Overview

Dataset statistics

Number of variables8
Number of observations107
Missing cells35
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory68.2 B

Variable types

Numeric3
Text4
Categorical1

Dataset

Description인천광역시 미추홀구의 경비실에 대한 데이터로 아파트명, 관할동, 도로명 주소, 전화번호, 좌표값을 등의 항목을 제공합니다.
Author인천광역시 미추홀구
URLhttps://www.data.go.kr/data/15086988/fileData.do

Alerts

연번 is highly overall correlated with 관할동High correlation
위도 is highly overall correlated with 관할동High correlation
경도 is highly overall correlated with 관할동High correlation
관할동 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
전화번호 has 10 (9.3%) missing valuesMissing
팩스번호 has 25 (23.4%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:18:34.131886
Analysis finished2023-12-12 15:18:35.938578
Duration1.81 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54
Minimum1
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:18:36.049113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.3
Q127.5
median54
Q380.5
95-th percentile101.7
Maximum107
Range106
Interquartile range (IQR)53

Descriptive statistics

Standard deviation31.032241
Coefficient of variation (CV)0.57467114
Kurtosis-1.2
Mean54
Median Absolute Deviation (MAD)27
Skewness0
Sum5778
Variance963
MonotonicityStrictly increasing
2023-12-13T00:18:36.228736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
69 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
73 1
 
0.9%
Other values (97) 97
90.7%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%
98 1
0.9%

기관명
Text

Distinct105
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
2023-12-13T00:18:36.494609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length9.3738318
Min length4

Characters and Unicode

Total characters1003
Distinct characters188
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

Unique104 ?
Unique (%)97.2%

Sample

1st row신비마을아파트
2nd row관교삼환2차
3rd row관교성지아파트
4th row관교풍림
5th row관교동동아아파트 관리사무소
ValueCountFrequency (%)
아파트 25
 
13.5%
용현 11
 
5.9%
관리사무소 9
 
4.9%
주안 7
 
3.8%
경비실 6
 
3.2%
학익 5
 
2.7%
신비마을아파트 3
 
1.6%
쌍용 2
 
1.1%
동아아파트 2
 
1.1%
도화 2
 
1.1%
Other values (112) 113
61.1%
2023-12-13T00:18:36.886719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78
 
7.8%
아 72
 
7.2%
트 62
 
6.2%
파 60
 
6.0%
관 25
 
2.5%
현 24
 
2.4%
리 22
 
2.2%
용 21
 
2.1%
동 19
 
1.9%
주 19
 
1.9%
Other values (178) 601
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 883
88.0%
Space Separator 78
 
7.8%
Decimal Number 31
 
3.1%
Uppercase Letter 6
 
0.6%
Lowercase Letter 2
 
0.2%
Dash Punctuation 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 72
 
8.2%
트 62
 
7.0%
파 60
 
6.8%
관 25
 
2.8%
현 24
 
2.7%
리 22
 
2.5%
용 21
 
2.4%
동 19
 
2.2%
주 19
 
2.2%
차 19
 
2.2%
Other values (162) 540
61.2%
Decimal Number
ValueCountFrequency (%)
1 9
29.0%
2 9
29.0%
3 3
 
9.7%
6 3
 
9.7%
8 2
 
6.5%
4 2
 
6.5%
5 2
 
6.5%
7 1
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
L 2
33.3%
H 2
33.3%
K 1
16.7%
S 1
16.7%
Space Separator
ValueCountFrequency (%)
78
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 883
88.0%
Common 112
 
11.2%
Latin 8
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 72
 
8.2%
트 62
 
7.0%
파 60
 
6.8%
관 25
 
2.8%
현 24
 
2.7%
리 22
 
2.5%
용 21
 
2.4%
동 19
 
2.2%
주 19
 
2.2%
차 19
 
2.2%
Other values (162) 540
61.2%
Common
ValueCountFrequency (%)
78
69.6%
1 9
 
8.0%
2 9
 
8.0%
3 3
 
2.7%
6 3
 
2.7%
8 2
 
1.8%
4 2
 
1.8%
- 2
 
1.8%
5 2
 
1.8%
, 1
 
0.9%
Latin
ValueCountFrequency (%)
L 2
25.0%
H 2
25.0%
e 2
25.0%
K 1
12.5%
S 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 883
88.0%
ASCII 120
 
