Overview

Dataset statistics

Number of variables8
Number of observations158
Missing cells12
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory67.8 B

Variable types

Numeric3
Categorical2
Text3

Dataset

Description연수구내 경로당 현황의 데이터에서 경로당명, 주소, 전화번호 등의 목록- 동명 , 유형, 경로당명, 주소, 연락처로 구분
Author인천광역시 연수구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15064884&srcSe=7661IVAWM27C61E190

Alerts

연번 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 동명 High correlation
동명 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
유형 is highly imbalanced (65.4%)Imbalance
위도 has 4 (2.5%) missing valuesMissing
경도 has 4 (2.5%) missing valuesMissing
연락처 has 4 (2.5%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 16:19:24.048738
Analysis finished2024-01-28 16:19:25.479539
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.5
Minimum1
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-01-29T01:19:25.531286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.85
Q140.25
median79.5
Q3118.75
95-th percentile150.15
Maximum158
Range157
Interquartile range (IQR)78.5

Descriptive statistics

Standard deviation45.754781
Coefficient of variation (CV)0.57553184
Kurtosis-1.2
Mean79.5
Median Absolute Deviation (MAD)39.5
Skewness0
Sum12561
Variance2093.5
MonotonicityStrictly increasing
2024-01-29T01:19:25.646109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
110 1
 
0.6%
103 1
 
0.6%
104 1
 
0.6%
105 1
 
0.6%
106 1
 
0.6%
107 1
 
0.6%
108 1
 
0.6%
109 1
 
0.6%
111 1
 
0.6%
Other values (148) 148
93.7%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
158 1
0.6%
157 1
0.6%
156 1
0.6%
155 1
0.6%
154 1
0.6%
153 1
0.6%
152 1
0.6%
151 1
0.6%
150 1
0.6%
149 1
0.6%

동명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
송도2동
18 
청학동
16 
옥련1동
14 
송도1동
14 
동춘1동
13 
Other values (11)
83 

Length

Max length6
Median length6
Mean length5.8164557
Min length5

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row 옥련1동
2nd row 옥련1동
3rd row 옥련1동
4th row 옥련1동
5th row 옥련1동

Common Values

ValueCountFrequency (%)
송도2동 18
11.4%
청학동 16
10.1%
옥련1동 14
8.9%
송도1동 14
8.9%
동춘1동 13
8.2%
선학동 12
7.6%
연수1동 12
7.6%
옥련2동 11
 
7.0%
송도3동 11
 
7.0%
연수3동 9
 
5.7%
Other values (6) 28
17.7%

Length

2024-01-29T01:19:25.787029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송도2동 18
11.4%
청학동 16
10.1%
옥련1동 14
8.9%
송도1동 14
8.9%
동춘1동 13
8.2%
선학동 12
7.6%
연수1동 12
7.6%
옥련2동 11
 
7.0%
송도3동 11
 
7.0%
연수3동 9
 
5.7%
Other values (5) 28
17.7%

유형
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
사립(공동)
141 
구립
15 
사립(일반)
 
2

Length

Max length8
Median length8
Mean length7.6202532
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 구립
2nd row 사립(공동)
3rd row 사립(공동)
4th row 사립(공동)
5th row 사립(공동)

Common Values

ValueCountFrequency (%)
사립(공동) 141
89.2%
구립 15
 
9.5%
사립(일반) 2
 
1.3%

Length

2024-01-29T01:19:25.885716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:19:25.985286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사립(공동 141
89.2%
구립 15
 
9.5%
사립(일반 2
 
1.3%

경로당명
Text

Distinct149
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-01-29T01:19:26.130869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length12.753165
Min length7

Characters and Unicode

Total characters2015
Distinct characters176
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)89.9%

Sample

1st row 대암마을경로당
2nd row 백산1차아파트경로당
3rd row 백산2차아파트경로당
4th row 벽산빌리지경로당
5th row 삼성아파트경로당
ValueCountFrequency (%)
동남아파트경로당 3
 
