Overview

Dataset statistics

Number of variables18
Number of observations136
Missing cells108
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.7 KiB
Average record size in memory156.0 B

Variable types

Numeric8
Text3
Boolean1
Categorical6

Dataset

Description인천광역시 부평구 공중화장실의 화장실명, 소재지, 변기 수, 관리기관, 개방시간, 설치년도 등에 대한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15102495/fileData.do

Alerts

전화번호 is highly overall correlated with 남성용-대변기수 and 10 other fieldsHigh correlation
관리기관명 is highly overall correlated with 남성용-대변기수 and 10 other fieldsHigh correlation
순번 is highly overall correlated with 남성용-장애인용대변기수 and 1 other fieldsHigh correlation
남성용-대변기수 is highly overall correlated with 남성용-소변기수 and 6 other fieldsHigh correlation
남성용-소변기수 is highly overall correlated with 남성용-대변기수 and 5 other fieldsHigh correlation
남성용-장애인용대변기수 is highly overall correlated with 순번 and 7 other fieldsHigh correlation
남성용-장애인용소변기수 is highly overall correlated with 남성용-장애인용대변기수 and 7 other fieldsHigh correlation
여성용-대변기수 is highly overall correlated with 남성용-대변기수 and 7 other fieldsHigh correlation
여성용-장애인용대변기수 is highly overall correlated with 순번 and 7 other fieldsHigh correlation
설치년도 is highly overall correlated with 남녀공용화장실여부High correlation
남녀공용화장실여부 is highly overall correlated with 설치년도High correlation
남성용-어린이용대변기수 is highly overall correlated with 남성용-장애인용소변기수 and 4 other fieldsHigh correlation
남성용-어린이용소변기수 is highly overall correlated with 남성용-대변기수 and 8 other fieldsHigh correlation
여성용-어린이용대변기수 is highly overall correlated with 남성용-장애인용소변기수 and 4 other fieldsHigh correlation
개방시간 is highly overall correlated with 남성용-소변기수 and 7 other fieldsHigh correlation
남녀공용화장실여부 is highly imbalanced (88.9%)Imbalance
남성용-어린이용대변기수 is highly imbalanced (75.7%)Imbalance
남성용-어린이용소변기수 is highly imbalanced (78.1%)Imbalance
여성용-어린이용대변기수 is highly imbalanced (67.7%)Imbalance
소재지도로명주소 has 5 (3.7%) missing valuesMissing
소재지지번주소 has 61 (44.9%) missing valuesMissing
설치년도 has 42 (30.9%) missing valuesMissing
순번 has unique valuesUnique
화장실명 has unique valuesUnique
남성용-대변기수 has 4 (2.9%) zerosZeros
남성용-소변기수 has 3 (2.2%) zerosZeros
남성용-장애인용대변기수 has 66 (48.5%) zerosZeros
남성용-장애인용소변기수 has 91 (66.9%) zerosZeros
여성용-대변기수 has 2 (1.5%) zerosZeros
여성용-장애인용대변기수 has 67 (49.3%) zerosZeros

Reproduction

Analysis started2023-12-12 02:50:46.339545
Analysis finished2023-12-12 02:50:54.423462
Duration8.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.5
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:50:54.497189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.75
Q134.75
median68.5
Q3102.25
95-th percentile129.25
Maximum136
Range135
Interquartile range (IQR)67.5

Descriptive statistics

Standard deviation39.403892
Coefficient of variation (CV)0.57523929
Kurtosis-1.2
Mean68.5
Median Absolute Deviation (MAD)34
Skewness0
Sum9316
Variance1552.6667
MonotonicityStrictly increasing
2023-12-12T11:50:54.686507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
95 1
 
0.7%
89 1
 
0.7%
90 1
 
0.7%
91 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
94 1
 
0.7%
96 1
 
0.7%
70 1
 
0.7%
Other values (126) 126
92.6%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%
129 1
0.7%
128 1
0.7%
127 1
0.7%

화장실명
Text

UNIQUE 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T11:50:54.977269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15.5
Mean length7.6691176
Min length3

Characters and Unicode

Total characters1043
Distinct characters204
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)100.0%

Sample

1st row굴포공원
2nd row숲속공원
3rd row세모공원
4th row산곡역
5th row부평공원
ValueCountFrequency (%)
고객화장실 3
 
1.8%
sk에너지㈜ 3
 
1.8%
부평점 3
 
1.8%
롯데마트 2
 
1.2%
주식회사 2
 
1.2%
지에스칼텍스㈜ 2
 
1.2%
부평아트센터 2
 
1.2%
㈜태보에너지 2
 
1.2%
굴포공원 1
 
0.6%
현대오일뱅크(주)직영 1
 
0.6%
Other values (150) 150
87.7%
2023-12-12T11:50:55.412814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 55
 