12.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
78
65.0%
1 9
 
7.5%
2 9
 
7.5%
3 3
 
2.5%
6 3
 
2.5%
L 2
 
1.7%
8 2
 
1.7%
H 2
 
1.7%
4 2
 
1.7%
e 2
 
1.7%
Other values (6) 8
 
6.7%
Hangul
ValueCountFrequency (%)
아 72
 
8.2%
트 62
 
7.0%
파 60
 
6.8%
관 25
 
2.8%
현 24
 
2.7%
리 22
 
2.5%
용 21
 
2.4%
동 19
 
2.2%
주 19
 
2.2%
차 19
 
2.2%
Other values (162) 540
61.2%

관할동
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size988.0 B
주안동
29 
용현동
26 
학익동
22 
도화동
15 
관교동
9 
Other values (2)
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주안동 29
27.1%
용현동 26
24.3%
학익동 22
20.6%
도화동 15
14.0%
관교동 9
 
8.4%
문학동 3
 
2.8%
숭의동 3
 
2.8%

Length

2023-12-13T00:18:37.022982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:18:37.131180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주안동 29
27.1%
용현동 26
24.3%
학익동 22
20.6%
도화동 15
14.0%
관교동 9
 
8.4%
문학동 3
 
2.8%
숭의동 3
 
2.8%

도로명주소
Text

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
2023-12-13T00:18:37.414757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length19.850467
Min length17

Characters and Unicode

Total characters2124
Distinct characters78
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)98.1%

Sample

1st row인천광역시 미추홀구 경원대로 627
2nd row인천광역시 미추홀구 문화로 23
3rd row인천광역시 미추홀구 문화로 35
4th row인천광역시 미추홀구 문화로 45
5th row인천광역시 미추홀구 인하로430번길 15
ValueCountFrequency (%)
인천광역시 107
24.9%
미추홀구 107
24.9%
소성로 6
 
1.4%
경인로 6
 
1.4%
매소홀로 6
 
1.4%
경원대로 5
 
1.2%
낙섬동로 5
 
1.2%
학익소로61번길 4
 
0.9%
주승로 4
 
0.9%
주안로 4
 
0.9%
Other values (134) 175
40.8%
2023-12-13T00:18:37.839659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
322
 
15.2%
인 120
 
5.6%
홀 120
 
5.6%
미 109
 
5.1%
추 109
 
5.1%
광 107
 
5.0%
역 107
 
5.0%
시 107
 
5.0%
구 107
 
5.0%
천 107
 
5.0%
Other values (68) 809
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1422
66.9%
Decimal Number 371
 
17.5%
Space Separator 322
 
15.2%
Dash Punctuation 9
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 120
 
8.4%
홀 120
 
8.4%
미 109
 
7.7%
추 109
 
7.7%
광 107
 
7.5%
역 107
 
7.5%
시 107
 
7.5%
구 107
 
7.5%
천 107
 
7.5%
로 103
 
7.2%
Other values (56) 326
22.9%
Decimal Number
ValueCountFrequency (%)
1 74
19.9%
3 57
15.4%
2 54
14.6%
6 35
9.4%
8 34
9.2%
5 32
8.6%
4 29
 
7.8%
9 19
 
5.1%
7 19
 
5.1%
0 18
 
4.9%
Space Separator
ValueCountFrequency (%)
322
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1422
66.9%
Common 702
33.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 120
 
