Overview

Dataset statistics

Number of variables9
Number of observations101
Missing cells12
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory76.3 B

Variable types

Numeric3
Categorical3
Text3

Dataset

Description충청남도 보령시에 설치된 폐농약용기 수거함에 대한 정보를 제공합니다. 수거함 설치 읍면동, 마을명, 도로명 주소, 지번주소, 위도, 경도를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=99&beforeMenuCd=DOM_000000201001001000&publicdatapk=15103992

Alerts

읍면동 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
시군 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
데이터기준일 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
연번 is highly overall correlated with 시군 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 시군 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 시군 and 1 other fieldsHigh correlation
시군 is highly imbalanced (92.0%)Imbalance
데이터기준일 is highly imbalanced (92.0%)Imbalance
도로명 주소 has 7 (6.9%) missing valuesMissing

Reproduction

Analysis started2024-01-09 22:58:46.739560
Analysis finished2024-01-09 22:58:48.378524
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-10T07:58:48.446667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2024-01-10T07:58:48.568475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
89.1%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

시군
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
충청남도 보령시
100 
<NA>
 
1

Length

Max length8
Median length8
Mean length7.960396
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row충청남도 보령시
2nd row충청남도 보령시
3rd row충청남도 보령시
4th row충청남도 보령시
5th row충청남도 보령시

Common Values

ValueCountFrequency (%)
충청남도 보령시 100
99.0%
<NA> 1
 
1.0%

Length

2024-01-10T07:58:48.691686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:58:48.786405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 100
49.8%
보령시 100
49.8%
na 1
 
0.5%

읍면동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
청소면
21 
주교면
15 
웅천읍
11 
주포면
9 
오천면
9 
Other values (9)
36 

Length

Max length4
Median length3
Mean length3.0891089
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
청소면 21
20.8%
주교면 15
14.9%
웅천읍 11
10.9%
주포면 9
8.9%
오천면 9
8.9%
청라면 7
 
6.9%
남포면 7
 
6.9%
미산면 6
 
5.9%
대천5동 5
 
5.0%
주산면 4
 
4.0%
Other values (4) 7
 
6.9%

Length

2024-01-10T07:58:48.880393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청소면 21
20.8%
주교면 15
14.9%
웅천읍 11
10.9%
주포면 9
8.9%
오천면 9
8.9%
청라면 7
 
6.9%
남포면 7
 
6.9%
미산면 6
 
5.9%
대천5동 5
 
5.0%
주산면 4
 
4.0%
Other values (4) 7
 
6.9%

마을명
Text

Distinct93
Distinct (%)93.0%
Missing1
Missing (%)1.0%
Memory size940.0 B
2024-01-10T07:58:49.126794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length4
Mean length4.21
Min length3

Characters and Unicode

Total characters421
Distinct characters81
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

Unique87 ?
Unique (%)87.0%

Sample

1st row죽청1리
2nd row관당2리
3rd row관당3리
4th row두룡1리
5th row평1리
ValueCountFrequency (%)
연지리 3
 
3.0%
봉당2리 2
 
2.0%
도화담2리 2
 
2.0%
주교2리 2
 
2.0%
남곡3동 2
 
2.0%
성동2리 2
 
2.0%
장곡1리 1
 
1.0%
신흥리 1
 
1.0%
음현리 1
 
1.0%
신산리 1
 
1.0%
Other values (84) 84
83.2%
2024-01-10T07:58:49.497882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 92
21.9%
2 35
 
8.3%
1 29
 
6.9%
동 16
 
3.8%
3 13
 
3.1%
성 12
 
2.9%
신 9
 
2.1%
죽 9
 
2.1%
정 9
 
2.1%
현 8
 
1.9%
Other values (71) 189
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 329
78.1%
Decimal Number 84
 
20.0%
Close Punctuation 3
 
0.7%
Open Punctuation 3
 
0.7%
Space Separator 1
 
0.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 92
28.0%
동 16
 
4.9%
성 12
 
3.6%
신 9
 
2.7%
죽 9
 
2.7%
정 9
 
2.7%
현 8
 
2.4%
포 7
 
2.1%
주 7
 
2.1%
교 6
 
1.8%
Other values (61) 154
46.8%
Decimal Number
ValueCountFrequency (%)
2 35
41.7%
1 29
34.5%
3 13
 
15.5%
4 4
 
4.8%
6 2
 
2.4%
5 1
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 329
78.1%
Common 92
 