1.9%
대동아파트경로당 3
 
1.9%
현대1차아파트경로당 2
 
1.2%
태산아파트경로당 2
 
1.2%
우성1차아파트경로당 2
 
1.2%
우성2차아파트경로당 2
 
1.2%
아주아파트경로당 2
 
1.2%
하나아파트경로당 2
 
1.2%
웰카운티4단지경로당(송도 1
 
0.6%
더샵퍼스드월드경로당(송도 1
 
0.6%
Other values (142) 142
87.7%
2024-01-29T01:19:26.414226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
319
15.8%
로 161
 
8.0%
경 159
 
7.9%
당 156
 
7.7%
아 126
 
6.3%
트 123
 
6.1%
파 122
 
6.1%
도 37
 
1.8%
송 37
 
1.8%
지 37
 
1.8%
Other values (166) 738
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1576
78.2%
Space Separator 319
 
15.8%
Decimal Number 73
 
3.6%
Open Punctuation 22
 
1.1%
Close Punctuation 22
 
1.1%
Other Punctuation 2
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 161
 
10.2%
경 159
 
10.1%
당 156
 
9.9%
아 126
 
8.0%
트 123
 
7.8%
파 122
 
7.7%
도 37
 
2.3%
송 37
 
2.3%
지 37
 
2.3%
차 33
 
2.1%
Other values (151) 585
37.1%
Decimal Number
ValueCountFrequency (%)
1 30
41.1%
2 16
21.9%
3 11
 
15.1%
4 4
 
5.5%
8 3
 
4.1%
7 3
 
4.1%
5 3
 
4.1%
9 1
 
1.4%
0 1
 
1.4%
6 1
 
1.4%
Space Separator
ValueCountFrequency (%)
319
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1576
78.2%
Common 438
 
21.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 161
 
10.2%
경 159
 
10.1%
당 156
 
9.9%
아 126
 
8.0%
트 123
 
7.8%
파 122
 
7.7%
도 37
 
2.3%
송 37
 
2.3%
지 37
 
2.3%
차 33
 
2.1%
Other values (151) 585
37.1%
Common
ValueCountFrequency (%)
319
72.8%
1 30
 
6.8%
( 22
 
5.0%
) 22
 
5.0%
2 16
 
3.7%
3 11
 
2.5%
4 4
 
0.9%
8 3
 
0.7%
7 3
 
0.7%
5 3
 
0.7%
Other values (4) 5
 
1.1%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1576
78.2%
ASCII 439
 
21.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
319
72.7%
1 30
 
6.8%
( 22
 
5.0%
) 22
 
5.0%
2 16
 
3.6%
3 11
 
2.5%
4 4
 
0.9%
8 3
 
0.7%
7 3
 
0.7%
5 3
 
0.7%
Other values (5) 6
 
1.4%
Hangul
ValueCountFrequency (%)
로 161
 
10.2%
경 159
 
10.1%
당 156
 
9.9%
아 126
 
8.0%
트 123
 
7.8%
파 122
 
7.7%
도 37
 
2.3%
송 37
 
2.3%
지 37
 
2.3%
차 33
 
2.1%
Other values (151) 585
37.1%

주소
Text

Distinct149
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-01-29T01:19:26.652844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length17
Mean length10.025316
Min length5

Characters and Unicode

Total characters1584
Distinct characters107
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

Unique143 ?
Unique (%)90.5%

Sample

1st row대암로14번길 19
2nd row한나루로79번길 11
3rd row능허대로123번길 8
4th row한나루로158번길 31
5th row독배로40번길 18
ValueCountFrequency (%)
원인재로 16
 
4.9%
선학로 8
 
2.5%
먼우금로 7
 
2.2%
컨벤시아대로 6
 
1.8%
새말로 6
 
1.8%
30 6
 
1.8%
20 6
 
1.8%
함박뫼로 5
 
1.5%
16 4
 
1.2%
용담로 4
 
1.2%
Other values (185) 257
79.1%
2024-01-29T01:19:26.978202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
187
 