5.3%
공 52
 
5.0%
소 48
 
4.6%
주 45
 
4.3%
유 43
 
4.1%
부 35
 
3.4%
35
 
3.4%
평 23
 
2.2%
장 19
 
1.8%
지 18
 
1.7%
Other values (194) 670
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 911
87.3%
Space Separator 35
 
3.4%
Uppercase Letter 20
 
1.9%
Close Punctuation 18
 
1.7%
Open Punctuation 18
 
1.7%
Other Symbol 17
 
1.6%
Decimal Number 16
 
1.5%
Lowercase Letter 8
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 55
 
6.0%
공 52
 
5.7%
소 48
 
5.3%
주 45
 
4.9%
유 43
 
4.7%
부 35
 
3.8%
평 23
 
2.5%
장 19
 
2.1%
지 18
 
2.0%
산 17
 
1.9%
Other values (175) 556
61.0%
Uppercase Letter
ValueCountFrequency (%)
C 4
20.0%
I 4
20.0%
K 3
15.0%
S 3
15.0%
L 2
10.0%
G 2
10.0%
P 2
10.0%
Decimal Number
ValueCountFrequency (%)
1 6
37.5%
7 4
25.0%
2 4
25.0%
0 2
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
s 2
25.0%
e 2
25.0%
f 2
25.0%
l 2
25.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 928
89.0%
Common 87
 
8.3%
Latin 28
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 55
 
5.9%
공 52
 
5.6%
소 48
 
5.2%
주 45
 
4.8%
유 43
 
4.6%
부 35
 
3.8%
평 23
 
2.5%
장 19
 
2.0%
지 18
 
1.9%
산 17
 
1.8%
Other values (176) 573
61.7%
Latin
ValueCountFrequency (%)
C 4
14.3%
I 4
14.3%
K 3
10.7%
S 3
10.7%
L 2
7.1%
s 2
7.1%
e 2
7.1%
G 2
7.1%
P 2
7.1%
f 2
7.1%
Common
ValueCountFrequency (%)
35
40.2%
) 18
20.7%
( 18
20.7%
1 6
 
6.9%
7 4
 
4.6%
2 4
 
4.6%
0 2
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 911
87.3%
ASCII 115
 
11.0%
None 17
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 55
 
6.0%
공 52
 
5.7%
소 48
 
5.3%
주 45
 
4.9%
유 43
 
4.7%
부 35
 
3.8%
평 23
 
2.5%
장 19
 
2.1%
지 18
 
2.0%
산 17
 
1.9%
Other values (175) 556
61.0%
ASCII
ValueCountFrequency (%)
35
30.4%
) 18
15.7%
( 18
15.7%
1 6
 
5.2%
7 4
 
3.5%
2 4
 
3.5%
C 4
 
3.5%
I 4
 
3.5%
K 3
 
2.6%
S 3
 
2.6%
Other values (8) 16
13.9%
None
ValueCountFrequency (%)
㈜ 17
100.0%

소재지도로명주소
Text

MISSING 

Distinct126
Distinct (%)96.2%
Missing5
Missing (%)3.7%
Memory size1.2 KiB
2023-12-12T11:50:55.680488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length28
Mean length20.877863
Min length15

Characters and Unicode

Total characters2735
Distinct characters89
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)92.4%

Sample

1st row인천광역시 부평구 주부토로 157(갈산동)
2nd row인천광역시 부평구 원적로269번길 28(산곡동)
3rd row인천광역시 부평구 부평문화로194번길 29(부개동)
4th row인천광역시 부평구 길주로 지하379, 산곡역 (산곡동)
5th row인천광역시 부평구 체육관로 161(삼산동)
ValueCountFrequency (%)
인천광역시 131
26.6%
부평구 130
26.4%
경인로 14
 
2.8%
부평대로 9
 
1.8%
경원대로 6
 
1.2%
장제로 6
 
1.2%
마장로 5
 
1.0%
평천로 4
 
0.8%
서달로 3
 
0.6%
부평북로 3
 
0.6%
Other values (161) 181
36.8%
2023-12-12T11:50:56.134874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
361
 
13.2%
부 170
 
6.2%
평 164
 
6.0%
천 149
 
5.4%
인 148
 
5.4%
구 137
 
5.0%
광 135
 
4.9%
역 132
 
4.8%
시 131
 
4.8%
로 128
 
4.7%
Other values (79) 1080
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1770
64.7%
Decimal Number 449
 
16.4%
Space Separator 361
 
13.2%
Close Punctuation 70
 
2.6%
Open Punctuation 70
 
2.6%
Dash Punctuation 13
 
0.5%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 170
9.6%
평 164
9.3%
천 149
 
