03-15T07:51:56.326389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
172 1
 
0.4%
159 1
 
0.4%
160 1
 
0.4%
161 1
 
0.4%
162 1
 
0.4%
163 1
 
0.4%
164 1
 
0.4%
165 1
 
0.4%
166 1
 
0.4%
Other values (239) 239
96.0%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
249 1
0.4%
248 1
0.4%
247 1
0.4%
246 1
0.4%
245 1
0.4%
244 1
0.4%
243 1
0.4%
242 1
0.4%
241 1
0.4%
240 1
0.4%

시군명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
전주시
76 
군산시
52 
익산시
49 
완주군
13 
정읍시
12 
Other values (9)
47 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 76
30.5%
군산시 52
20.9%
익산시 49
19.7%
완주군 13
 
5.2%
정읍시 12
 
4.8%
남원시 10
 
4.0%
김제시 10
 
4.0%
무주군 6
 
2.4%
순창군 5
 
2.0%
임실군 4
 
1.6%
Other values (4) 12
 
4.8%

Length

2024-03-15T07:51:56.741095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 76
30.5%
군산시 52
20.9%
익산시 49
19.7%
완주군 13
 
5.2%
정읍시 12
 
4.8%
남원시 10
 
4.0%
김제시 10
 
4.0%
무주군 6
 
2.4%
순창군 5
 
2.0%
임실군 4
 
1.6%
Other values (4) 12
 
4.8%

시설구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
공공지정
150 
정부지원
58 
공공용지정
32 
자치단체
 
5
지자체지원
 
3

Length

Max length5
Median length4
Mean length4.1365462
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
공공지정 150
60.2%
정부지원 58
 
23.3%
공공용지정 32
 
12.9%
자치단체 5
 
2.0%
지자체지원 3
 
1.2%
지자체 1
 
0.4%

Length

2024-03-15T07:51:57.164167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:51:57.514804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공지정 150
60.2%
정부지원 58
 
23.3%
공공용지정 32
 
12.9%
자치단체 5
 
2.0%
지자체지원 3
 
1.2%
지자체 1
 
0.4%

명 칭
Text

MISSING 

Distinct236
Distinct (%)97.1%
Missing6
Missing (%)2.4%
Memory size2.1 KiB
2024-03-15T07:51:58.492751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.1440329
Min length3

Characters and Unicode

Total characters1493
Distinct characters264
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)94.2%

Sample

1st row서곡어린이공원
2nd row삼천약수공원
3rd row평화1공원
4th row평화주공3단지
5th row풍년주택
ValueCountFrequency (%)
삼성아파트 2
 
0.8%
공설운동장 2
 
0.8%
수정사우나 2
 
0.8%
지곡초등학교 2
 
0.8%
현대아파트 2
 
0.8%
제일1차아파트 2
 
0.8%
전주콩나물영농조합 2
 
0.8%
마동 2
 
0.8%
화이트사우나(1 1
 
0.4%
한솔홈데코익산공장 1
 
0.4%
Other values (239) 239
93.0%
2024-03-15T07:51:59.693404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 78
 
5.2%
학 70
 
4.7%
등 55
 
3.7%
주 37
 
2.5%
초 37
 
2.5%
공 33
 
2.2%
원 33
 
2.2%
고 28
 
1.9%
아 27
 
1.8%
파 27
 
1.8%
Other values (254) 1068
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1413
94.6%
Decimal Number 21
 
1.4%
Close Punctuation 16
 
1.1%
Open Punctuation 16
 
1.1%
Space Separator 14
 
0.9%
Uppercase Letter 6
 
0.4%
Other Punctuation 4
 
0.3%
Other Symbol 2
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 78
 
5.5%
학 70
 
5.0%
등 55
 
3.9%
주 37
 
2.6%
초 37
 
2.6%
공 33
 
2.3%
원 33
 
2.3%
고 28
 
2.0%
아 27
 
1.9%
파 27
 
1.9%
Other values (236) 988
69.9%
Decimal Number
ValueCountFrequency (%)
1 9
42.9%
2 5
23.8%
3 2
 
9.5%
5 1
 
4.8%
4 1
 
4.8%
6 1
 
4.8%
0 1
 
4.8%
9 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
L 2
33.3%
G 2
33.3%
V 1
16.7%
D 1
16.7%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 4
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1415
94.8%
Common 72
 