11.8%
로 158
 
10.0%
1 113
 
7.1%
2 104
 
6.6%
길 82
 
5.2%
번 82
 
5.2%
3 55
 
3.5%
0 55
 
3.5%
4 43
 
2.7%
5 42
 
2.7%
Other values (97) 663
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 823
52.0%
Decimal Number 562
35.5%
Space Separator 187
 
11.8%
Dash Punctuation 8
 
0.5%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 158
19.2%
길 82
 
10.0%
번 82
 
10.0%
대 37
 
4.5%
아 20
 
2.4%
재 20
 
2.4%
송 19
 
2.3%
원 18
 
2.2%
인 17
 
2.1%
시 16
 
1.9%
Other values (83) 354
43.0%
Decimal Number
ValueCountFrequency (%)
1 113
20.1%
2 104
18.5%
3 55
9.8%
0 55
9.8%
4 43
 
7.7%
5 42
 
7.5%
8 39
 
6.9%
7 38
 
6.8%
9 37
 
6.6%
6 36
 
6.4%
Space Separator
ValueCountFrequency (%)
187
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 823
52.0%
Common 761
48.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 158
19.2%
길 82
 
10.0%
번 82
 
10.0%
대 37
 
4.5%
아 20
 
2.4%
재 20
 
2.4%
송 19
 
2.3%
원 18
 
2.2%
인 17
 
2.1%
시 16
 
1.9%
Other values (83) 354
43.0%
Common
ValueCountFrequency (%)
187
24.6%
1 113
14.8%
2 104
13.7%
3 55
 
7.2%
0 55
 
7.2%
4 43
 
5.7%
5 42
 
5.5%
8 39
 
5.1%
7 38
 
5.0%
9 37
 
4.9%
Other values (4) 48
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 823
52.0%
ASCII 761
48.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
187
24.6%
1 113
14.8%
2 104
13.7%
3 55
 
7.2%
0 55
 
7.2%
4 43
 
5.7%
5 42
 
5.5%
8 39
 
5.1%
7 38
 
5.0%
9 37
 
4.9%
Other values (4) 48
 
6.3%
Hangul
ValueCountFrequency (%)
로 158
19.2%
길 82
 
10.0%
번 82
 
10.0%
대 37
 
4.5%
아 20
 
2.4%
재 20
 
2.4%
송 19
 
2.3%
원 18
 
2.2%
인 17
 
2.1%
시 16
 
1.9%
Other values (83) 354
43.0%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct138
Distinct (%)89.6%
Missing4
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean37.411485
Minimum37.371442
Maximum37.616886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-01-29T01:19:27.088102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.371442
5-th percentile37.381222
Q137.397476
median37.414683
Q337.423529
95-th percentile37.427967
Maximum37.616886
Range0.245444
Interquartile range (IQR)0.02605325

Descriptive statistics

Standard deviation0.022906009
Coefficient of variation (CV)0.0006122721
Kurtosis41.4787
Mean37.411485
Median Absolute Deviation (MAD)0.0106285
Skewness4.474996
Sum5761.3688
Variance0.00052468524
MonotonicityNot monotonic
2024-01-29T01:19:27.189743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.414683 4
 