8.4%
인 148
 
8.4%
구 137
 
7.7%
광 135
 
7.6%
역 132
 
7.5%
시 131
 
7.4%
로 128
 
7.2%
동 79
 
4.5%
Other values (64) 397
22.4%
Decimal Number
ValueCountFrequency (%)
1 96
21.4%
2 63
14.0%
3 44
9.8%
9 42
9.4%
4 42
9.4%
6 40
8.9%
5 37
 
8.2%
7 32
 
7.1%
0 28
 
6.2%
8 25
 
5.6%
Space Separator
ValueCountFrequency (%)
361
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1770
64.7%
Common 965
35.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 170
9.6%
평 164
9.3%
천 149
 
8.4%
인 148
 
8.4%
구 137
 
7.7%
광 135
 
7.6%
역 132
 
7.5%
시 131
 
7.4%
로 128
 
7.2%
동 79
 
4.5%
Other values (64) 397
22.4%
Common
ValueCountFrequency (%)
361
37.4%
1 96
 
9.9%
) 70
 
7.3%
( 70
 
7.3%
2 63
 
6.5%
3 44
 
4.6%
9 42
 
4.4%
4 42
 
4.4%
6 40
 
4.1%
5 37
 
3.8%
Other values (5) 100
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1770
64.7%
ASCII 965
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
361
37.4%
1 96
 
9.9%
) 70
 
7.3%
( 70
 
7.3%
2 63
 
6.5%
3 44
 
4.6%
9 42
 
4.4%
4 42
 
4.4%
6 40
 
4.1%
5 37
 
3.8%
Other values (5) 100
 
10.4%
Hangul
ValueCountFrequency (%)
부 170
9.6%
평 164
9.3%
천 149
 
8.4%
인 148
 
8.4%
구 137
 
7.7%
광 135
 
7.6%
역 132
 
7.5%
시 131
 
7.4%
로 128
 
7.2%
동 79
 
4.5%
Other values (64) 397
22.4%

소재지지번주소
Text

MISSING 

Distinct72
Distinct (%)96.0%
Missing61
Missing (%)44.9%
Memory size1.2 KiB
2023-12-12T11:50:56.420617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length18.493333
Min length15

Characters and Unicode

Total characters1387
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)92.0%

Sample

1st row인천광역시 부평구 갈산동 373-1
2nd row인천광역시 부평구 산곡동 179-54
3rd row인천광역시 부평구 부개동 126-12
4th row인천광역시 부평구 산곡동 100-38 산곡역
5th row인천광역시 부평구 부평동 299
ValueCountFrequency (%)
인천광역시 75
28.0%
부평구 75
28.0%
부평동 12
 
4.5%
산곡동 6
 
2.2%
청천동 5
 
1.9%
갈산동 5
 
1.9%
십정동 5
 
1.9%
삼산동 3
 
1.1%
부개동 2
 
0.7%
738-21 2
 
0.7%
Other values (76) 78
29.1%
2023-12-12T11:50:56.860394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
193
13.9%
부 105
 
7.6%
평 97
 
7.0%
천 80
 
5.8%
인 76
 
5.5%
역 76
 
5.5%
광 75
 
5.4%
시 75
 
5.4%
구 75
 
5.4%
동 73
 
5.3%
Other values (33) 462
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 839
60.5%
Decimal Number 300
 
21.6%
Space Separator 193
 
13.9%
Dash Punctuation 54
 
3.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 105
12.5%
평 97
11.6%
천 80
9.5%
인 76
9.1%
역 76
9.1%
광 75
8.9%
시 75
8.9%
구 75
8.9%
동 73
8.7%
산 34
 
4.1%
Other values (20) 73
8.7%
Decimal Number
ValueCountFrequency (%)
1 68
22.7%
2 37
12.3%
6 34
11.3%
8 29
9.7%
4 29
9.7%
3 27
 
9.0%
5 23
 
7.7%
9 19
 
6.3%
7 17
 
5.7%
0 17
 
5.7%
Space Separator
ValueCountFrequency (%)
193
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 839
60.5%
Common 548
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 105
12.5%
평 97
11.6%
천 80
9.5%
인 76
9.1%
역 76
9.1%
광 75
8.9%
시 75
8.9%
구 75
8.9%
동 73
8.7%
산 34
 
4.1%
Other values (20) 73
8.7%
Common
ValueCountFrequency (%)
193
35.2%
1 68
 
12.4%
- 54
 
9.9%
2 37
 
6.8%
6 34
 
6.2%
8 29
 
5.3%
4 29
 
5.3%
3 27
 
4.9%
5 23
 
4.2%
9 19
 
3.5%
Other values (3) 35
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 839
60.5%
ASCII 548
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
193
35.2%
1 68
 