4.8%
Latin 6
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 78
 
5.5%
학 70
 
4.9%
등 55
 
3.9%
주 37
 
2.6%
초 37
 
2.6%
공 33
 
2.3%
원 33
 
2.3%
고 28
 
2.0%
아 27
 
1.9%
파 27
 
1.9%
Other values (237) 990
70.0%
Common
ValueCountFrequency (%)
) 16
22.2%
( 16
22.2%
14
19.4%
1 9
12.5%
2 5
 
6.9%
@ 4
 
5.6%
3 2
 
2.8%
5 1
 
1.4%
4 1
 
1.4%
6 1
 
1.4%
Other values (3) 3
 
4.2%
Latin
ValueCountFrequency (%)
L 2
33.3%
G 2
33.3%
V 1
16.7%
D 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1413
94.6%
ASCII 78
 
5.2%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 78
 
5.5%
학 70
 
5.0%
등 55
 
3.9%
주 37
 
2.6%
초 37
 
2.6%
공 33
 
2.3%
원 33
 
2.3%
고 28
 
2.0%
아 27
 
1.9%
파 27
 
1.9%
Other values (236) 988
69.9%
ASCII
ValueCountFrequency (%)
) 16
20.5%
( 16
20.5%
14
17.9%
1 9
11.5%
2 5
 
6.4%
@ 4
 
5.1%
L 2
 
2.6%
G 2
 
2.6%
3 2
 
2.6%
V 1
 
1.3%
Other values (7) 7
9.0%
None
ValueCountFrequency (%)
㈜ 2
100.0%

위 치
Text

MISSING 

Distinct233
Distinct (%)97.9%
Missing11
Missing (%)4.4%
Memory size2.1 KiB
2024-03-15T07:52:01.216832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.113445
Min length3

Characters and Unicode

Total characters2407
Distinct characters193
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

Unique228 ?
Unique (%)95.8%

Sample

1st row완산구 서곡로 22
2nd row완산구 거마서로 54
3rd row완산구 평화16길16
4th row완산구 덕적골2길 10
5th row완산구 장승배기로 292-11
ValueCountFrequency (%)
완산구 14
 
2.4%
완주군 13
 
2.2%
봉동읍 9
 
1.5%
영등동 9
 
1.5%
마동 6
 
1.0%
김제시 6
 
1.0%
백제대로 5
 
0.9%
22 5
 
0.9%
10 5
 
0.9%
중앙로 5
 
0.9%
Other values (385) 506
86.8%
2024-03-15T07:52:03.078574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
363
 
15.1%
1 146
 
6.1%
로 142
 
5.9%
2 114
 
4.7%
길 95
 
3.9%
동 86
 
3.6%
3 84
 
3.5%
5 66
 
2.7%
9 52
 
2.2%
7 52
 
2.2%
Other values (183) 1207
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1209
50.2%
Decimal Number 701
29.1%
Space Separator 363
 
15.1%
Close Punctuation 46
 
1.9%
Open Punctuation 46
 
1.9%
Dash Punctuation 39
 
1.6%
Other Punctuation 2
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 142
 
11.7%
길 95
 
7.9%
동 86
 
7.1%
읍 34
 
2.8%
산 32
 
2.6%
완 27
 
2.2%
리 26
 
2.2%
구 24
 
2.0%
서 20
 
1.7%
대 18
 
1.5%
Other values (167) 705
58.3%
Decimal Number
ValueCountFrequency (%)
1 146
20.8%
2 114
16.3%
3 84
12.0%
5 66
9.4%
9 52
 
7.4%
7 52
 
7.4%
0 51
 
7.3%
4 48
 
6.8%
6 46
 
6.6%
8 42
 
6.0%
Space Separator
ValueCountFrequency (%)
363
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1209
50.2%
Common 1197
49.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 142
 
11.7%
길 95
 
7.9%
동 86
 
7.1%
읍 34
 
2.8%
산 32
 
2.6%
완 27
 
2.2%
리 26
 
2.2%
구 24
 
2.0%
서 20
 
1.7%
대 18
 
1.5%
Other values (167) 705
58.3%
Common
ValueCountFrequency (%)
363
30.3%
1 146
12.2%
2 114
 
9.5%
3 84
 
7.0%
5 66
 
5.5%
9 52
 
4.3%
7 52
 
4.3%
0 51
 
4.3%
4 48
 
4.0%
6 46
 
3.8%
Other values (5) 175
14.6%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1209
50.2%
ASCII 1198
49.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
363
30.3%
1 146
12.2%
2 114
 