21.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 92
28.0%
동 16
 
4.9%
성 12
 
3.6%
신 9
 
2.7%
죽 9
 
2.7%
정 9
 
2.7%
현 8
 
2.4%
포 7
 
2.1%
주 7
 
2.1%
교 6
 
1.8%
Other values (61) 154
46.8%
Common
ValueCountFrequency (%)
2 35
38.0%
1 29
31.5%
3 13
 
14.1%
4 4
 
4.3%
) 3
 
3.3%
( 3
 
3.3%
6 2
 
2.2%
1
 
1.1%
5 1
 
1.1%
, 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 329
78.1%
ASCII 92
 
21.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 92
28.0%
동 16
 
4.9%
성 12
 
3.6%
신 9
 
2.7%
죽 9
 
2.7%
정 9
 
2.7%
현 8
 
2.4%
포 7
 
2.1%
주 7
 
2.1%
교 6
 
1.8%
Other values (61) 154
46.8%
ASCII
ValueCountFrequency (%)
2 35
38.0%
1 29
31.5%
3 13
 
14.1%
4 4
 
4.3%
) 3
 
3.3%
( 3
 
3.3%
6 2
 
2.2%
1
 
1.1%
5 1
 
1.1%
, 1
 
1.1%

도로명 주소
Text

MISSING 

Distinct94
Distinct (%)100.0%
Missing7
Missing (%)6.9%
Memory size940.0 B
2024-01-10T07:58:49.802717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length21.12766
Min length15

Characters and Unicode

Total characters1986
Distinct characters147
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

Unique94 ?
Unique (%)100.0%

Sample

1st row충청남도 보령시 웅천읍 죽청로 64
2nd row충청남도 보령시 웅천읍 무챙이2길 11
3rd row충청남도 보령시 웅천읍 간드리큰길 41
4th row충청남도 보령시 웅천읍 충서로 1284-5
5th row충청남도 보령시 웅천읍 평리큰길 78
ValueCountFrequency (%)
충청남도 94
19.8%
보령시 94
19.8%
청소면 21
 
4.4%
주교면 15
 
3.2%
웅천읍 11
 
2.3%
주포면 9
 
1.9%
오천면 9
 
1.9%
청라면 6
 
1.3%
미산면 5
 
1.1%
남포면 5
 
1.1%
Other values (172) 205
43.2%
2024-01-10T07:58:50.232895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
383
19.3%
청 123
 
6.2%
남 102
 
5.1%
도 99
 
5.0%
충 96
 
4.8%
보 96
 
4.8%
령 96
 
4.8%
시 94
 
4.7%
면 76
 
3.8%
길 69
 
3.5%
Other values (137) 752
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1286
64.8%
Space Separator 383
 
19.3%
Decimal Number 280
 
14.1%
Dash Punctuation 22
 
1.1%
Open Punctuation 6
 
0.3%
Close Punctuation 6
 
0.3%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 123
 
9.6%
남 102
 
7.9%
도 99
 
7.7%
충 96
 
7.5%
보 96
 
7.5%
령 96
 
7.5%
시 94
 
7.3%
면 76
 
5.9%
길 69
 
5.4%
주 31
 
2.4%
Other values (122) 404
31.4%
Decimal Number
ValueCountFrequency (%)
1 42
15.0%
2 40
14.3%
4 32
11.4%
6 30
10.7%
5 28
10.0%
3 28
10.0%
8 25
8.9%
9 23
8.2%
7 21
7.5%
0 11
 
3.9%
Space Separator
ValueCountFrequency (%)
383
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1286
64.8%
Common 700
35.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 123
 
9.6%
남 102
 
7.9%
도 99
 
7.7%
충 96
 
7.5%
보 96
 
7.5%
령 96
 
7.5%
시 94
 
7.3%
면 76
 
5.9%
길 69
 
5.4%
주 31
 
2.4%
Other values (122) 404
31.4%
Common
ValueCountFrequency (%)
383
54.7%
1 42
 
6.0%
2 40
 
5.7%
4 32
 
4.6%
6 30
 
4.3%
5 28
 
4.0%
3 28
 
4.0%
8 25
 
3.6%
9 23
 
3.3%
- 22
 
3.1%
Other values (5) 47
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1286
64.8%
ASCII 700
35.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
383
54.7%
1 42
 