2.5%
37.397889 3
 
1.9%
37.423055 3
 
1.9%
37.42189 3
 
1.9%
37.383912 2
 
1.3%
37.420713 2
 
1.3%
37.425052 2
 
1.3%
37.403893 2
 
1.3%
37.401513 2
 
1.3%
37.392921 2
 
1.3%
Other values (128) 129
81.6%
(Missing) 4
 
2.5%
ValueCountFrequency (%)
37.371442 1
0.6%
37.372751 1
0.6%
37.373381 1
0.6%
37.375956 1
0.6%
37.37599 1
0.6%
37.377414 1
0.6%
37.379109 1
0.6%
37.38102 1
0.6%
37.38133 1
0.6%
37.381629 1
0.6%
ValueCountFrequency (%)
37.616886 1
0.6%
37.436735 1
0.6%
37.430007 1
0.6%
37.429817 1
0.6%
37.428608 1
0.6%
37.428549 1
0.6%
37.428178 1
0.6%
37.427971 1
0.6%
37.427965 1
0.6%
37.427926 1
0.6%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct138
Distinct (%)89.6%
Missing4
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean126.66721
Minimum126.61863
Maximum127.00942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-01-29T01:19:27.299703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.61863
5-th percentile126.64228
Q1126.64903
median126.6662
Q3126.67853
95-th percentile126.69771
Maximum127.00942
Range0.390788
Interquartile range (IQR)0.02949725

Descriptive statistics

Standard deviation0.03302772
Coefficient of variation (CV)0.00026074403
Kurtosis75.476643
Mean126.66721
Median Absolute Deviation (MAD)0.0149295
Skewness7.3466606
Sum19506.751
Variance0.0010908303
MonotonicityNot monotonic
2024-01-29T01:19:27.427306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.687855 4
 
2.5%
126.645143 3
 
1.9%
126.68054 3
 
1.9%
126.699262 3
 
1.9%
126.643855 2
 
1.3%
126.694656 2
 
1.3%
126.702191 2
 
1.3%
126.668714 2
 
1.3%
126.64725 2
 
1.3%
126.646044 2
 
1.3%
Other values (128) 129
81.6%
(Missing) 4
 
2.5%
ValueCountFrequency (%)
126.618631 1
0.6%
126.63303 1
0.6%
126.636717 1
0.6%
126.637799 1
0.6%
126.639028 1
0.6%
126.640718 1
0.6%
126.640779 1
0.6%
126.64127 1
0.6%
126.642819 1
0.6%
126.643241 1
0.6%
ValueCountFrequency (%)
127.009419 1
 
0.6%
126.702191 2
1.3%
126.700175 1
 
0.6%
126.699951 1
 
0.6%
126.699262 3
1.9%
126.696871 1
 
0.6%
126.696752 1
 
0.6%
126.696356 1
 
0.6%
126.695562 1
 
0.6%
126.695544 1
 
0.6%

연락처
Text

MISSING 

Distinct154
Distinct (%)100.0%
Missing4
Missing (%)2.5%
Memory size1.4 KiB
2024-01-29T01:19:27.645614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.058442
Min length12

Characters and Unicode

Total characters1857
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)100.0%

Sample

1st row032-834-0041
2nd row032-832-6701
3rd row032-834-1911
4th row032-833-6488
5th row032-833-9222
ValueCountFrequency (%)
032-834-1700 1
 
0.6%
032-814-2593 1
 
0.6%
032-851-6006 1
 
0.6%
032-258-1802 1
 
0.6%
032-813-0420 1
 
0.6%
032-816-3045 1
 
0.6%
032-813-3442 1
 
0.6%
032-815-6421 1
 
0.6%
032-817-0972 1
 
0.6%
032-835-5870 1
 
0.6%
Other values (144) 144
93.5%
2024-01-29T01:19:27.970472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 308
16.6%
3 265
14.3%
2 254
13.7%
0 251
13.5%
8 207
11.1%
1 170
9.2%
5 91
 
4.9%
7 87
 
4.7%
4 79
 
4.3%
9 75
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1549
83.4%
Dash Punctuation 308
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 265
17.1%
2 254
16.4%
0 251
16.2%
8 207
13.4%
1 170
11.0%
5 91
 
5.9%
7 87
 
5.6%
4 79
 
5.1%
9 75
 
4.8%
6 70
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1857
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 308
16.6%
3 265
14.3%
2 254
13.7%
0 251
13.5%
8 207
11.1%
1 170
9.2%
5 91
 