12.4%
- 54
 
9.9%
2 37
 
6.8%
6 34
 
6.2%
8 29
 
5.3%
4 29
 
5.3%
3 27
 
4.9%
5 23
 
4.2%
9 19
 
3.5%
Other values (3) 35
 
6.4%
Hangul
ValueCountFrequency (%)
부 105
12.5%
평 97
11.6%
천 80
9.5%
인 76
9.1%
역 76
9.1%
광 75
8.9%
시 75
8.9%
구 75
8.9%
동 73
8.7%
산 34
 
4.1%
Other values (20) 73
8.7%

남녀공용화장실여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size268.0 B
False
134 
True
 
2
ValueCountFrequency (%)
False 134
98.5%
True 2
 
1.5%
2023-12-12T11:50:56.992920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

남성용-대변기수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9926471
Minimum0
Maximum72
Zeros4
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:50:57.092366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile19
Maximum72
Range72
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.1792813
Coefficient of variation (CV)2.2990465
Kurtosis31.900839
Mean3.9926471
Median Absolute Deviation (MAD)1
Skewness5.3772417
Sum543
Variance84.259205
MonotonicityNot monotonic
2023-12-12T11:50:57.212955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 51
37.5%
2 46
33.8%
4 9
 
6.6%
3 8
 
5.9%
6 6
 
4.4%
0 4
 
2.9%
5 3
 
2.2%
19 2
 
1.5%
9 1
 
0.7%
51 1
 
0.7%
Other values (5) 5
 
3.7%
ValueCountFrequency (%)
0 4
 
2.9%
1 51
37.5%
2 46
33.8%
3 8
 
5.9%
4 9
 
6.6%
5 3
 
2.2%
6 6
 
4.4%
9 1
 
0.7%
19 2
 
1.5%
20 1
 
0.7%
ValueCountFrequency (%)
72 1
 
0.7%
52 1
 
0.7%
51 1
 
0.7%
25 1
 
0.7%
22 1
 
0.7%
20 1
 
0.7%
19 2
 
1.5%
9 1
 
0.7%
6 6
4.4%
5 3
2.2%

남성용-소변기수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7205882
Minimum0
Maximum79
Zeros3
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:50:57.359783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile24.25
Maximum79
Range79
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.5255533
Coefficient of variation (CV)2.0178742
Kurtosis32.854502
Mean4.7205882
Median Absolute Deviation (MAD)1
Skewness5.2919612
Sum642
Variance90.736166
MonotonicityNot monotonic
2023-12-12T11:50:57.528277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 43
31.6%
1 28
20.6%
3 26
19.1%
4 11
 
8.1%
5 7
 
5.1%
6 7
 
5.1%
0 3
 
2.2%
27 2
 
1.5%
8 1
 
0.7%
13 1
 
0.7%
Other values (7) 7
 
5.1%
ValueCountFrequency (%)
0 3
 
2.2%
1 28
20.6%
2 43
31.6%
3 26
19.1%
4 11
 
8.1%
5 7
 
5.1%
6 7
 
5.1%
7 1
 
0.7%
8 1
 
0.7%
13 1
 
0.7%
ValueCountFrequency (%)
79 1
0.7%
52 1
0.7%
41 1
0.7%
27 2
1.5%
26 1
0.7%
25 1
0.7%
24 1
0.7%
13 1
0.7%
8 1
0.7%
7 1
0.7%

남성용-장애인용대변기수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77205882
Minimum0
Maximum15
Zeros66
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:50:57.669750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6687405
Coefficient of variation (CV)2.1614163
Kurtosis48.975512
Mean0.77205882
Median Absolute Deviation (MAD)1
Skewness6.4449369
Sum105
Variance2.784695
MonotonicityNot monotonic
2023-12-12T11:50:57.875166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 66
48.5%
1 61
44.9%
2 4
 
2.9%
3 2
 
1.5%
15 1
 
0.7%
4 1
 
0.7%
11 1
 
0.7%
ValueCountFrequency (%)
0 66
48.5%
1 61
44.9%
2 4
 
2.9%
3 2
 
1.5%
4 1
 
0.7%
11 1
 
0.7%
15 1
 
0.7%
ValueCountFrequency (%)
15 1
 
0.7%
11 1
 
0.7%
4 1
 
0.7%
3 2
 
1.5%
2 4
 
2.9%
1 61
44.9%
0 66
48.5%

남성용-장애인용소변기수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58823529
Minimum0
Maximum15
Zeros91
Zeros (%)66.9%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:50:58.015179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7190462
Coefficient of variation (CV)2.9223785
Kurtosis44.849761
Mean0.58823529
Median Absolute Deviation (MAD)0
Skewness6.2634643
Sum80
Variance2.9551198
MonotonicityNot monotonic
2023-12-12T11:50:58.148006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 91
66.9%
1 37
27.2%
2 5
 