6.0%
2 40
 
5.7%
4 32
 
4.6%
6 30
 
4.3%
5 28
 
4.0%
3 28
 
4.0%
8 25
 
3.6%
9 23
 
3.3%
- 22
 
3.1%
Other values (5) 47
 
6.7%
Hangul
ValueCountFrequency (%)
청 123
 
9.6%
남 102
 
7.9%
도 99
 
7.7%
충 96
 
7.5%
보 96
 
7.5%
령 96
 
7.5%
시 94
 
7.3%
면 76
 
5.9%
길 69
 
5.4%
주 31
 
2.4%
Other values (122) 404
31.4%

지번 주소
Text

Distinct100
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Memory size940.0 B
2024-01-10T07:58:50.565987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length21.36
Min length16

Characters and Unicode

Total characters2136
Distinct characters85
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

Unique100 ?
Unique (%)100.0%

Sample

1st row충청남도 보령시 웅천읍 죽청리 212-5
2nd row충청남도 보령시 웅천읍 관당리 777-8
3rd row충청남도 보령시 웅천읍 관당리 340-2
4th row충청남도 보령시 웅천읍 두룡리 129-5
5th row충청남도 보령시 웅천읍 평리 23
ValueCountFrequency (%)
충청남도 100
20.2%
보령시 100
20.2%
청소면 21
 
4.3%
주교면 15
 
3.0%
웅천읍 11
 
2.2%
오천면 9
 
1.8%
주포면 9
 
1.8%
청라면 7
 
1.4%
남포면 7
 
1.4%
미산면 6
 
1.2%
Other values (159) 209
42.3%
2024-01-10T07:58:51.025316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
394
18.4%
청 129
 
6.0%
남 110
 
5.1%
보 104
 
4.9%
도 103
 
4.8%
령 102
 
4.8%
충 100
 
4.7%
시 100
 
4.7%
리 92
 
4.3%
- 84
 
3.9%
Other values (75) 818
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1280
59.9%
Space Separator 394
 
18.4%
Decimal Number 378
 
17.7%
Dash Punctuation 84
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 129
 
10.1%
남 110
 
8.6%
보 104
 
8.1%
도 103
 
8.0%
령 102
 
8.0%
충 100
 
7.8%
시 100
 
7.8%
리 92
 
7.2%
면 81
 
6.3%
주 35
 
2.7%
Other values (63) 324
25.3%
Decimal Number
ValueCountFrequency (%)
1 77
20.4%
3 53
14.0%
2 45
11.9%
5 41
10.8%
4 36
9.5%
6 28
 
7.4%
7 27
 
7.1%
0 26
 
6.9%
8 25
 
6.6%
9 20
 
5.3%
Space Separator
ValueCountFrequency (%)
394
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1280
59.9%
Common 856
40.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 129
 
10.1%
남 110
 
8.6%
보 104
 
8.1%
도 103
 
8.0%
령 102
 
8.0%
충 100
 
7.8%
시 100
 
7.8%
리 92
 
7.2%
면 81
 
6.3%
주 35
 
2.7%
Other values (63) 324
25.3%
Common
ValueCountFrequency (%)
394
46.0%
- 84
 
9.8%
1 77
 
9.0%
3 53
 
6.2%
2 45
 
5.3%
5 41
 
4.8%
4 36
 
4.2%
6 28
 
3.3%
7 27
 
3.2%
0 26
 
3.0%
Other values (2) 45
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1280
59.9%
ASCII 856
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
394
46.0%
- 84
 
9.8%
1 77
 
9.0%
3 53
 
6.2%
2 45
 
5.3%
5 41
 
4.8%
4 36
 
4.2%
6 28
 
3.3%
7 27
 
3.2%
0 26
 
3.0%
Other values (2) 45
 
5.3%
Hangul
ValueCountFrequency (%)
청 129
 
10.1%
남 110
 
8.6%
보 104
 
8.1%
도 103
 
8.0%
령 102
 
8.0%
충 100
 
7.8%
시 100
 
7.8%
리 92
 
7.2%
면 81
 
6.3%
주 35
 
2.7%
Other values (63) 324
25.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean36.365872
Minimum36.194761
Maximum36.483001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-10T07:58:51.162425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.194761
5-th percentile36.220186
Q136.304527
median36.383847
Q336.433429
95-th percentile36.463131
Maximum36.483001
Range0.28823981
Interquartile range (IQR)0.12890224