8.4%
홀 120
 
8.4%
미 109
 
7.7%
추 109
 
7.7%
광 107
 
7.5%
역 107
 
7.5%
시 107
 
7.5%
구 107
 
7.5%
천 107
 
7.5%
로 103
 
7.2%
Other values (56) 326
22.9%
Common
ValueCountFrequency (%)
322
45.9%
1 74
 
10.5%
3 57
 
8.1%
2 54
 
7.7%
6 35
 
5.0%
8 34
 
4.8%
5 32
 
4.6%
4 29
 
4.1%
9 19
 
2.7%
7 19
 
2.7%
Other values (2) 27
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1422
66.9%
ASCII 702
33.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
322
45.9%
1 74
 
10.5%
3 57
 
8.1%
2 54
 
7.7%
6 35
 
5.0%
8 34
 
4.8%
5 32
 
4.6%
4 29
 
4.1%
9 19
 
2.7%
7 19
 
2.7%
Other values (2) 27
 
3.8%
Hangul
ValueCountFrequency (%)
인 120
 
8.4%
홀 120
 
8.4%
미 109
 
7.7%
추 109
 
7.7%
광 107
 
7.5%
역 107
 
7.5%
시 107
 
7.5%
구 107
 
7.5%
천 107
 
7.5%
로 103
 
7.2%
Other values (56) 326
22.9%

전화번호
Text

MISSING 

Distinct93
Distinct (%)95.9%
Missing10
Missing (%)9.3%
Memory size988.0 B
2023-12-13T00:18:38.149259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1164
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

Unique90 ?
Unique (%)92.8%

Sample

1st row032-425-2754
2nd row032-429-8472
3rd row032-421-9384
4th row032-437-9251
5th row032-435-7557
ValueCountFrequency (%)
032-425-2754 3
 
3.1%
032-881-6611 2
 
2.1%
032-437-9251 2
 
2.1%
032-420-0681 1
 
1.0%
032-865-0120 1
 
1.0%
032-865-9782 1
 
1.0%
032-429-5021 1
 
1.0%
032-427-6274 1
 
1.0%
032-421-1761 1
 
1.0%
032-435-1576 1
 
1.0%
Other values (83) 83
85.6%
2023-12-13T00:18:38.666733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 194
16.7%
2 177
15.2%
3 163
14.0%
0 152
13.1%
8 129
11.1%
4 75
 
6.4%
1 70
 
6.0%
7 69
 
5.9%
5 52
 
4.5%
6 52
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 970
83.3%
Dash Punctuation 194
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 177
18.2%
3 163
16.8%
0 152
15.7%
8 129
13.3%
4 75
7.7%
1 70
 
7.2%
7 69
 
7.1%
5 52
 
5.4%
6 52
 
5.4%
9 31
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1164
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 194
16.7%
2 177
15.2%
3 163
14.0%
0 152
13.1%
8 129
11.1%
4 75
 
6.4%
1 70
 
6.0%
7 69
 
5.9%
5 52
 
4.5%
6 52
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 194
16.7%
2 177
15.2%
3 163
14.0%
0 152
13.1%
8 129
11.1%
4 75
 
6.4%
1 70
 
6.0%
7 69
 
5.9%
5 52
 
4.5%
6 52
 
4.5%

팩스번호
Text

MISSING 

Distinct80
Distinct (%)97.6%
Missing25
Missing (%)23.4%
Memory size988.0 B
2023-12-13T00:18:39.014958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters984
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

Unique79 ?
Unique (%)96.3%

Sample

1st row032-432-2141
2nd row032-213-8472
3rd row032-437-1028
4th row032-433-9251
5th row032-439-7558
ValueCountFrequency (%)
032-432-2141 3
 
3.7%
032-881-6612 1
 
1.2%
032-881-7371 1
 
1.2%
032-861-8823 1
 
1.2%
032-427-6275 1
 
1.2%
032-421-1762 1
 
1.2%
032-435-1577 1
 
1.2%
704-575-3485 1
 
1.2%
032-873-7951 1
 
1.2%
032-213-2322 1
 
1.2%
Other values (70) 70
85.4%
2023-12-13T00:18:39.494696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 164
16.7%
2 156
15.9%
3 142
14.4%
0 119
12.1%
8 110
11.2%
1 61
 
