Overview

Dataset statistics

Number of variables11
Number of observations96
Missing cells8
Missing cells (%)0.8%
Duplicate rows1
Duplicate rows (%)1.0%
Total size in memory9.0 KiB
Average record size in memory96.4 B

Variable types

Text2
Numeric2
Categorical7

Dataset

Description경기도 김포시 현수막지정게시대(게시대명칭, 소재지지변주소, 위도, 경도, 행정동, 부착일, 금액, 가로규격,세로규격 면수 데이터기준일자)의 데이터 정보를 제공합니다.
Author경기도 김포시
URLhttps://www.data.go.kr/data/15034881/fileData.do

Alerts

Dataset has 1 (1.0%) duplicate rowsDuplicates
금액 is highly overall correlated with 위도 and 7 other fieldsHigh correlation
행정동 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
부착일 is highly overall correlated with 위도 and 7 other fieldsHigh correlation
면수 is highly overall correlated with 부착일 and 4 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 위도 and 7 other fieldsHigh correlation
세로규격(센티미터) is highly overall correlated with 위도 and 7 other fieldsHigh correlation
가로규격(센티미터) is highly overall correlated with 위도 and 7 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 6 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
부착일 is highly imbalanced (85.4%)Imbalance
금액 is highly imbalanced (85.4%)Imbalance
가로규격(센티미터) is highly imbalanced (85.4%)Imbalance
세로규격(센티미터) is highly imbalanced (85.4%)Imbalance
면수 is highly imbalanced (85.5%)Imbalance
데이터기준일자 is highly imbalanced (85.4%)Imbalance
게시대명칭 has 2 (2.1%) missing valuesMissing
소재지지번주소 has 2 (2.1%) missing valuesMissing
위도 has 2 (2.1%) missing valuesMissing
경도 has 2 (2.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:33:24.205502
Analysis finished2023-12-12 13:33:25.618601
Duration1.41 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

게시대명칭
Text

MISSING 

Distinct94
Distinct (%)100.0%
Missing2
Missing (%)2.1%
Memory size900.0 B
2023-12-12T22:33:25.817778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.9893617
Min length2

Characters and Unicode

Total characters751
Distinct characters132
Distinct categories6 ?
Distinct scripts3 ?
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 row운동장정문1
2nd row운동장정문2
3rd row유현사거리
4th row원당고개1
5th row원당고개2
ValueCountFrequency (%)
나진교 2
 
2.0%
장기한강로입구 2
 
2.0%
우리병원입구 1
 
1.0%
대곶사거리(우)1 1
 
1.0%
터미널삼거리2 1
 
1.0%
양촌마산동삼거리2 1
 
1.0%
양촌마산동삼거리1 1
 
1.0%
양촌산업단지입구(우 1
 
1.0%
양촌산업단지입구(좌 1
 
1.0%
구래동주민센타사거리 1
 
1.0%
Other values (90) 90
88.2%
2023-12-12T22:33:26.249899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 66
 
8.8%
거 55
 
7.3%
사 41
 
5.5%
구 23
 
3.1%
2 22
 
2.9%
입 22
 
2.9%
1 21
 
2.8%
장 19
 
2.5%
삼 18
 
2.4%
) 17
 
2.3%
Other values (122) 447
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 656
87.4%
Decimal Number 43
 
5.7%
Close Punctuation 17
 
2.3%
Open Punctuation 17
 
2.3%
Uppercase Letter 10
 
1.3%
Space Separator 8
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 66
 
10.1%
거 55
 
8.4%
사 41
 
6.2%
구 23
 
3.5%
입 22
 
3.4%
장 19
 
2.9%
삼 18
 
2.7%
기 16
 
2.4%
교 16
 
2.4%
양 15
 
2.3%
Other values (112) 365
55.6%
Uppercase Letter
ValueCountFrequency (%)
A 6
60.0%
C 1
 
10.0%
H 1
 
10.0%
I 1
 
10.0%
L 1
 
10.0%
Decimal Number
ValueCountFrequency (%)
2 22
51.2%
1 21
48.8%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 656
87.4%
Common 85
 