Descriptive statistics

Standard deviation0.07871844
Coefficient of variation (CV)0.002164624
Kurtosis-0.83875671
Mean36.365872
Median Absolute Deviation (MAD)0.05584305
Skewness-0.57961248
Sum3636.5872
Variance0.0061965928
MonotonicityNot monotonic
2024-01-10T07:58:51.285638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.2404044502 1
 
1.0%
36.4332242047 1
 
1.0%
36.2743004032 1
 
1.0%
36.2823428065 1
 
1.0%
36.4097337239 1
 
1.0%
36.4271984258 1
 
1.0%
36.4363950802 1
 
1.0%
36.3985918255 1
 
1.0%
36.3746456206 1
 
1.0%
36.3686944214 1
 
1.0%
Other values (90) 90
89.1%
ValueCountFrequency (%)
36.1947609664 1
1.0%
36.2023619778 1
1.0%
36.2053354709 1
1.0%
36.2191573882 1
1.0%
36.2196421771 1
1.0%
36.2202141168 1
1.0%
36.2233532512 1
1.0%
36.2285899871 1
1.0%
36.2404044502 1
1.0%
36.2430990459 1
1.0%
ValueCountFrequency (%)
36.4830007781 1
1.0%
36.4743084696 1
1.0%
36.4685602636 1
1.0%
36.4674818057 1
1.0%
36.4671622227 1
1.0%
36.4629189388 1
1.0%
36.459057003 1
1.0%
36.4585885839 1
1.0%
36.45855937 1
1.0%
36.4553482511 1
1.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean126.59238
Minimum126.50295
Maximum126.70427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-01-10T07:58:51.417483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.50295
5-th percentile126.522
Q1126.55857
median126.58957
Q3126.62
95-th percentile126.67791
Maximum126.70427
Range0.20131155
Interquartile range (IQR)0.061427684

Descriptive statistics

Standard deviation0.04670471
Coefficient of variation (CV)0.00036893777
Kurtosis-0.38229266
Mean126.59238
Median Absolute Deviation (MAD)0.031032274
Skewness0.30345534
Sum12659.238
Variance0.00218133
MonotonicityNot monotonic
2024-01-10T07:58:51.823338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.5538812303 1
 
1.0%
126.5760365937 1
 
1.0%
126.5672669088 1
 
1.0%
126.5832419826 1
 
1.0%
126.6778195939 1
 
1.0%
126.684299641 1
 
1.0%
126.6747982534 1
 
1.0%
126.6406792788 1
 
1.0%
126.6368356087 1
 
1.0%
126.6446146547 1
 
1.0%
Other values (90) 90
89.1%
ValueCountFrequency (%)
126.5029544743 1
1.0%
126.5044123771 1
1.0%
126.5103240755 1
1.0%
126.5134958468 1
1.0%
126.5207572441 1
1.0%
126.5220613697 1
1.0%
126.5245907255 1
1.0%
126.5247967025 1
1.0%
126.5255906669 1
1.0%
126.53197935 1
1.0%
ValueCountFrequency (%)
126.7042660276 1
1.0%
126.7006606743 1
1.0%
126.6854526685 1
1.0%
126.684299641 1
1.0%
126.6796083624 1
1.0%
126.6778195939 1
1.0%
126.6756253437 1
1.0%
126.6747982534 1
1.0%
126.6745229576 1
1.0%
126.6616080904 1
1.0%

데이터기준일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2022-08-13
100 
<NA>
 
1

Length

Max length10
Median length10
Mean length9.9405941
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2022-08-13
2nd row2022-08-13
3rd row2022-08-13
4th row2022-08-13
5th row2022-08-13

Common Values

ValueCountFrequency (%)
2022-08-13 100
99.0%
<NA> 1
 
1.0%

Length

2024-01-10T07:58:51.952157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:58:52.041063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-13 100
99.0%
na 1
 
1.0%

Interactions

2024-01-10T07:58:47.696389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:58:47.218588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:58:47.456775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:58:47.777971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:58:47.289836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:58:47.530339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:58:47.862091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:58:47.366223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:58:47.608735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:58:52.097853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동마을명도로명 주소지번 주소위도경도
연번1.0000.9441.0001.0001.0000.9490.723
읍면동0.9441.0001.0001.0001.0000.9110.753
마을명1.0001.0001.0001.0001.0000.9880.995
도로명 주소1.0001.0001.0001.0001.0001.0001.000
지번 주소1.0001.0001.0001.0001.0001.0001.000
위도0.9490.9110.9881.0001.0001.0000.513
경도0.7230.7530.9951.0001.0000.5131.000
2024-01-10T07:58:52.196772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동시군데이터기준일
읍면동1.0001.0001.000
시군1.0001.0001.000
데이터기준일1.0001.0001.000
2024-01-10T07:58:52.284504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도시군읍면동데이터기준일
연번1.000-0.1100.4031.0000.7771.000
위도-0.1101.000-0.1701.0000.6851.000
경도0.403-0.1701.0001.0000.4271.000
시군1.0001.0001.0001.0001.0001.000
읍면동0.7770.6850.4271.0001.0001.000
데이터기준일1.0001.0001.0001.0001.0001.000

