Overview

Dataset statistics

Number of variables11
Number of observations90
Missing cells5
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory95.5 B

Variable types

Numeric3
Categorical6
Text2

Dataset

Description서산시에 설치된 광고물 게시대 현황으로 관리번호, 관리기관, 지번주소, 위치설명, 열수, 수량 등에 관한 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=445&beforeMenuCd=DOM_000000201001001000&publicdatapk=3070710

Alerts

관리기관 연락처 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
관리기관 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
수량 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
가능매수 is highly overall correlated with 관리기관 and 4 other fieldsHigh correlation
열수 is highly overall correlated with 관리기관 and 4 other fieldsHigh correlation
관리번호 is highly overall correlated with 관리기관 and 3 other fieldsHigh correlation
경도(WGS84좌표) is highly overall correlated with 관리기관 and 3 other fieldsHigh correlation
위도(WGS84좌표) is highly overall correlated with 관리기관 and 3 other fieldsHigh correlation
관리기관 is highly imbalanced (91.2%)Imbalance
관리기관 연락처 is highly imbalanced (91.2%)Imbalance
열수 is highly imbalanced (77.5%)Imbalance
수량 is highly imbalanced (91.2%)Imbalance
가능매수 is highly imbalanced (77.5%)Imbalance
데이터기준일자 is highly imbalanced (91.2%)Imbalance
관리번호 has 1 (1.1%) missing valuesMissing
지번주소 has 1 (1.1%) missing valuesMissing
위치설명 has 1 (1.1%) missing valuesMissing
경도(WGS84좌표) has 1 (1.1%) missing valuesMissing
위도(WGS84좌표) has 1 (1.1%) missing valuesMissing

Reproduction

Analysis started2024-01-09 20:57:05.918979
Analysis finished2024-01-09 20:57:07.554976
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean45
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-01-10T05:57:07.625213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.4
Q123
median45
Q367
95-th percentile84.6
Maximum89
Range88
Interquartile range (IQR)44

Descriptive statistics

Standard deviation25.836021
Coefficient of variation (CV)0.57413381
Kurtosis-1.2
Mean45
Median Absolute Deviation (MAD)22
Skewness0
Sum4005
Variance667.5
MonotonicityStrictly increasing
2024-01-10T05:57:07.751845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
68 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
59 1
 
1.1%
Other values (79) 79
87.8%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%
81 1
1.1%
80 1
1.1%

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size852.0 B
서산시청
89 
<NA>
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
서산시청 89
98.9%
<NA> 1
 
1.1%

Length

2024-01-10T05:57:07.863115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:08.053560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서산시청 89
98.9%
na 1
 
1.1%

관리기관 연락처
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size852.0 B
041-660-3094
89 
<NA>
 
1

Length

Max length12
Median length12
Mean length11.911111
Min length4

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row041-660-3094
2nd row041-660-3094
3rd row041-660-3094
4th row041-660-3094
5th row041-660-3094

Common Values

ValueCountFrequency (%)
041-660-3094 89
98.9%
<NA> 1
 
1.1%

Length

2024-01-10T05:57:08.187876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:08.286925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
041-660-3094 89
98.9%
na 1
 
1.1%

지번주소
Text

MISSING 

Distinct63
Distinct (%)70.8%
Missing1
Missing (%)1.1%
Memory size852.0 B
2024-01-10T05:57:08.523587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length19.865169
Min length16

Characters and Unicode

Total characters1768
Distinct characters82
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

Unique42 ?
Unique (%)47.2%

Sample

1st row충청남도 서산시 석림동 952
2nd row충청남도 서산시 석림동 952
3rd row충청남도 서산시 석림동 952
4th row충청남도 서산시 석림동 952
5th row충청남도 서산시 석림동 317
ValueCountFrequency (%)
서산시 90
22.1%
충청남도 89
21.9%
대산읍 11
 