11.3%
Latin 10
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 66
 
10.1%
거 55
 
8.4%
사 41
 
6.2%
구 23
 
3.5%
입 22
 
3.4%
장 19
 
2.9%
삼 18
 
2.7%
기 16
 
2.4%
교 16
 
2.4%
양 15
 
2.3%
Other values (112) 365
55.6%
Common
ValueCountFrequency (%)
2 22
25.9%
1 21
24.7%
) 17
20.0%
( 17
20.0%
8
 
9.4%
Latin
ValueCountFrequency (%)
A 6
60.0%
C 1
 
10.0%
H 1
 
10.0%
I 1
 
10.0%
L 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 656
87.4%
ASCII 95
 
12.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 66
 
10.1%
거 55
 
8.4%
사 41
 
6.2%
구 23
 
3.5%
입 22
 
3.4%
장 19
 
2.9%
삼 18
 
2.7%
기 16
 
2.4%
교 16
 
2.4%
양 15
 
2.3%
Other values (112) 365
55.6%
ASCII
ValueCountFrequency (%)
2 22
23.2%
1 21
22.1%
) 17
17.9%
( 17
17.9%
8
 
8.4%
A 6
 
6.3%
C 1
 
1.1%
H 1
 
1.1%
I 1
 
1.1%
L 1
 
1.1%

소재지지번주소
Text

MISSING 

Distinct82
Distinct (%)87.2%
Missing2
Missing (%)2.1%
Memory size900.0 B
2023-12-12T22:33:26.544499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length19.095745
Min length10

Characters and Unicode

Total characters1795
Distinct characters60
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

Unique70 ?
Unique (%)74.5%

Sample

1st row경기도 김포시 사우동152번지
2nd row경기도 김포시 사우동152번지
3rd row경기도 김포시 풍무동 762
4th row경기도 김포시 풍무동437-15번지
5th row경기도 김포시 풍무동437-15번지
ValueCountFrequency (%)
경기도 89
28.3%
김포시 89
28.3%
양촌읍 12
 
3.8%
고촌읍 10
 
3.2%
대곶면 5
 
1.6%
통진읍 4
 
1.3%
풍무동437-15번지 2
 
0.6%
장기동917-5번지 2
 
0.6%
월곶면 2
 
0.6%
사우동152번지 2
 
0.6%
Other values (87) 98
31.1%
2023-12-12T22:33:26.989614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
221
 
12.3%
기 114
 
6.4%
포 93
 
5.2%
1 93
 
5.2%
도 90
 
5.0%
경 89
 
5.0%
김 89
 
5.0%
시 89
 
5.0%
번 86
 
4.8%
지 86
 
4.8%
Other values (50) 745
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1093
60.9%
Decimal Number 404
 
22.5%
Space Separator 221
 
12.3%
Dash Punctuation 77
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 114
10.4%
포 93
 
8.5%
도 90
 
8.2%
경 89
 
8.1%
김 89
 
8.1%
시 89
 
8.1%
번 86
 
7.9%
지 86
 
7.9%
동 60
 
5.5%
리 34
 
3.1%
Other values (38) 263
24.1%
Decimal Number
ValueCountFrequency (%)
1 93
23.0%
2 60
14.9%
3 44
10.9%
5 42
10.4%
4 33
 
8.2%
7 29
 
7.2%
8 27
 
6.7%
6 27
 
6.7%
9 25
 
6.2%
0 24
 
5.9%
Space Separator
ValueCountFrequency (%)
221
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1093
60.9%
Common 702
39.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 114
10.4%
포 93
 
8.5%
도 90
 
8.2%
경 89
 
8.1%
김 89
 
8.1%
시 89
 
8.1%
번 86
 
7.9%
지 86
 
7.9%
동 60
 
5.5%
리 34
 
3.1%
Other values (38) 263
24.1%
Common
ValueCountFrequency (%)
221
31.5%
1 93
13.2%
- 77
 
11.0%
2 60
 
8.5%
3 44
 
6.3%
5 42
 
6.0%
4 33
 
4.7%
7 29
 
4.1%
8 27
 
3.8%
6 27
 
3.8%
Other values (2) 49
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1093
60.9%
ASCII 702
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221
31.5%
1 93
13.2%
- 77
 
