Overview

Dataset statistics

Number of variables8
Number of observations34
Missing cells36
Missing cells (%)13.2%
Duplicate rows6
Duplicate rows (%)17.6%
Total size in memory2.4 KiB
Average record size in memory70.9 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description부산 동구 관내 현수막 게시대 현황(설치장소, 설치수량, 형태, 규격, 게시건 수)-민원용(상업용) 및 공공용(행정용) 게시대, 저단형 행정용 게시대
Author부산광역시 동구
URLhttps://www.data.go.kr/data/3080990/fileData.do

Alerts

설치수량 has constant value ""Constant
Dataset has 6 (17.6%) duplicate rowsDuplicates
형 태 is highly overall correlated with 좌표값(위도) and 4 other fieldsHigh correlation
게시건수 is highly overall correlated with 좌표값(위도) and 4 other fieldsHigh correlation
규 격(m) is highly overall correlated with 좌표값(위도) and 4 other fieldsHigh correlation
좌표값(위도) is highly overall correlated with 좌표값(경도) and 4 other fieldsHigh correlation
좌표값(경도) is highly overall correlated with 좌표값(위도) and 4 other fieldsHigh correlation
용도 is highly overall correlated with 좌표값(위도) and 4 other fieldsHigh correlation
좌표값(위도) has 18 (52.9%) missing valuesMissing
좌표값(경도) has 18 (52.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 07:55:55.210751
Analysis finished2023-12-12 07:55:56.342012
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

용도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
공공용
18 
민원용
16 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공용 18
52.9%
민원용 16
47.1%

Length

2023-12-12T16:55:56.448547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:55:56.552425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공용 18
52.9%
민원용 16
47.1%

설 치 장 소
Text

Distinct25
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T16:55:56.779532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length14.088235
Min length7

Characters and Unicode

Total characters479
Distinct characters103
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

Unique19 ?
Unique (%)55.9%

Sample

1st row부산역 충장대로변 선상주차장 입구
2nd row초량 메리츠화재 건물옆
3rd row윤흥신석상 옆 도로변
4th row지하철 좌천역 2번 출구 인근
5th row좌천1동 OB신호대 앞
ValueCountFrequency (%)
앞 9
 
8.1%
범일2동 8
 
7.2%
지하철 5
 
4.5%
2번 5
 
4.5%
출구 5
 
4.5%
인근 5
 
4.5%
좌천역 5
 
4.5%
충장대로변 3
 
2.7%
맞은편 3
 
2.7%
인도 3
 
2.7%
Other values (47) 60
54.1%
2023-12-12T16:55:57.197674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
16.1%
동 23
 
4.8%
2 17
 
3.5%
지 14
 
2.9%
앞 13
 
2.7%
일 12
 
2.5%
인 12
 
2.5%
범 12
 
2.5%
도 11
 
2.3%
구 11
 
2.3%
Other values (93) 277
57.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 366
76.4%
Space Separator 77
 
16.1%
Decimal Number 22
 
4.6%
Uppercase Letter 6
 
1.3%
Open Punctuation 4
 
0.8%
Close Punctuation 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
 
6.3%
지 14
 
3.8%
앞 13
 
3.6%
일 12
 
3.3%
인 12
 
3.3%
범 12
 
3.3%
도 11
 
3.0%
구 11
 
3.0%
장 11
 
3.0%
로 11
 
3.0%
Other values (84) 236
64.5%
Uppercase Letter
ValueCountFrequency (%)
T 2
33.3%
K 2
33.3%
O 1
16.7%
B 1
16.7%
Decimal Number
ValueCountFrequency (%)
2 17
77.3%
1 5
 
22.7%
Space Separator
ValueCountFrequency (%)
77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 366
76.4%
Common 107
 
22.3%
Latin 6
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
 
6.3%
지 14
 
3.8%
앞 13
 
3.6%
일 12
 
3.3%
인 12
 
3.3%
범 12
 
3.3%
도 11
 
3.0%
구 11
 
3.0%
장 11
 
3.0%
로 11
 
3.0%
Other values (84) 236
64.5%
Common
ValueCountFrequency (%)
77
72.0%
2 17
 
15.9%
1 5
 
4.7%
( 4
 
3.7%
) 4
 
3.7%
Latin
ValueCountFrequency (%)
T 2
33.3%
K 2
33.3%
O 1
16.7%
B 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 366
76.4%
ASCII 113
 
23.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
68.1%
2 17
 
15.0%
1 5
 
4.4%
( 4
 
3.5%
) 4
 
3.5%
T 2
 
1.8%
K 2
 
1.8%
O 1
 
0.9%
B 1
 
0.9%
Hangul
ValueCountFrequency (%)
동 23
 
6.3%
지 14
 
3.8%
앞 13
 
3.6%
일 12
 
3.3%
인 12
 
3.3%
범 12
 
3.3%
도 11
 
3.0%
구 11
 
3.0%
장 11
 
3.0%
로 11
 
3.0%
Other values (84) 236
64.5%

설치수량
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
1
34 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 34
100.0%