9.5%
3 84
 
7.0%
5 66
 
5.5%
9 52
 
4.3%
7 52
 
4.3%
0 51
 
4.3%
4 48
 
4.0%
6 46
 
3.8%
Other values (6) 176
14.7%
Hangul
ValueCountFrequency (%)
로 142
 
11.7%
길 95
 
7.9%
동 86
 
7.1%
읍 34
 
2.8%
산 32
 
2.6%
완 27
 
2.2%
리 26
 
2.2%
구 24
 
2.0%
서 20
 
1.7%
대 18
 
1.5%
Other values (167) 705
58.3%

일일생산량(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.46145
Minimum42
Maximum2084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T07:52:03.473588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile75
Q190
median120
Q3180
95-th percentile295.6
Maximum2084
Range2042
Interquartile range (IQR)90

Descriptive statistics

Standard deviation149.8909
Coefficient of variation (CV)0.96416769
Kurtosis111.85896
Mean155.46145
Median Absolute Deviation (MAD)30
Skewness9.1721062
Sum38709.9
Variance22467.283
MonotonicityNot monotonic
2024-03-15T07:52:03.832329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150.0 34
13.7%
90.0 34
13.7%
100.0 31
12.4%
200.0 23
 
9.2%
120.0 13
 
5.2%
80.0 13
 
5.2%
110.0 10
 
4.0%
70.0 7
 
2.8%
250.0 6
 
2.4%
170.0 5
 
2.0%
Other values (51) 73
29.3%
ValueCountFrequency (%)
42.0 1
 
0.4%
50.0 2
 
0.8%
60.0 1
 
0.4%
66.0 1
 
0.4%
70.0 7
2.8%
75.0 2
 
0.8%
78.0 3
 
1.2%
80.0 13
5.2%
82.0 2
 
0.8%
88.0 1
 
0.4%
ValueCountFrequency (%)
2084.0 1
0.4%
752.0 1
0.4%
550.0 1
0.4%
500.0 2
0.8%
483.0 1
0.4%
410.0 1
0.4%
400.0 1
0.4%
340.0 1
0.4%
325.0 1
0.4%
324.0 1
0.4%

설치년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)31.6%
Missing173
Missing (%)69.5%
Infinite0
Infinite (%)0.0%
Mean2002.8421
Minimum1982
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T07:52:04.073869image/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
2024-03-15T07:52:04.286376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2004 10
 
4.0%
2003 9
 
3.6%
2006 8
 
3.2%
2011 5
 
2.0%
1994 4
 
1.6%
1996 4
 
1.6%
2010 4
 
1.6%
2005 4
 
1.6%
2001 3
 
1.2%
2000 3
 
1.2%
Other values (14) 22
 
8.8%
(Missing) 173
69.5%
ValueCountFrequency (%)
1982 1
 
0.4%
1984 1
 
0.4%
1986 2
0.8%
1991 1
 
0.4%
1992 1
 
0.4%
1994 4
1.6%
1995 2
0.8%
1996 4
1.6%
1999 2
0.8%
2000 3
1.2%
ValueCountFrequency (%)
2014 2
 
0.8%
2013 1
 
0.4%
2012 2
 
0.8%
2011 5
2.0%
2010 4
1.6%
2009 2
 
0.8%
2008 1
 
0.4%
2007 3
 
1.2%
2006 8
3.2%
2005 4
1.6%

심도(미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)51.2%
Missing206
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean122.88372
Minimum50
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T07:52:04.504983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile75.1
Q1100
median110
Q3150
95-th percentile179
Maximum230
Range180
Interquartile range (IQR)50

Descriptive statistics

Standard deviation34.989256
Coefficient of variation (CV)0.28473468
Kurtosis0.79703585
Mean122.88372
Median Absolute Deviation (MAD)20
Skewness0.58186537
Sum5284
Variance1224.2481
MonotonicityNot monotonic
2024-03-15T07:52:04.798764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
150 7
 
2.8%
100 6
 
2.4%
110 5
 
2.0%
130 4
 
1.6%
102 2
 
0.8%
180 2
 
0.8%
105 2
 
0.8%
93 1
 
0.4%
153 1
 
0.4%
79 1
 
0.4%
Other values (12) 12
 
4.8%
(Missing) 206
82.7%
ValueCountFrequency (%)
50 1
 
0.4%
70 1
 
0.4%
75 1
 
0.4%
76 1
 
0.4%
79 1
 
0.4%
93 1
 
0.4%
100 6
2.4%
101 1
 
0.4%
102 2
 
0.8%
105 2
 
0.8%
ValueCountFrequency (%)
230 1
 
0.4%
180 2
 
0.8%
170 1
 
0.4%
161 1
 
0.4%
154 1
 
0.4%
153 1
 
0.4%
152 1
 
0.4%
150 7
2.8%
130 4
1.6%
120 1
 
0.4%

부대시설
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing246
Missing (%)98.8%
Memory size2.1 KiB
2024-03-15T07:52:05.203357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length3
Min length2