11.0%
2 60
 
8.5%
3 44
 
6.3%
5 42
 
6.0%
4 33
 
4.7%
7 29
 
4.1%
8 27
 
3.8%
6 27
 
3.8%
Other values (2) 49
 
7.0%
Hangul
ValueCountFrequency (%)
기 114
10.4%
포 93
 
8.5%
도 90
 
8.2%
경 89
 
8.1%
김 89
 
8.1%
시 89
 
8.1%
번 86
 
7.9%
지 86
 
7.9%
동 60
 
5.5%
리 34
 
3.1%
Other values (38) 263
24.1%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct91
Distinct (%)96.8%
Missing2
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean126.67892
Minimum126.57013
Maximum126.78455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T22:33:27.413366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.57013
5-th percentile126.58887
Q1126.63417
median126.67979
Q3126.71197
95-th percentile126.76736
Maximum126.78455
Range0.2144226
Interquartile range (IQR)0.077801875

Descriptive statistics

Standard deviation0.051950524
Coefficient of variation (CV)0.00041009603
Kurtosis-0.49069308
Mean126.67892
Median Absolute Deviation (MAD)0.0387644
Skewness-0.11741524
Sum11907.819
Variance0.0026988569
MonotonicityNot monotonic
2023-12-12T22:33:27.538724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.738745 2
 
2.1%
126.703559 2
 
2.1%
126.6223754 2
 
2.1%
126.570696 1
 
1.0%
126.602966 1
 
1.0%
126.6276504 1
 
1.0%
126.6317258 1
 
1.0%
126.6316126 1
 
1.0%
126.6301882 1
 
1.0%
126.6302403 1
 
1.0%
Other values (81) 81
84.4%
(Missing) 2
 
2.1%
ValueCountFrequency (%)
126.5701279 1
1.0%
126.570696 1
1.0%
126.5831117 1
1.0%
126.5836664 1
1.0%
126.5839158 1
1.0%
126.5915383 1
1.0%
126.5951249 1
1.0%
126.5952501 1
1.0%
126.5979362 1
1.0%
126.60043 1
1.0%
ValueCountFrequency (%)
126.7845505 1
1.0%
126.7845299 1
1.0%
126.7734918 1
1.0%
126.7722975 1
1.0%
126.7674299 1
1.0%
126.7673161 1
1.0%
126.7589762 1
1.0%
126.7588233 1
1.0%
126.7586426 1
1.0%
126.7559773 1
1.0%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct91
Distinct (%)96.8%
Missing2
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean37.637585
Minimum37.595957
Maximum37.701421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T22:33:27.704653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.595957
5-th percentile37.602229
Q137.621936
median37.637849
Q337.650792
95-th percentile37.686355
Maximum37.701421
Range0.10546425
Interquartile range (IQR)0.0288557

Descriptive statistics

Standard deviation0.023315734
Coefficient of variation (CV)0.00061948009
Kurtosis0.47867438
Mean37.637585
Median Absolute Deviation (MAD)0.01393578
Skewness0.53996817
Sum3537.9329
Variance0.00054362346
MonotonicityNot monotonic
2023-12-12T22:33:27.844768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.610178 2
 
2.1%
37.631594 2
 
2.1%
37.65845592 2
 
2.1%
37.701396 1
 
1.0%
37.6880489 1
 
1.0%
37.65149141 1
 
1.0%
37.63762783 1
 
1.0%
37.63760945 1
 
1.0%
37.61943016 1
 
1.0%
37.61906139 1
 
1.0%
Other values (81) 81
84.4%
(Missing) 2
 
2.1%
ValueCountFrequency (%)
37.59595682 1
1.0%
37.59852858 1
1.0%
37.59954323 1
1.0%
37.6009038 1
1.0%
37.60100411 1
1.0%
37.60288902 1
1.0%
37.60292387 1
1.0%
37.6046203 1
1.0%
37.60493212 1
1.0%
37.60496105 1
1.0%
ValueCountFrequency (%)
37.70142107 1
1.0%
37.701396 1
1.0%
37.69238972 1
1.0%
37.68966489 1
1.0%
37.6880489 1
1.0%
37.6854422 1
1.0%
37.68541666 1
1.0%
37.66549325 1
1.0%
37.66036346 1
1.0%
37.66032751 1
1.0%