Length

2023-12-12T16:55:57.332813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:55:57.416701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 34
100.0%

형 태
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
반자동접이형
20 
저단형
14 

Length

Max length6
Median length6
Mean length4.7647059
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
반자동접이형 20
58.8%
저단형 14
41.2%

Length

2023-12-12T16:55:57.511117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:55:57.623181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
반자동접이형 20
58.8%
저단형 14
41.2%

규 격(m)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
6.2 × 6.26
18 
6.2 × 1.10
14 
6.2 × 3.50
2 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.2 × 6.26
2nd row6.2 × 6.26
3rd row6.2 × 6.26
4th row6.2 × 6.26
5th row6.2 × 6.26

Common Values

ValueCountFrequency (%)
6.2 × 6.26 18
52.9%
6.2 × 1.10 14
41.2%
6.2 × 3.50 2
 
5.9%

Length

2023-12-12T16:55:57.719726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:55:57.836148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6.2 34
33.3%
× 34
33.3%
6.26 18
17.6%
1.10 14
13.7%
3.50 2
 
2.0%

게시건수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
1개
14 
5개
9 
6개
9 
3개
2 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5개
2nd row5개
3rd row5개
4th row6개
5th row6개

Common Values

ValueCountFrequency (%)
1개 14
41.2%
5개 9
26.5%
6개 9
26.5%
3개 2
 
5.9%

Length

2023-12-12T16:55:57.927598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:55:58.015764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1개 14
41.2%
5개 9
26.5%
6개 9
26.5%
3개 2
 
5.9%

좌표값(위도)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)75.0%
Missing18
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean35.129638
Minimum35.116739
Maximum35.141816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T16:55:58.102265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.116739
5-th percentile35.118616
Q135.123628
median35.132509
Q335.133687
95-th percentile35.136263
Maximum35.141816
Range0.025077
Interquartile range (IQR)0.010059

Descriptive statistics

Standard deviation0.006702505
Coefficient of variation (CV)0.00019079346
Kurtosis-0.35148816
Mean35.129638
Median Absolute Deviation (MAD)0.0019025
Skewness-0.41529925
Sum562.07421
Variance4.4923574 × 10-5
MonotonicityNot monotonic
2023-12-12T16:55:58.205648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
35.133459 3
 
8.8%
35.134412 2
 
5.9%
35.123418 2
 
5.9%
35.116739 1
 
2.9%
35.123698 1
 
2.9%
35.126637 1
 
2.9%
35.132276 1
 
2.9%
35.132743 1
 
2.9%
35.141816 1
 
2.9%
35.134371 1
 
2.9%
Other values (2) 2
 
5.9%
(Missing) 18
52.9%
ValueCountFrequency (%)
35.116739 1
 
2.9%
35.119241 1
 
2.9%
35.123418 2
5.9%
35.123698 1
 
2.9%
35.126637 1
 
2.9%
35.130647 1
 
2.9%
35.132276 1
 
2.9%
35.132743 1
 
2.9%
35.133459 3
8.8%
35.134371 1
 
2.9%
ValueCountFrequency (%)
35.141816 1
 
2.9%
35.134412 2
5.9%
35.134371 1
 
2.9%
35.133459 3
8.8%
35.132743 1
 
2.9%
35.132276 1
 
2.9%
35.130647 1
 
2.9%
35.126637 1
 
2.9%
35.123698 1
 
2.9%
35.123418 2
5.9%

좌표값(경도)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)75.0%
Missing18
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean129.05001
Minimum129.02944
Maximum129.05881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T16:55:58.302465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.02944
5-th percentile129.03764
Q1129.04625
median129.05195
Q3129.05447
95-th percentile129.05881
Maximum129.05881
Range0.029366
Interquartile range (IQR)0.00822375

Descriptive statistics

Standard deviation0.0077523717
Coefficient of variation (CV)6.0072618 × 10-5
Kurtosis2.0274116
Mean129.05001
Median Absolute Deviation (MAD)0.005052
Skewness-1.2318476
Sum2064.8001
Variance6.0099268 × 10-5
MonotonicityNot monotonic
2023-12-12T16:55:58.400070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
129.05377 3
 