Characters and Unicode

Total characters9
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row비상발전기
2nd row자가
3rd row자가
ValueCountFrequency (%)
자가 2
66.7%
비상발전기 1
33.3%
2024-03-15T07:52:05.932284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 2
22.2%
가 2
22.2%
비 1
11.1%
상 1
11.1%
발 1
11.1%
전 1
11.1%
기 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 2
22.2%
가 2
22.2%
비 1
11.1%
상 1
11.1%
발 1
11.1%
전 1
11.1%
기 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 2
22.2%
가 2
22.2%
비 1
11.1%
상 1
11.1%
발 1
11.1%
전 1
11.1%
기 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 2
22.2%
가 2
22.2%
비 1
11.1%
상 1
11.1%
발 1
11.1%
전 1
11.1%
기 1
11.1%

활용(음용_생활)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
음용
102 
생활
86 
생활용수
35 
음용수
19 
식수
 
6

Length

Max length4
Median length2
Mean length2.3654618
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
음용 102
41.0%
생활 86
34.5%
생활용수 35
 
14.1%
음용수 19
 
7.6%
식수 6
 
2.4%
<NA> 1
 
0.4%

Length

2024-03-15T07:52:06.361493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:52:06.712786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
음용 102
41.0%
생활 86
34.5%
생활용수 35
 
14.1%
음용수 19
 
7.6%
식수 6
 
2.4%
na 1
 
0.4%

개방여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)2.6%
Missing173
Missing (%)69.5%
Memory size626.0 B
True
50 
False
26 
(Missing)
173 
ValueCountFrequency (%)
True 50
 
20.1%
False 26
 
10.4%
(Missing) 173
69.5%
2024-03-15T07:52:06.985436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

자가발전기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
244 
10
 
2
26
 
1
350
 
1
20
 
1

Length

Max length4
Median length4
Mean length3.9638554
Min length2

Unique

Unique3 ?
Unique (%)1.2%

Sample

1st row<NA>
2nd row<NA>
3rd row26
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 244
98.0%
10 2
 
0.8%
26 1
 
0.4%
350 1
 
0.4%
20 1
 
0.4%

Length

2024-03-15T07:52:07.362854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:52:07.718827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 244
98.0%
10 2
 
0.8%
26 1
 
0.4%
350 1
 
0.4%
20 1
 
0.4%

수중모터
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
237 
3.0
 
9
7.5
 
2
8.0
 
1

Length

Max length4
Median length4
Mean length3.9518072
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 237
95.2%
3.0 9
 
3.6%
7.5 2
 
0.8%
8.0 1
 
0.4%

Length

2024-03-15T07:52:08.084778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:52:08.410662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 237
95.2%
3.0 9
 