행정동
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size900.0 B
장기동
26 
양촌읍
11 
고촌읍
10 
북변동
9 
사우동
7 
Other values (11)
33 

Length

Max length4
Median length3
Mean length3.0208333
Min length3

Unique

Unique2 ?
Unique (%)2.1%

Sample

1st row사우동
2nd row사우동
3rd row풍무동
4th row풍무동
5th row풍무동

Common Values

ValueCountFrequency (%)
장기동 26
27.1%
양촌읍 11
11.5%
고촌읍 10
 
10.4%
북변동 9
 
9.4%
사우동 7
 
7.3%
감정동 5
 
5.2%
대곶면 5
 
5.2%
걸포동 4
 
4.2%
운양동 4
 
4.2%
통진읍 4
 
4.2%
Other values (6) 11
11.5%

Length

2023-12-12T22:33:27.965244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
장기동 26
27.1%
양촌읍 11
11.5%
고촌읍 10
 
10.4%
북변동 9
 
9.4%
사우동 7
 
7.3%
감정동 5
 
5.2%
대곶면 5
 
5.2%
걸포동 4
 
4.2%
운양동 4
 
4.2%
통진읍 4
 
4.2%
Other values (6) 11
11.5%

부착일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
7
94 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.0625
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 94
97.9%
<NA> 2
 
2.1%

Length

2023-12-12T22:33:28.097951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:33:28.197311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7 94
97.9%
na 2
 
2.1%

금액
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
11000
94 
<NA>
 
2

Length

Max length5
Median length5
Mean length4.9791667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11000
2nd row11000
3rd row11000
4th row11000
5th row11000

Common Values

ValueCountFrequency (%)
11000 94
97.9%
<NA> 2
 
2.1%

Length

2023-12-12T22:33:28.292240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:33:28.376629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11000 94
97.9%
na 2
 
2.1%

가로규격(센티미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
600
94 
<NA>
 
2

Length

Max length4
Median length3
Mean length3.0208333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row600
2nd row600
3rd row600
4th row600
5th row600

Common Values

ValueCountFrequency (%)
600 94
97.9%
<NA> 2
 
2.1%

Length

2023-12-12T22:33:28.465924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:33:28.552864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
600 94
97.9%
na 2
 
2.1%

세로규격(센티미터)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
70
94 
<NA>
 
2

Length

Max length4
Median length2
Mean length2.0416667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70
2nd row70
3rd row70
4th row70
5th row70

Common Values

ValueCountFrequency (%)
70 94
97.9%
<NA> 2
 
2.1%

Length

2023-12-12T22:33:28.651071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:33:28.738445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
70 94
97.9%
na 2
 
2.1%

면수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
6
93 
<NA>
 
2
5
 
1

Length

Max length4
Median length1
Mean length1.0625
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 93
96.9%
<NA> 2
 
2.1%
5 1
 
1.0%

Length

2023-12-12T22:33:28.830089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:33:28.914761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 93
96.9%
na 2
 
2.1%
5 1
 
1.0%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-09-15
94 
<NA>
 
2

Length

Max length10
Median length10
Mean length9.875
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-15
2nd row2023-09-15
3rd row2023-09-15
4th row2023-09-15
5th row2023-09-15

Common Values

ValueCountFrequency (%)
2023-09-15 94
97.9%
<NA> 2
 
2.1%

Length

2023-12-12T22:33:28.998115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:33:29.074466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-15 94
97.9%
na 2
 