8.8%
129.05881 2
 
5.9%
129.04809 2
 
5.9%
129.044462 1
 
2.9%
129.045567 1
 
2.9%
129.046476 1
 
2.9%
129.052178 1
 
2.9%
129.051725 1
 
2.9%
129.05658 1
 
2.9%
129.058199 1
 
2.9%
Other values (2) 2
 
5.9%
(Missing) 18
52.9%
ValueCountFrequency (%)
129.029444 1
 
2.9%
129.040367 1
 
2.9%
129.044462 1
 
2.9%
129.045567 1
 
2.9%
129.046476 1
 
2.9%
129.04809 2
5.9%
129.051725 1
 
2.9%
129.052178 1
 
2.9%
129.05377 3
8.8%
129.05658 1
 
2.9%
ValueCountFrequency (%)
129.05881 2
5.9%
129.058199 1
 
2.9%
129.05658 1
 
2.9%
129.05377 3
8.8%
129.052178 1
 
2.9%
129.051725 1
 
2.9%
129.04809 2
5.9%
129.046476 1
 
2.9%
129.045567 1
 
2.9%
129.044462 1
 
2.9%

Interactions

2023-12-12T16:55:55.798656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:55:55.572611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:55:55.896985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:55:55.667748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:55:58.484532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도설 치 장 소형 태규 격(m)게시건수좌표값(위도)좌표값(경도)
용도1.0000.8010.9040.5170.947NaNNaN
설 치 장 소0.8011.0000.7920.9500.9331.0001.000
형 태0.9040.7921.0001.0001.000NaNNaN
규 격(m)0.5170.9501.0001.0001.0000.6821.000
게시건수0.9470.9331.0001.0001.0000.7180.978
좌표값(위도)NaN1.000NaN0.6820.7181.0000.840
좌표값(경도)NaN1.000NaN1.0000.9780.8401.000
2023-12-12T16:55:58.593732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
형 태게시건수용도규 격(m)
형 태1.0000.9680.7190.984
게시건수0.9681.0000.7650.984
용도0.7190.7651.0000.768
규 격(m)0.9840.9840.7681.000
2023-12-12T16:55:58.675755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
좌표값(위도)좌표값(경도)용도형 태규 격(m)게시건수
좌표값(위도)1.0000.9051.0001.0000.5800.515
좌표값(경도)0.9051.0001.0001.0000.8020.663
용도1.0001.0001.0000.7190.7680.765
형 태1.0001.0000.7191.0000.9840.968
규 격(m)0.5800.8020.7680.9841.0000.984
게시건수0.5150.6630.7650.9680.9841.000

Missing values

2023-12-12T16:55:56.025677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:55:56.159161image/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.
2023-12-12T16:55:56.273408image/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

용도설 치 장 소설치수량형 태규 격(m)게시건수좌표값(위도)좌표값(경도)
0민원용부산역 충장대로변 선상주차장 입구1반자동접이형6.2 × 6.265개35.116739129.044462
1민원용초량 메리츠화재 건물옆1반자동접이형6.2 × 6.265개35.123698129.045567
2민원용윤흥신석상 옆 도로변1반자동접이형6.2 × 6.265개35.126637129.046476
3민원용지하철 좌천역 2번 출구 인근1반자동접이형6.2 × 6.266개35.133459129.05377
4민원용좌천1동 OB신호대 앞1반자동접이형6.2 × 6.266개35.132276129.052178
5민원용동원아파트 앞 인도1반자동접이형6.2 × 6.265개35.132743129.051725
6민원용범일동 신극동정비 앞(구 보림극장 맞은편)1반자동접이형6.2 × 6.266개35.141816129.05658
7민원용범일2동 성남초교옆1반자동접이형6.2 × 6.265개35.134412129.05881
8민원용범일2동 성남초교옆1반자동접이형6.2 × 6.265개35.134412129.05881
9민원용범일2동 성남초교담장옆 난간쪽1반자동접이형6.2 × 6.266개35.134371129.058199
용도설 치 장 소설치수량형 태규 격(m)게시건수좌표값(위도)좌표값(경도)
24공공용좌천동 성남초등학교위 고가도로1저단형6.2 × 1.101개<NA><NA>
25공공용범일2동 KT남부산지사 앞1저단형6.2 × 1.101개<NA><NA>
26공공용범일1동 서광교회 앞1저단형6.2 × 1.101개<NA><NA>
27공공용범일1동 구보림극장옆1저단형6.2 × 1.101개<NA><NA>
28공공용범일2동 신한은행 범일동지점 앞1저단형6.2 × 1.101개<NA><NA>
29공공용범일2동 KT남부산지사 앞1저단형6.2 × 1.101개<NA><NA>
30공공용범일2동 조선통신사 역사관앞1저단형6.2 × 1.101개<NA><NA>
31공공용범일2동 불교전시관 앞1저단형6.2 × 1.101개<NA><NA>
32공공용금수사 삼거리1저단형6.2 × 1.101개<NA><NA>
33공공용금수사 삼거리1저단형6.2 × 1.101개<NA><NA>

Duplicate rows

Most frequently occurring

용도설 치 장 소설치수량형 태규 격(m)게시건수좌표값(위도)좌표값(경도)# duplicates
5민원용지하철 좌천역 2번 출구 인근1반자동접이형6.2 × 6.266개35.133459129.053773
0공공용금수사 삼거리1저단형6.2 × 1.101개<NA><NA>2
1공공용동원아파트앞 인도1반자동접이형6.2 × 6.266개<NA><NA>2
2공공용범일2동 KT남부산지사 앞1저단형6.2 × 1.101개<NA><NA>2
3공공용지하철 좌천역 2번 출구 인근1저단형6.2 × 1.101개<NA><NA>2
4민원용범일2동 성남초교옆1반자동접이형6.2 × 6.265개35.134412129.058812