3.7%
10 1
 
0.7%
8 1
 
0.7%
15 1
 
0.7%
ValueCountFrequency (%)
0 91
66.9%
1 37
27.2%
2 5
 
3.7%
8 1
 
0.7%
10 1
 
0.7%
15 1
 
0.7%
ValueCountFrequency (%)
15 1
 
0.7%
10 1
 
0.7%
8 1
 
0.7%
2 5
 
3.7%
1 37
27.2%
0 91
66.9%

남성용-어린이용대변기수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
127 
1
 
8
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 127
93.4%
1 8
 
5.9%
8 1
 
0.7%

Length

2023-12-12T11:50:58.316934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:50:58.450287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 127
93.4%
1 8
 
5.9%
8 1
 
0.7%

남성용-어린이용소변기수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
125 
1
 
8
5
 
1
2
 
1
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)2.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 125
91.9%
1 8
 
5.9%
5 1
 
0.7%
2 1
 
0.7%
8 1
 
0.7%

Length

2023-12-12T11:50:58.580925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:50:58.710524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 125
91.9%
1 8
 
5.9%
5 1
 
0.7%
2 1
 
0.7%
8 1
 
0.7%

여성용-대변기수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5661765
Minimum0
Maximum141
Zeros2
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:50:58.832709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q35
95-th percentile20.75
Maximum141
Range141
Interquartile range (IQR)4

Descriptive statistics

Standard deviation16.655864
Coefficient of variation (CV)2.5366154
Kurtosis42.451708
Mean6.5661765
Median Absolute Deviation (MAD)2
Skewness6.1843746
Sum893
Variance277.41781
MonotonicityNot monotonic
2023-12-12T11:50:58.952682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 42
30.9%
2 23
16.9%
3 19
14.0%
4 11
 
8.1%
5 8
 
5.9%
6 5
 
3.7%
8 4
 
2.9%
10 3
 
2.2%
12 3
 
2.2%
9 3
 
2.2%
Other values (12) 15
 
11.0%
ValueCountFrequency (%)
0 2
 
1.5%
1 42
30.9%
2 23
16.9%
3 19
14.0%
4 11
 
8.1%
5 8
 
5.9%
6 5
 
3.7%
7 2
 
1.5%
8 4
 
2.9%
9 3
 
2.2%
ValueCountFrequency (%)
141 1
 
0.7%
108 1
 
0.7%
72 1
 
0.7%
38 1
 
0.7%
30 1
 
0.7%
25 1
 
0.7%
23 1
 
0.7%
20 1
 
0.7%
16 1
 
0.7%
12 3
2.2%

여성용-장애인용대변기수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74264706
Minimum0
Maximum14
Zeros67
Zeros (%)49.3%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:50:59.059304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5962749
Coefficient of variation (CV)2.1494395
Kurtosis47.181087
Mean0.74264706
Median Absolute Deviation (MAD)1
Skewness6.3524935
Sum101
Variance2.5480937
MonotonicityNot monotonic
2023-12-12T11:50:59.175515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 67
49.3%
1 61
44.9%
2 4
 
2.9%
14 1
 
0.7%
3 1
 
0.7%
4 1
 
0.7%
11 1
 
0.7%
ValueCountFrequency (%)
0 67
49.3%
1 61
44.9%
2 4
 
2.9%
3 1
 
0.7%
4 1
 
0.7%
11 1
 
0.7%
14 1
 
0.7%
ValueCountFrequency (%)
14 1
 
0.7%
11 1
 
0.7%
4 1
 
0.7%
3 1
 
0.7%
2 4
 
2.9%
1 61
44.9%
0 67
49.3%

여성용-어린이용대변기수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
118 
1
16 
2
 
1
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 118
86.8%
1 16
 
11.8%
2 1
 
0.7%
9 1
 
0.7%

Length

2023-12-12T11:50:59.336538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:50:59.453785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 118
86.8%
1 16
 
11.8%
2 1
 
0.7%
9 1
 
0.7%

관리기관명
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
부평구청 기후변화대응과
52 
부평구시설관리공단
45 
인천교통공사
 
5
인천메트로서비스
 
4
부평구시설관리공단(공원관리팀)
 
3
Other values (24)
27 

Length

Max length16
Median length15
Mean length10.088235
Min length3

Unique

Unique21 ?
Unique (%)15.4%

Sample

1st row부평구시설관리공단(공원관리팀)
2nd row부평구시설관리공단(공원관리팀)
3rd row부평구시설관리공단(공원관리팀)
4th row서울교통공사
5th row인천대공원사업소

Common Values

ValueCountFrequency (%)
부평구청 기후변화대응과 52
38.2%
부평구시설관리공단 45
33.1%
인천교통공사 5
 
3.7%
인천메트로서비스 4
 
2.9%
부평구시설관리공단(공원관리팀) 3
 
2.2%
(재)인천광역시부평구문화재단 2
 
1.5%
인천대공원사업소 2
 
1.5%
부평구청 공원녹지과 2
 
1.5%
부평대로우체국 1
 
0.7%
인천시설공단 1
 
0.7%
Other values (19) 19
 
14.0%

Length

2023-12-12T11:50:59.587354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부평구청 54
27.3%
기후변화대응과 52
26.3%
부평구시설관리공단 45
22.7%
인천교통공사 5
 