2.7%
석림동 11
 
2.7%
성연면 8
 
2.0%
지곡면 8
 
2.0%
석남동 7
 
1.7%
예천동 6
 
1.5%
잠홍동 5
 
1.2%
해미면 5
 
1.2%
Other values (101) 167
41.0%
2024-01-10T05:57:08.921727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
318
18.0%
산 123
 
7.0%
남 96
 
5.4%
도 91
 
5.1%
시 90
 
5.1%
서 90
 
5.1%
충 89
 
5.0%
청 89
 
5.0%
- 65
 
3.7%
1 61
 
3.5%
Other values (72) 656
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1045
59.1%
Decimal Number 340
 
19.2%
Space Separator 318
 
18.0%
Dash Punctuation 65
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 123
11.8%
남 96
 
9.2%
도 91
 
8.7%
시 90
 
8.6%
서 90
 
8.6%
충 89
 
8.5%
청 89
 
8.5%
리 47
 
4.5%
동 46
 
4.4%
면 36
 
3.4%
Other values (60) 248
23.7%
Decimal Number
ValueCountFrequency (%)
1 61
17.9%
3 41
12.1%
6 38
11.2%
2 34
10.0%
5 33
9.7%
4 32
9.4%
9 32
9.4%
8 26
7.6%
7 22
 
6.5%
0 21
 
6.2%
Space Separator
ValueCountFrequency (%)
318
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1045
59.1%
Common 723
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 123
11.8%
남 96
 
9.2%
도 91
 
8.7%
시 90
 
8.6%
서 90
 
8.6%
충 89
 
8.5%
청 89
 
8.5%
리 47
 
4.5%
동 46
 
4.4%
면 36
 
3.4%
Other values (60) 248
23.7%
Common
ValueCountFrequency (%)
318
44.0%
- 65
 
9.0%
1 61
 
8.4%
3 41
 
5.7%
6 38
 
5.3%
2 34
 
4.7%
5 33
 
4.6%
4 32
 
4.4%
9 32
 
4.4%
8 26
 
3.6%
Other values (2) 43
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1045
59.1%
ASCII 723
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
318
44.0%
- 65
 
9.0%
1 61
 
8.4%
3 41
 
5.7%
6 38
 
5.3%
2 34
 
4.7%
5 33
 
4.6%
4 32
 
4.4%
9 32
 
4.4%
8 26
 
3.6%
Other values (2) 43
 
5.9%
Hangul
ValueCountFrequency (%)
산 123
11.8%
남 96
 
9.2%
도 91
 
8.7%
시 90
 
8.6%
서 90
 
8.6%
충 89
 
8.5%
청 89
 
8.5%
리 47
 
4.5%
동 46
 
4.4%
면 36
 
3.4%
Other values (60) 248
23.7%

위치설명
Text

MISSING 

Distinct89
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Memory size852.0 B
2024-01-10T05:57:09.120767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.9438202
Min length6

Characters and Unicode

Total characters796
Distinct characters136
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)100.0%

Sample

1st row의료원사거리게시대A
2nd row의료원사거리게시대B
3rd row의료원사거리게시대C
4th row의료원사거리게시대D
5th row석림육교게시대A
ValueCountFrequency (%)
의료원사거리게시대a 1
 
1.1%
해미지구대게시대 1
 
1.1%
캐슬아파트입구b 1
 
1.1%
창리사거리게시대 1
 
1.1%
영탑리게시대 1
 
1.1%
화곡삼거리게시대b 1
 
1.1%
오스카빌아파트게시대b 1
 
1.1%
세창아파트입구게시대b 1
 
1.1%
캐슬아파트입구a 1
 
1.1%
잠홍삼거리게시대a 1
 
1.1%
Other values (79) 79
88.8%
2024-01-10T05:57:09.434178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 95
 
11.9%
시 88
 
11.1%
게 86
 
10.8%
리 48
 
6.0%
거 40
 
5.0%
사 27
 
3.4%
A 26
 
3.3%
B 22
 
2.8%
삼 17
 
2.1%
아 14
 
1.8%
Other values (126) 333
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 739
92.8%
Uppercase Letter 57
 