Missing values

2024-01-10T07:58:48.020755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:58:48.154489image/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.
2024-01-10T07:58:48.283811image/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

연번시군읍면동마을명도로명 주소지번 주소위도경도데이터기준일
01충청남도 보령시웅천읍죽청1리충청남도 보령시 웅천읍 죽청로 64충청남도 보령시 웅천읍 죽청리 212-536.240404126.5538812022-08-13
12충청남도 보령시웅천읍관당2리충청남도 보령시 웅천읍 무챙이2길 11충청남도 보령시 웅천읍 관당리 777-836.247844126.5402452022-08-13
23충청남도 보령시웅천읍관당3리충청남도 보령시 웅천읍 간드리큰길 41충청남도 보령시 웅천읍 관당리 340-236.243099126.5417322022-08-13
34충청남도 보령시웅천읍두룡1리충청남도 보령시 웅천읍 충서로 1284-5충청남도 보령시 웅천읍 두룡리 129-536.260267126.5861122022-08-13
45충청남도 보령시웅천읍평1리충청남도 보령시 웅천읍 평리큰길 78충청남도 보령시 웅천읍 평리 2336.270389126.634482022-08-13
56충청남도 보령시웅천읍수부3리충청남도 보령시 웅천읍 부당길 27충청남도 보령시 웅천읍 수부리 63336.264756126.6179482022-08-13
67충청남도 보령시웅천읍성동1리충청남도 보령시 웅천읍 내성1길 42충청남도 보령시 웅천읍 성동리 199-136.247907126.6242052022-08-13
78충청남도 보령시웅천읍성동2리충청남도 보령시 웅천읍 외성2길 42충청남도 보령시 웅천읍 성동리 92836.252932126.6112482022-08-13
89충청남도 보령시웅천읍성동2리충청남도 보령시 웅천읍 성동큰길 273충청남도 보령시 웅천읍 성동리 384-336.251371126.619732022-08-13
910충청남도 보령시웅천읍성동2,3리충청남도 보령시 웅천읍 성동큰길 239충청남도 보령시 웅천읍 성동리 782-136.250928126.6160822022-08-13
연번시군읍면동마을명도로명 주소지번 주소위도경도데이터기준일
9192충청남도 보령시성주면성주5리충청남도 보령시 성주면 벌뜸길 9-80충청남도 보령시 성주면 성주리 92-236.341348126.6544942022-08-13
9293충청남도 보령시대천3동화산1동충청남도 보령시 대청로 340충청남도 보령시 화산동 41536.367153126.6248792022-08-13
9394충청남도 보령시대천3동동대2동충청남도 보령시 옥마로 200충청남도 보령시 동대동 4-136.349665126.6237222022-08-13
9495충청남도 보령시대천3동동대 16동충청남도 보령시 동현로 47충청남도 보령시 동대동 575-4736.350159126.6119482022-08-13
9596충청남도 보령시대천5동남곡2동충청남도 보령시 새태말길 38충청남도 보령시 남곡동 산 81-136.328829126.5643162022-08-13
9697충청남도 보령시대천5동남곡3동충청남도 보령시 남서2길 117 (2개)충청남도 보령시 남곡동 1030-236.337801126.5523472022-08-13
9798충청남도 보령시대천5동남곡3동충청남도 보령시 남서2길 143 (2개)충청남도 보령시 남곡동 산 107-136.336686126.5496282022-08-13
9899충청남도 보령시대천5동신흑2동충청남도 보령시 흑포1길 33-22 (2개)충청남도 보령시 신흑동 245-136.318746126.5352922022-08-13
99100충청남도 보령시대천5동신흑6동<NA>충청남도 보령시 신흑동 163836.309298126.5207572022-08-13
100<NA><NA><NA><NA><NA><NA><NA><NA><NA>