2.1%

Interactions

2023-12-12T22:33:24.944440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:33:24.783640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:33:25.036134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:33:24.859694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:33:29.124592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
게시대명칭소재지지번주소위도경도행정동면수
게시대명칭1.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.8240.9470.522
경도1.0001.0000.8241.0000.9310.000
행정동1.0001.0000.9470.9311.0000.000
면수1.0001.0000.5220.0000.0001.000
2023-12-12T22:33:29.224702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
금액행정동부착일면수데이터기준일자세로규격(센티미터)가로규격(센티미터)
금액1.0001.0001.0001.0001.0001.0001.000
행정동1.0001.0001.0000.0001.0001.0001.000
부착일1.0001.0001.0001.0001.0001.0001.000
면수1.0000.0001.0001.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.0001.0001.0001.000
세로규격(센티미터)1.0001.0001.0001.0001.0001.0001.000
가로규격(센티미터)1.0001.0001.0001.0001.0001.0001.000
2023-12-12T22:33:29.323144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동부착일금액가로규격(센티미터)세로규격(센티미터)면수데이터기준일자
위도1.000-0.8610.7121.0001.0001.0001.0000.3831.000
경도-0.8611.0000.7051.0001.0001.0001.0000.0001.000
행정동0.7120.7051.0001.0001.0001.0001.0000.0001.000
부착일1.0001.0001.0001.0001.0001.0001.0001.0001.000
금액1.0001.0001.0001.0001.0001.0001.0001.0001.000
가로규격(센티미터)1.0001.0001.0001.0001.0001.0001.0001.0001.000
세로규격(센티미터)1.0001.0001.0001.0001.0001.0001.0001.0001.000
면수0.3830.0000.0001.0001.0001.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T22:33:25.159229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:33:25.336979image/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.
2023-12-12T22:33:25.497327image/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

게시대명칭소재지지번주소위도경도행정동부착일금액가로규격(센티미터)세로규격(센티미터)면수데이터기준일자
0운동장정문1경기도 김포시 사우동152번지126.71983837.618626사우동7110006007062023-09-15
1운동장정문2경기도 김포시 사우동152번지126.7200237.618583사우동7110006007062023-09-15
2유현사거리경기도 김포시 풍무동 762126.72216337.595957풍무동7110006007062023-09-15
3원당고개1경기도 김포시 풍무동437-15번지126.7214137.600904풍무동7110006007062023-09-15
4원당고개2경기도 김포시 풍무동437-15번지126.72152137.601004풍무동7110006007062023-09-15
5태리교차로1경기도 김포시 사우동572-3번지126.73874537.610178사우동7110006007062023-09-15
6태리교차로2경기도 김포시 사우동572-3번지126.73874537.610178사우동7110006007062023-09-15
7천둥고개(서)경기도 김포시 고촌읍 태리1-3번지126.75897637.60462고촌읍7110006007062023-09-15
8한화(A)앞1경기도 김포시 고촌읍 신곡리560-15번지126.76731637.602924고촌읍7110006007062023-09-15
9한화(A)앞2경기도 김포시 고촌읍 신곡리560-6번지126.7674337.602889고촌읍7110006007062023-09-15
게시대명칭소재지지번주소위도경도행정동부착일금액가로규격(센티미터)세로규격(센티미터)면수데이터기준일자
86장기삼성래미안A사거리공원앞경기도 김포시 장기동702-1번지126.67541637.640362장기동7110006007062023-09-15
87장기동외곽도로사거리1경기도 김포시 장기동2083-4번지126.67960637.638544장기동7110006007062023-09-15
88자기동외곽도로사거리2경기도 김포시 장기동2083-4번지126.67970737.638608장기동7110006007062023-09-15
89양곡우회도로 신사거리양촌읍 구래리 12-23126.61626537.655099양촌읍7110006007062023-09-15
90대곶면 초원신사거리대곶면 율생리 41-15126.6004337.654565대곶면7110006007062023-09-15
91검은다리사거리(서울)장기동 898-53126.69000837.63807장기동7110006007062023-09-15
92나진교 일산대교입구장기동 898-68126.69308437.635799장기동7110006007062023-09-15
93나진교 일산대교입구2운양동 213-16126.69383737.635704운양동7110006007062023-09-15
94<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
95<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

게시대명칭소재지지번주소위도경도행정동부착일금액가로규격(센티미터)세로규격(센티미터)면수데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2