7.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 95
 
12.9%
시 88
 
11.9%
게 86
 
11.6%
리 48
 
6.5%
거 40
 
5.4%
사 27
 
3.7%
삼 17
 
2.3%
아 14
 
1.9%
원 12
 
1.6%
트 12
 
1.6%
Other values (119) 300
40.6%
Uppercase Letter
ValueCountFrequency (%)
A 26
45.6%
B 22
38.6%
C 4
 
7.0%
D 2
 
3.5%
E 1
 
1.8%
G 1
 
1.8%
F 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 739
92.8%
Latin 57
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 95
 
12.9%
시 88
 
11.9%
게 86
 
11.6%
리 48
 
6.5%
거 40
 
5.4%
사 27
 
3.7%
삼 17
 
2.3%
아 14
 
1.9%
원 12
 
1.6%
트 12
 
1.6%
Other values (119) 300
40.6%
Latin
ValueCountFrequency (%)
A 26
45.6%
B 22
38.6%
C 4
 
7.0%
D 2
 
3.5%
E 1
 
1.8%
G 1
 
1.8%
F 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 739
92.8%
ASCII 57
 
7.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 95
 
12.9%
시 88
 
11.9%
게 86
 
11.6%
리 48
 
6.5%
거 40
 
5.4%
사 27
 
3.7%
삼 17
 
2.3%
아 14
 
1.9%
원 12
 
1.6%
트 12
 
1.6%
Other values (119) 300
40.6%
ASCII
ValueCountFrequency (%)
A 26
45.6%
B 22
38.6%
C 4
 
7.0%
D 2
 
3.5%
E 1
 
1.8%
G 1
 
1.8%
F 1
 
1.8%

열수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size852.0 B
5
84 
2
 
3
3
 
2
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0333333
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 84
93.3%
2 3
 
3.3%
3 2
 
2.2%
<NA> 1
 
1.1%

Length

2024-01-10T05:57:09.548179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:09.641231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 84
93.3%
2 3
 
3.3%
3 2
 
2.2%
na 1
 
1.1%

수량
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size852.0 B
1
89 
89
 
1

Length

Max length2
Median length1
Mean length1.0111111
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 89
98.9%
89 1
 
1.1%

Length

2024-01-10T05:57:09.734270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:09.819951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 89
98.9%
89 1
 
1.1%

가능매수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size852.0 B
5
84 
2
 
3
3
 
2
432
 
1

Length

Max length3
Median length1
Mean length1.0222222
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 84
93.3%
2 3
 
3.3%
3 2
 
2.2%
432 1
 
1.1%

Length

2024-01-10T05:57:09.916588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:10.012978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 84
93.3%
2 3
 
3.3%
3 2
 
2.2%
432 1
 
1.1%

경도(WGS84좌표)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct88
Distinct (%)98.9%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean36.796956
Minimum36.45154
Maximum36.99976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-01-10T05:57:10.355151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.45154
5-th percentile36.687242
Q136.76405
median36.78455
Q336.82999
95-th percentile36.972784
Maximum36.99976
Range0.54822
Interquartile range (IQR)0.06594

Descriptive statistics

Standard deviation0.086191822
Coefficient of variation (CV)0.0023423629
Kurtosis2.8881525
Mean36.796956
Median Absolute Deviation (MAD)0.02763
Skewness-0.058831105
Sum3274.929
Variance0.0074290301
MonotonicityNot monotonic
2024-01-10T05:57:10.481330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.76386 2
 
2.2%
36.77497 1
 
1.1%
36.77118 1
 
1.1%
36.77813 1
 
1.1%
36.62751 1
 
1.1%
36.92985 1
 
1.1%
36.98896 1
 
1.1%
36.84506 1
 
1.1%
36.78667 1
 
1.1%
36.77817 1
 
1.1%
Other values (78) 78
86.7%
ValueCountFrequency (%)
36.45154 1
1.1%
36.61489 1
1.1%
36.62751 1
1.1%
36.67122 1
1.1%
36.68531 1
1.1%
36.69014 1
1.1%
36.70578 1
1.1%
36.70856 1
1.1%
36.71216 1
1.1%
36.71716 1
1.1%
ValueCountFrequency (%)
36.99976 1
1.1%
36.989 1
1.1%
36.98896 1
1.1%
36.98843 1
1.1%
36.97612 1
1.1%
36.96778 1
1.1%
36.96765 1
1.1%
36.94201 1
1.1%
36.93769 1
1.1%
36.93758 1
1.1%