2.5%
인천메트로서비스 4
 
2.0%
부평구시설관리공단(공원관리팀 3
 
1.5%
재)인천광역시부평구문화재단 2
 
1.0%
인천대공원사업소 2
 
1.0%
공원녹지과 2
 
1.0%
관리사무소 2
 
1.0%
Other values (24) 27
13.6%

전화번호
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
032-509-6594
52 
032-262-9244
40 
<NA>
8 
032-262-9288
6 
032-509-8822
 
2
Other values (27)
28 

Length

Max length12
Median length12
Mean length11.529412
Min length4

Unique

Unique26 ?
Unique (%)19.1%

Sample

1st row032-262-9244
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
032-509-6594 52
38.2%
032-262-9244 40
29.4%
<NA> 8
 
5.9%
032-262-9288 6
 
4.4%
032-509-8822 2
 
1.5%
032-500-2000 2
 
1.5%
032-502-0286 1
 
0.7%
032-513-3118 1
 
0.7%
032-451-3117 1
 
0.7%
032-502-0004 1
 
0.7%
Other values (22) 22
16.2%

Length

2023-12-12T11:50:59.740004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
032-509-6594 52
38.2%
032-262-9244 40
29.4%
na 8
 
5.9%
032-262-9288 6
 
4.4%
032-509-8822 2
 
1.5%
032-500-2000 2
 
1.5%
032-509-5487 1
 
0.7%
032-421-7788 1
 
0.7%
032-440-6472 1
 
0.7%
032-715-6211 1
 
0.7%
Other values (22) 22
16.2%

개방시간
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
24시간
81 
18시간
13 
19시간
 
8
17시간
 
6
16시간
 
5
Other values (8)
23 

Length

Max length4
Median length4
Mean length3.9485294
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row24시간
2nd row24시간
3rd row24시간
4th row18시간
5th row24시간

Common Values

ValueCountFrequency (%)
24시간 81
59.6%
18시간 13
 
9.6%
19시간 8
 
5.9%
17시간 6
 
4.4%
16시간 5
 
3.7%
8시간 4
 
2.9%
20시간 4
 
2.9%
13시간 4
 
2.9%
11시간 3
 
2.2%
9시간 3
 
2.2%
Other values (3) 5
 
3.7%

Length

2023-12-12T11:50:59.866409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
24시간 81
59.6%
18시간 13
 
9.6%
19시간 8
 
5.9%
17시간 6
 
4.4%
16시간 5
 
3.7%
8시간 4
 
2.9%
20시간 4
 
2.9%
13시간 4
 
2.9%
11시간 3
 
2.2%
9시간 3
 
2.2%
Other values (3) 5
 
3.7%

설치년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)35.1%
Missing42
Missing (%)30.9%
Infinite0
Infinite (%)0.0%
Mean2005.6809
Minimum1983
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:51:00.012455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1991
Q12000
median2007
Q32011.75
95-th percentile2020.35
Maximum2022
Range39
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation9.0496988
Coefficient of variation (CV)0.0045120333
Kurtosis-0.16423707
Mean2005.6809
Median Absolute Deviation (MAD)6
Skewness-0.4105646
Sum188534
Variance81.897049
MonotonicityNot monotonic
2023-12-12T11:51:00.505246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2007 9
 
6.6%
2010 8
 
5.9%
2009 8
 
5.9%
2017 5
 
3.7%
2013 5
 
3.7%
2004 4
 
2.9%
2006 4
 
2.9%
2012 4
 
2.9%
1998 3
 
2.2%
1995 3
 
2.2%
Other values (23) 41
30.1%
(Missing) 42
30.9%
ValueCountFrequency (%)
1983 2
1.5%
1984 1
 