3.6%
7.5 2
 
0.8%
8.0 1
 
0.4%

Interactions

2024-03-15T07:51:53.108654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:49.985665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:51.096452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:52.123455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:53.393544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:50.223130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:51.351618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:52.295219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:53.666466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:50.493138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:51.628419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:52.570502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:53.920397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:50.841985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:51.901504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:51:52.833603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T07:52:08.630955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군명시설구분일일생산량(톤)설치년도심도(미터)부대시설활용(음용_생활)개방여부자가발전기수중모터
연번1.0000.9170.7140.0900.4020.3190.0000.8530.0000.0000.342
시군명0.9171.0000.7540.000NaNNaNNaN0.818NaNNaNNaN
시설구분0.7140.7541.0000.0000.5260.0000.0000.4020.1031.0000.000
일일생산량(톤)0.0900.0000.0001.0000.4760.000NaN0.0000.126NaN0.370
설치년도0.402NaN0.5260.4761.0000.4561.0000.1560.1200.7710.000
심도(미터)0.319NaN0.0000.0000.4561.0001.0000.0000.0000.9130.135
부대시설0.000NaN0.000NaN1.0001.0001.0000.0000.000NaNNaN
활용(음용_생활)0.8530.8180.4020.0000.1560.0000.0001.0000.0001.0000.000
개방여부0.000NaN0.1030.1260.1200.0000.0000.0001.0001.0000.000
자가발전기0.000NaN1.000NaN0.7710.913NaN1.0001.0001.0001.000
수중모터0.342NaN0.0000.3700.0000.135NaN0.0000.0001.0001.000
2024-03-15T07:52:08.974802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
활용(음용_생활)시설구분시군명개방여부수중모터자가발전기
활용(음용_생활)1.0000.2850.5920.0000.0000.577
시설구분0.2851.0000.4900.1690.0000.577
시군명0.5920.4901.0001.0001.0001.000
개방여부0.0000.1691.0001.0000.0000.577
수중모터0.0000.0001.0000.0001.0000.577
자가발전기0.5770.5771.0000.5770.5771.000
2024-03-15T07:52:09.266381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번일일생산량(톤)설치년도심도(미터)시군명시설구분활용(음용_생활)개방여부자가발전기수중모터
연번1.000-0.1300.088-0.0040.6970.4760.5170.0000.0000.508
일일생산량(톤)-0.1301.000-0.242-0.0420.0000.0000.0000.1491.0000.548
설치년도0.088-0.2421.000-0.1191.0000.3540.0000.1170.0000.000
심도(미터)-0.004-0.042-0.1191.0001.0000.0000.0000.0000.0000.000
시군명0.6970.0001.0001.0001.0000.4900.5921.0001.0001.000
시설구분0.4760.0000.3540.0000.4901.0000.2850.1690.5770.000
활용(음용_생활)0.5170.0000.0000.0000.5920.2851.0000.0000.5770.000
개방여부0.0000.1490.1170.0001.0000.1690.0001.0000.5770.000
자가발전기0.0001.0000.0000.0001.0000.5770.5770.5771.0000.577
수중모터0.5080.5480.0000.0001.0000.0000.0000.0000.5771.000

Missing values

2024-03-15T07:51:54.300983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T07:51:54.781042image/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.
2024-03-15T07:51:55.196345image/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 치일일생산량(톤)설치년도심도(미터)부대시설활용(음용_생활)개방여부자가발전기수중모터
01전주시정부지원서곡어린이공원완산구 서곡로 22100.02010106<NA>음용Y<NA>3.0
12전주시정부지원삼천약수공원완산구 거마서로 5490.01996101<NA>음용Y<NA>3.0
23전주시정부지원평화1공원완산구 평화16길16230.02004110<NA>음용Y268.0
34전주시정부지원평화주공3단지완산구 덕적골2길 1090.01996161<NA>음용Y<NA>3.0
45전주시정부지원풍년주택완산구 장승배기로 292-1180.01994152<NA>음용Y<NA>3.0
56전주시정부지원기린연립완산구 견훤로 100-10160.0198676<NA>음용Y103.0
67전주시자치단체전통문화센터완산구 전주천동로 20150.02004102<NA>생활N3503.0
78전주시자치단체정혜사완산구 외칠봉1길 22200.01995150<NA>음용Y<NA>3.0
89전주시공공지정삼천초등학교완산구 성지산로 38150.02004230<NA>생활Y<NA><NA>
910전주시공공지정삼천탕완산구 백제대로 69150.02006110비상발전기생활N<NA><NA>
연번시군명시설구분명 칭위 치일일생산량(톤)설치년도심도(미터)부대시설활용(음용_생활)개방여부자가발전기수중모터
239240순창군정부지원도룡마을인계면 도룡리 1191150.0<NA><NA><NA>음용<NA><NA><NA>
240241순창군정부지원민속마을순창읍 백산리 265-66150.0<NA><NA><NA>음용<NA><NA><NA>
241242고창군정부지원고창읍성고창읍 읍내리 산9번지105.0<NA><NA><NA>음용<NA><NA><NA>
242243고창군정부지원상하수도사업소고창읍 중거리 당산로 74-12157.0<NA><NA><NA>음용<NA><NA><NA>
243244고창군정부지원(구)동초등학교고창읍 월산길6150.0<NA><NA><NA>음용<NA><NA><NA>
244245고창군정부지원복지회관고창읍 월곡14길 19220.0<NA><NA><NA>음용<NA><NA><NA>
245246부안군정부지원수도사업소부안읍 수정길 9-8 수도사업소150.0<NA><NA><NA>음용<NA><NA><NA>
246247부안군정부지원노인여성회관부안읍 매창로 127 노인여성회관120.0<NA><NA><NA>음용<NA><NA><NA>
247248부안군정부지원스포츠파크행안면 체육공원길 31 스포츠파크140.0<NA><NA><NA>음용<NA><NA><NA>
248249부안군정부지원예술회관부안읍 예술회관길 11 예술회관150.0<NA><NA><NA>생활<NA><NA><NA>