위도(WGS84좌표)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)89.9%
Missing1
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean126.45628
Minimum126.3502
Maximum126.5795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-01-10T05:57:10.610963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.3502
5-th percentile126.39002
Q1126.4357
median126.4498
Q3126.4674
95-th percentile126.5443
Maximum126.5795
Range0.2293
Interquartile range (IQR)0.0317

Descriptive statistics

Standard deviation0.042932045
Coefficient of variation (CV)0.00033950109
Kurtosis1.5498283
Mean126.45628
Median Absolute Deviation (MAD)0.0163
Skewness0.6998419
Sum11254.609
Variance0.0018431605
MonotonicityNot monotonic
2024-01-10T05:57:10.731331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4646 3
 
3.3%
126.4627 2
 
2.2%
126.4722 2
 
2.2%
126.4415 2
 
2.2%
126.4338 2
 
2.2%
126.4674 2
 
2.2%
126.4431 2
 
2.2%
126.4806 2
 
2.2%
126.46428 1
 
1.1%
126.3502 1
 
1.1%
Other values (70) 70
77.8%
ValueCountFrequency (%)
126.3502 1
1.1%
126.3681 1
1.1%
126.3702 1
1.1%
126.376 1
1.1%
126.3883 1
1.1%
126.3926 1
1.1%
126.3954 1
1.1%
126.4114 1
1.1%
126.4125 1
1.1%
126.4127 1
1.1%
ValueCountFrequency (%)
126.5795 1
1.1%
126.5758 1
1.1%
126.5655 1
1.1%
126.5653 1
1.1%
126.5515 1
1.1%
126.5335 1
1.1%
126.5334 1
1.1%
126.5323 1
1.1%
126.5136 1
1.1%
126.5131 1
1.1%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size852.0 B
2016-03-16
89 
<NA>
 
1

Length

Max length10
Median length10
Mean length9.9333333
Min length4

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row2016-03-16
2nd row2016-03-16
3rd row2016-03-16
4th row2016-03-16
5th row2016-03-16

Common Values

ValueCountFrequency (%)
2016-03-16 89
98.9%
<NA> 1
 
1.1%

Length

2024-01-10T05:57:10.856932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:10.969347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-03-16 89
98.9%
na 1
 
1.1%

Interactions

2024-01-10T05:57:06.892071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:06.438110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:06.649069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:06.975558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:06.504284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:06.724590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:07.064707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:06.575747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:06.810804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:57:11.047560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호지번주소위치설명열수수량가능매수경도(WGS84좌표)위도(WGS84좌표)
관리번호1.0000.9531.0000.253NaN0.2530.6640.664
지번주소0.9531.0001.0001.000NaN1.0000.9841.000
위치설명1.0001.0001.0001.000NaN1.0001.0001.000
열수0.2531.0001.0001.000NaN1.0000.7610.000
수량NaNNaNNaNNaN1.0001.000NaNNaN
가능매수0.2531.0001.0001.0001.0001.0000.7610.000
경도(WGS84좌표)0.6640.9841.0000.761NaN0.7611.0000.650
위도(WGS84좌표)0.6641.0001.0000.000NaN0.0000.6501.000
2024-01-10T05:57:11.172576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관 연락처관리기관수량데이터기준일자가능매수열수
관리기관 연락처1.0001.0001.0001.0001.0001.000
관리기관1.0001.0001.0001.0001.0001.000
수량1.0001.0001.0001.0000.9891.000
데이터기준일자1.0001.0001.0001.0001.0001.000
가능매수1.0001.0000.9891.0001.0001.000
열수1.0001.0001.0001.0001.0001.000
2024-01-10T05:57:11.277567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호경도(WGS84좌표)위도(WGS84좌표)관리기관관리기관 연락처열수수량가능매수데이터기준일자
관리번호1.0000.073-0.0661.0001.0000.1581.0000.1581.000
경도(WGS84좌표)0.0731.000-0.3081.0001.0000.4491.0000.4491.000
위도(WGS84좌표)-0.066-0.3081.0001.0001.0000.0001.0000.0001.000
관리기관1.0001.0001.0001.0001.0001.0001.0001.0001.000
관리기관 연락처1.0001.0001.0001.0001.0001.0001.0001.0001.000
열수0.1580.4490.0001.0001.0001.0001.0001.0001.000
수량1.0001.0001.0001.0001.0001.0001.0000.9891.000
가능매수0.1580.4490.0001.0001.0001.0000.9891.0001.000
데이터기준일자1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-01-10T05:57:07.174452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:57:07.324810image/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.
2024-01-10T05:57:07.456457image/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