0.7%
1989 1
 
0.7%
1991 3
2.2%
1992 2
1.5%
1993 2
1.5%
1994 1
 
0.7%
1995 3
2.2%
1996 1
 
0.7%
1997 2
1.5%
ValueCountFrequency (%)
2022 3
2.2%
2021 2
 
1.5%
2020 1
 
0.7%
2019 1
 
0.7%
2017 5
3.7%
2016 1
 
0.7%
2015 2
 
1.5%
2013 5
3.7%
2012 4
2.9%
2011 1
 
0.7%

Interactions

2023-12-12T11:50:53.142067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:47.350169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.068719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.777972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.536803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.239873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.081913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.932243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:53.252301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:47.432725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.155252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.866450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.623045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.325956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.176245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:52.027149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:53.371612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:47.518379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.241411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.953084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.706603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.474334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.270107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:52.137788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:53.497034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:47.621638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.330137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.041906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.790668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.569175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.390002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:52.254966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:53.607767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:47.719622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.416107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.128032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.870543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.662499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.491786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:52.673142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:53.710956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:47.801004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.502671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.255402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.955528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.777467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.604382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:52.801944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:53.797485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:47.891982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.586133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.345178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.053818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.884088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.724746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:52.910605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:53.891308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:47.980713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:48.675512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:49.439320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.149711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:50.981724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:51.823159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:53.021672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:51:00.647671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번소재지지번주소남녀공용화장실여부남성용-대변기수남성용-소변기수남성용-장애인용대변기수남성용-장애인용소변기수남성용-어린이용대변기수남성용-어린이용소변기수여성용-대변기수여성용-장애인용대변기수여성용-어린이용대변기수관리기관명전화번호개방시간설치년도
순번1.0000.8980.3410.3710.4250.4140.3020.2480.0000.2550.3490.2250.8150.8490.5850.494
소재지지번주소0.8981.000NaN0.8830.9200.0001.0000.6450.6800.0000.0000.0000.9220.9550.9020.981
남녀공용화장실여부0.341NaN1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaN
남성용-대변기수0.3710.8830.0001.0000.9750.7820.7280.7080.6620.9710.7910.6160.9881.0000.7570.000
남성용-소변기수0.4250.9200.0000.9751.0000.8840.8400.5480.7070.9910.8840.4790.9861.0000.8020.000
남성용-장애인용대변기수0.4140.0000.0000.7820.8841.0000.9860.0000.8500.8940.9990.3740.9850.9660.7930.000
남성용-장애인용소변기수0.3021.0000.0000.7280.8400.9861.0000.7140.9540.8570.9860.6400.9880.9860.8060.000
남성용-어린이용대변기수0.2480.6450.0000.7080.5480.0000.7141.0000.7210.6510.1210.7950.9120.9020.5950.420
남성용-어린이용소변기수0.0000.6800.0000.6620.7070.8500.9540.7211.0000.6840.8540.6810.9710.9710.8090.220
여성용-대변기수0.2550.0000.0000.9710.9910.8940.8570.6510.6841.0000.9080.6420.9931.0000.7940.000
여성용-장애인용대변기수0.3490.0000.0000.7910.8840.9990.9860.1210.8540.9081.0000.4640.9850.9660.7720.000
여성용-어린이용대변기수0.2250.0000.0000.6160.4790.3740.6400.7950.6810.6420.4641.0000.9640.9660.6080.473
관리기관명0.8150.9220.0000.9880.9860.9850.9880.9120.9710.9930.9850.9641.0000.9990.9610.722
전화번호0.8490.9550.0001.0001.0000.9660.9860.9020.9711.0000.9660.9660.9991.0000.9620.609
개방시간0.5850.9020.0000.7570.8020.7930.8060.5950.8090.7940.7720.6080.9610.9621.0000.519
설치년도0.4940.981NaN0.0000.0000.0000.0000.4200.2200.0000.0000.4730.7220.6090.5191.000
2023-12-12T11:51:00.848936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전화번호개방시간남녀공용화장실여부관리기관명남성용-어린이용소변기수남성용-어린이용대변기수여성용-어린이용대변기수
전화번호1.0000.6890.0000.9550.7720.6590.766
개방시간0.6891.0000.0000.6970.5920.3980.388
남녀공용화장실여부0.0000.0001.0000.0000.0000.0000.000
관리기관명0.9550.6970.0001.0000.7870.6910.776
남성용-어린이용소변기수0.7720.5920.0000.7871.0000.7060.612
남성용-어린이용대변기수0.6590.3980.0000.6910.7061.0000.853
여성용-어린이용대변기수0.7660.3880.0000.7760.6120.8531.000
2023-12-12T11:51:00.998568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번남성용-대변기수남성용-소변기수남성용-장애인용대변기수남성용-장애인용소변기수여성용-대변기수여성용-장애인용대변기수설치년도남녀공용화장실여부남성용-어린이용대변기수남성용-어린이용소변기수여성용-어린이용대변기수관리기관명전화번호개방시간
순번1.000-0.150-0.140-0.555-0.294-0.393-0.597-0.3200.2740.1530.0000.1360.4210.4580.284
남성용-대변기수-0.1501.0000.7450.5250.3580.7150.504-0.1590.0000.3850.5200.4430.8480.8880.490
남성용-소변기수-0.1400.7451.0000.4750.2410.7310.444-0.0430.0000.2650.5700.3250.8350.8920.547
남성용-장애인용대변기수-0.5550.5250.4751.0000.5770.6520.9350.1650.0000.0490.4800.1840.8580.8460.537
남성용-장애인용소변기수-0.2940.3580.2410.5771.0000.4140.5060.1450.0000.6960.6970.5670.8490.8240.588
여성용-대변기수-0.3930.7150.7310.6520.4141.0000.6160.2300.0000.3380.5440.4670.8680.8920.535
여성용-장애인용대변기수-0.5970.5040.4440.9350.5060.6161.0000.1250.0000.0890.4910.3930.8360.7560.543
설치년도-0.320-0.159-0.0430.1650.1450.2300.1251.0001.0000.2710.1350.2620.3160.2230.244
남녀공용화장실여부0.2740.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.000
남성용-어린이용대변기수0.1530.3850.2650.0490.6960.3380.0890.2710.0001.0000.7060.8530.6910.6590.398
남성용-어린이용소변기수0.0000.5200.5700.4800.6970.5440.4910.1350.0000.7061.0000.6120.7870.7720.592
여성용-어린이용대변기수0.1360.4430.3250.1840.5670.4670.3930.2620.0000.8530.6121.0000.7760.7660.388
관리기관명0.4210.8480.8350.8580.8490.8680.8360.3160.0000.6910.7870.7761.0000.9550.697
전화번호0.4580.8880.8920.8460.8240.8920.7560.2230.0000.6590.7720.7660.9551.0000.689
개방시간0.2840.4900.5470.5370.5880.5350.5430.2440.0000.3980.5920.3880.6970.6891.000