6.2%
7 57
 
5.8%
4 55
 
5.6%
6 50
 
5.1%
5 42
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 820
83.3%
Dash Punctuation 164
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 156
19.0%
3 142
17.3%
0 119
14.5%
8 110
13.4%
1 61
 
7.4%
7 57
 
7.0%
4 55
 
6.7%
6 50
 
6.1%
5 42
 
5.1%
9 28
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 984
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 164
16.7%
2 156
15.9%
3 142
14.4%
0 119
12.1%
8 110
11.2%
1 61
 
6.2%
7 57
 
5.8%
4 55
 
5.6%
6 50
 
5.1%
5 42
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 164
16.7%
2 156
15.9%
3 142
14.4%
0 119
12.1%
8 110
11.2%
1 61
 
6.2%
7 57
 
5.8%
4 55
 
5.6%
6 50
 
5.1%
5 42
 
4.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.453227
Minimum37.436545
Maximum37.476905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:18:39.662205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.436545
5-th percentile37.438538
Q137.44303
median37.451027
Q337.46335
95-th percentile37.474432
Maximum37.476905
Range0.04035963
Interquartile range (IQR)0.020319915

Descriptive statistics

Standard deviation0.011597868
Coefficient of variation (CV)0.00030966272
Kurtosis-1.0235853
Mean37.453227
Median Absolute Deviation (MAD)0.0086661
Skewness0.45449698
Sum4007.4952
Variance0.00013451054
MonotonicityNot monotonic
2023-12-13T00:18:39.822799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.45201495 2
 
1.9%
37.43956173 1
 
0.9%
37.4437066 1
 
0.9%
37.4489707 1
 
0.9%
37.44689977 1
 
0.9%
37.44585584 1
 
0.9%
37.44718711 1
 
0.9%
37.4503696 1
 
0.9%
37.45013942 1
 
0.9%
37.46498883 1
 
0.9%
Other values (96) 96
89.7%
ValueCountFrequency (%)
37.43654516 1
0.9%
37.43713135 1
0.9%
37.43721956 1
0.9%
37.43791838 1
0.9%
37.43850222 1
0.9%
37.43851864 1
0.9%
37.43858226 1
0.9%
37.43884555 1
0.9%
37.43901058 1
0.9%
37.43918179 1
0.9%
ValueCountFrequency (%)
37.47690479 1
0.9%
37.47625317 1
0.9%
37.4759267 1
0.9%
37.47501371 1
0.9%
37.47472939 1
0.9%
37.47444557 1
0.9%
37.47440152 1
0.9%
37.47405725 1
0.9%
37.47292609 1
0.9%
37.47197085 1
0.9%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.66669
Minimum126.6316
Maximum126.69726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T00:18:39.988759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.6316
5-th percentile126.63769
Q1126.64947
median126.66931
Q3126.68223
95-th percentile126.69484
Maximum126.69726
Range0.0656529
Interquartile range (IQR)0.03275685

Descriptive statistics

Standard deviation0.018841289
Coefficient of variation (CV)0.00014874699
Kurtosis-1.1300478
Mean126.66669
Median Absolute Deviation (MAD)0.0142572
Skewness-0.25103199
Sum13553.336
Variance0.00035499418
MonotonicityNot monotonic
2023-12-13T00:18:40.150393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6450629 2
 
1.9%
126.6937737 1
 
0.9%
126.6812456 1
 
0.9%
126.689323 1
 
0.9%
126.683352 1
 
0.9%
126.6822051 1
 
0.9%
126.6798112 1
 
0.9%
126.6835682 1
 
0.9%
126.6771167 1
 
0.9%
126.6838371 1
 
0.9%
Other values (96) 96
89.7%
ValueCountFrequency (%)
126.6316047 1
0.9%
126.6326813 1
0.9%
126.6336494 1
0.9%
126.6358757 1
0.9%
126.6367798 1
0.9%
126.6375506 1
0.9%
126.6380152 1
0.9%
126.6386032 1
0.9%
126.6386727 1
0.9%
126.6386986 1
0.9%
ValueCountFrequency (%)
126.6972576 1
0.9%
126.6969867 1
0.9%
126.6968763 1
0.9%
126.6962681 1
0.9%
126.6961002 1
0.9%
126.6950059 1
0.9%
126.6944531 1
0.9%
126.694317 1
0.9%
126.6937737 1
0.9%
126.6899647 1
0.9%