관리번호관리기관관리기관 연락처지번주소위치설명열수수량가능매수경도(WGS84좌표)위도(WGS84좌표)데이터기준일자
01서산시청041-660-3094충청남도 서산시 석림동 952의료원사거리게시대A51536.77497126.464282016-03-16
12서산시청041-660-3094충청남도 서산시 석림동 952의료원사거리게시대B51536.45154126.452012016-03-16
23서산시청041-660-3094충청남도 서산시 석림동 952의료원사거리게시대C51536.7738126.46272016-03-16
34서산시청041-660-3094충청남도 서산시 석림동 952의료원사거리게시대D51536.77379126.46272016-03-16
45서산시청041-660-3094충청남도 서산시 석림동 317석림육교게시대A51536.76984126.47222016-03-16
56서산시청041-660-3094충청남도 서산시 석림동 317석림육교게시대B51536.76977126.47222016-03-16
67서산시청041-660-3094충청남도 서산시 예천동 산7-69예천사거리게시대A51536.77306126.44152016-03-16
78서산시청041-660-3094충청남도 서산시 예천동 산7-69예천사거리게시대B51536.77308126.44172016-03-16
89서산시청041-660-3094충청남도 서산시 예천동 산7-69예천사거리게시대C51536.77311126.44192016-03-16
910서산시청041-660-3094충청남도 서산시 석남동 35-13영진크로바아파트게시대A51536.76947126.45462016-03-16
관리번호관리기관관리기관 연락처지번주소위치설명열수수량가능매수경도(WGS84좌표)위도(WGS84좌표)데이터기준일자
8081서산시청041-660-3094충청남도 서산시 석림동 565-10의료원사거리게시대F21236.77538126.46462016-03-16
8182서산시청041-660-3094충청남도 서산시 석림동 565-10의료원사거리게시대G21236.77525126.46462016-03-16
8283서산시청041-660-3094충청남도 서산시 동문동 1043-14코아루후문게시대A51536.78657126.46332016-03-16
8384서산시청041-660-3094충청남도 서산시 동문동 1043-14코아루후문게시대B51536.78672126.46322016-03-16
8485서산시청041-660-3094충청남도 서산시 읍내동 609-7맛이나가든게시대B51536.78455126.44042016-03-16
8586서산시청041-660-3094충청남도 서산시 예천동 464-5예천주공사거리게시대B51536.77096126.44772016-03-16
8687서산시청041-660-3094충청남도 서산시 석남동 577-3석지제사거리게시대A51536.76112126.45362016-03-16
8788서산시청041-660-3094충청남도 서산시 석남동 577-3석지제사거리게시대B51536.76106126.45352016-03-16
8889서산시청041-660-3094충청남도 서산시 대산읍 대산리 243-33대산농협하나로마트게시대51536.94201126.432016-03-16
89<NA><NA><NA><NA><NA><NA>89432<NA><NA><NA>