Missing values

2023-12-12T11:50:54.022531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:50:54.234738image/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.
2023-12-12T11:50:54.367665image/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

순번화장실명소재지도로명주소소재지지번주소남녀공용화장실여부남성용-대변기수남성용-소변기수남성용-장애인용대변기수남성용-장애인용소변기수남성용-어린이용대변기수남성용-어린이용소변기수여성용-대변기수여성용-장애인용대변기수여성용-어린이용대변기수관리기관명전화번호개방시간설치년도
01굴포공원인천광역시 부평구 주부토로 157(갈산동)인천광역시 부평구 갈산동 373-1N110000100부평구시설관리공단(공원관리팀)032-262-924424시간2022
12숲속공원인천광역시 부평구 원적로269번길 28(산곡동)인천광역시 부평구 산곡동 179-54N001100110부평구시설관리공단(공원관리팀)<NA>24시간2022
23세모공원인천광역시 부평구 부평문화로194번길 29(부개동)인천광역시 부평구 부개동 126-12N101100110부평구시설관리공단(공원관리팀)<NA>24시간2022
34산곡역인천광역시 부평구 길주로 지하379, 산곡역 (산곡동)인천광역시 부평구 산곡동 100-38 산곡역N6511101211서울교통공사<NA>18시간<NA>
45부평공원<NA>인천광역시 부평구 부평동 299N9622001620인천대공원사업소<NA>24시간<NA>
56원적산공원<NA>인천광역시 부평구 청천동 71-12N5422001120인천대공원사업소<NA>24시간<NA>
67삼산유수지체육공원인천광역시 부평구 체육관로 161(삼산동)<NA>N211101510부평구시설관리공단032-262-928824시간2011
78원적산체육공원인천광역시 부평구 세월천로 137번길(산곡동)인천광역시 부평구 산곡동 179-140N110000110부평구시설관리공단032-262-928824시간2004
89희망체육공원인천광역시 부평구 경인로 842(부평동)<NA>N001100110부평구시설관리공단032-262-928824시간2006
910다목적실내체육관인천광역시 부평구 부평대로 296번길 33인천광역시 부평구 갈산동 166-14N4611101111부평구시설관리공단032-262-928815시간2015
순번화장실명소재지도로명주소소재지지번주소남녀공용화장실여부남성용-대변기수남성용-소변기수남성용-장애인용대변기수남성용-장애인용소변기수남성용-어린이용대변기수남성용-어린이용소변기수여성용-대변기수여성용-장애인용대변기수여성용-어린이용대변기수관리기관명전화번호개방시간설치년도
126127㈜태보에너지 지점인천광역시 부평구 경인로1148번길 9(일신동)<NA>N120000200부평구청 기후변화대응과032-509-659424시간2007
127128부평LPG충전소인천광역시 부평구 부평대로 216(갈산동)<NA>N220000100부평구청 기후변화대응과032-509-659424시간2004
128129㈜태보에너지 송내IC충전소인천광역시 부평구 경인로 1182(일신동)<NA>N120000200부평구청 기후변화대응과032-509-659424시간2007
129130부평IC충전소인천광역시 부평구 부평북로 253(청천동)<NA>N220000100부평구청 기후변화대응과032-509-659424시간2007
130131구산충전소인천광역시 부평구 경인로 1203(구산동)<NA>N220000200부평구청 기후변화대응과032-509-659424시간2017
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