Interactions

2023-12-13T00:18:35.286192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:34.583529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:34.922294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:35.398097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:34.686961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:35.033218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:35.516853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:34.816161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:18:35.141547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:18:40.283674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관할동전화번호팩스번호위도경도
연번1.0000.9220.8590.9630.8260.899
관할동0.9221.0000.9160.8810.7570.846
전화번호0.8590.9161.0001.0000.9510.949
팩스번호0.9630.8811.0001.0000.9570.926
위도0.8260.7570.9510.9571.0000.647
경도0.8990.8460.9490.9260.6471.000
2023-12-13T00:18:40.425632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도관할동
연번1.000-0.3600.0110.788
위도-0.3601.000-0.1510.509
경도0.011-0.1511.0000.637
관할동0.7880.5090.6371.000

Missing values

2023-12-13T00:18:35.679101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:18:35.801349image/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.
2023-12-13T00:18:35.893595image/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

연번기관명관할동도로명주소전화번호팩스번호위도경도
01신비마을아파트관교동인천광역시 미추홀구 경원대로 627032-425-2754032-432-214137.439562126.693774
12관교삼환2차관교동인천광역시 미추홀구 문화로 23032-429-8472032-213-847237.441327126.697258
23관교성지아파트관교동인천광역시 미추홀구 문화로 35032-421-9384032-437-102837.442512126.696876
34관교풍림관교동인천광역시 미추홀구 문화로 45032-437-9251032-433-925137.442998126.696987
45관교동동아아파트 관리사무소관교동인천광역시 미추홀구 인하로430번길 15032-435-7557032-439-755837.444111126.694317
56신비마을아파트관교동인천광역시 미추홀구 주승로 148032-425-2754032-432-214137.444758126.686444
67관교삼환1단지관교동인천광역시 미추홀구 주승로 223032-434-4841032-428-010437.443391126.6961
78관교 쌍용 아파트관교동인천광역시 미추홀구 주승로 231032-421-4864032-438-056237.442361126.695006
89관교동부관교동인천광역시 미추홀구 주승로 253032-421-3360032-421-336237.441224126.696268
910동아아파트도화동인천광역시 미추홀구 경인로 203-12032-872-0204032-872-730637.465998126.666282
연번기관명관할동도로명주소전화번호팩스번호위도경도
9798후문경비실학익동인천광역시 미추홀구 학익동 278-1<NA><NA>37.443159126.669311
9899주안신동아4차아파트학익동인천광역시 미추홀구 학익소로61번길 132032-228-8440032-873-788637.442503126.677913
99100학익신동아5차아파트학익동인천광역시 미추홀구 학익소로61번길 135032-874-3128032-868-591037.442987126.677426
100101학익 서원 아파트학익동인천광역시 미추홀구 학익소로61번길 36-23032-867-2880032-867-288037.442148126.67253
101102학익 신동아7차 아파트학익동인천광역시 미추홀구 학익소로61번길 83032-227-7502032-866-616737.442935126.674303
102103학익신동아6차 아파트학익동인천광역시 미추홀구 학익소로63번길 57032-872-1275032-872-127337.440787126.673151
103104원흥아파트 관리사무실학익동인천광역시 미추홀구 한나루로341번길 38032-875-4948032-213-494837.438846126.65978
104105태산아파트 경비실학익동인천광역시 미추홀구 한나루로341번길 40032-874-9860032-874-986037.439011126.659019
105106학익현광1차학익동인천광역시 미추홀구 한나루로357번길 165032-865-0120032-241-012037.437918126.654645
106107학익정광산호학익동인천광역시 미추홀구 한나루로357번길 87-18032-874-5115032-229-511537.438519126.656844