4.9%
7 87
 
4.7%
4 79
 
4.3%
9 75
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1857
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 308
16.6%
3 265
14.3%
2 254
13.7%
0 251
13.5%
8 207
11.1%
1 170
9.2%
5 91
 
4.9%
7 87
 
4.7%
4 79
 
4.3%
9 75
 
4.0%

Interactions

2024-01-29T01:19:24.792442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:19:24.374409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:19:24.590330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:19:24.865768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:19:24.443566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:19:24.663928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:19:24.931903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:19:24.508987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:19:24.721332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:19:28.052854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동명유형위도경도
연번1.0000.9670.3900.8000.725
동명0.9671.0000.4110.8690.855
유형0.3900.4111.0000.1240.000
위도0.8000.8690.1241.0000.918
경도0.7250.8550.0000.9181.000
2024-01-29T01:19:28.127968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형동명
유형1.0000.235
동명0.2351.000
2024-01-29T01:19:28.191563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도동명유형
연번1.000-0.850-0.3500.8310.248
위도-0.8501.0000.3400.6800.116
경도-0.3500.3401.0000.6580.000
동명0.8310.6800.6581.0000.235
유형0.2480.1160.0000.2351.000

Missing values

2024-01-29T01:19:25.278511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:19:25.367783image/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.
2024-01-29T01:19:25.440519image/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

연번동명유형경로당명주소위도경도연락처
01옥련1동구립대암마을경로당대암로14번길 1937.416771126.652465032-834-0041
12옥련1동사립(공동)백산1차아파트경로당한나루로79번길 1137.422375126.646058032-832-6701
23옥련1동사립(공동)백산2차아파트경로당능허대로123번길 837.422026126.6445032-834-1911
34옥련1동사립(공동)벽산빌리지경로당한나루로158번길 3137.422495126.656476032-833-6488
45옥련1동사립(공동)삼성아파트경로당독배로40번길 1837.423793126.645572032-833-9222
56옥련1동사립(공동)서해희망아파트경로당청량로185번길 1637.423716126.651752032-833-8119
67옥련1동사립(공동)쌍용아파트경로당청량로 21037.424958126.65292032-212-6448
78옥련1동구립옥련경로당한나루로157번길 1237.424147126.654653032-832-1579
89옥련1동사립(공동)우성1차아파트경로당독배로40번길 4837.423672126.6473032-834-1700
910옥련1동사립(공동)우성2차아파트경로당청량로185번길 7337.422717126.648656032-832-7780
연번동명유형경로당명주소위도경도연락처
148149송도3동사립(공동)더샵센트럴시티아파트시니어회송도 국제대로 26137.384878126.658734032-822-9048
149150송도3동사립(공동)송도아메리칸타운아이파크시니어경로당송도과학로 27번길 1537.385435126.662667032-814-9992
150151송도4동사립(공동)더샵마스터뷰2단지아파트경로당 (송도)컨벤시아대로 274번길 3537.38133126.63303032-831-8125
151152송도4동사립(공동)마스터뷰1단지경로당컨벤시아대로 274번길 55<NA><NA><NA>
152153송도4동사립(공동)아트윈푸르지오아파트경로당(송도)인천타워대로 253-2537.390558126.636717070-4575-7004
153154송도4동사립(공동)더샵퍼스트파크13단지경로당컨벤시아대로252번길7037.392921126.646044032-834-8996
154155송도4동사립(공동)더샵퍼스트파크14단지경로당컨벤시아대로252번길 5037.392921126.646044032-834-8974
155156송도4동사립(공동)더샵퍼스트파크15단지경로당컨벤시아대로252버길 3037.392868126.646148032-831-1526
156157송도5동사립(공동)e편한세상송도아파트경로당랜드마크로11337.411283126.618631032-858-9793
157158송도5동사립(공동)마리나베이아파트경로당랜드마크로 160<NA><NA><NA>