Overview

Dataset statistics

Number of variables9
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory79.7 B

Variable types

Categorical5
Text2
Numeric2

Dataset

Description대구광역시 남구 관내 현수막지정게시대 현황(연번,위치,소재지도로명주소,규격,게시면수,비고(총설치면수)) 정보를 제공합니다.
Author대구광역시 남구
URLhttps://www.data.go.kr/data/15094153/fileData.do

Alerts

관리기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
비고(총설치면수) is highly overall correlated with 규격 and 1 other fieldsHigh correlation
게시면수 is highly overall correlated with 규격 and 1 other fieldsHigh correlation
규격 is highly overall correlated with 게시면수 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 12:50:06.323325
Analysis finished2023-12-12 12:50:07.420329
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
대구광역시 남구청
28 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 남구청
2nd row대구광역시 남구청
3rd row대구광역시 남구청
4th row대구광역시 남구청
5th row대구광역시 남구청

Common Values

ValueCountFrequency (%)
대구광역시 남구청 28
100.0%

Length

2023-12-12T21:50:07.513227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:50:07.642022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 28
50.0%
남구청 28
50.0%

위치
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T21:50:07.831690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length7.5357143
Min length4

Characters and Unicode

Total characters211
Distinct characters94
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row대덕성당 앞
2nd row캠프워크 헬기장
3rd row대명9동행정복지센터
4th row현충삼거리
5th row캠프헨리
ValueCountFrequency (%)
앞 7
 
16.3%
대덕성당 3
 
7.0%
현충삼거리 2
 
4.7%
앞산네거리조헌포이비인후과 1
 
2.3%
명덕네거리 1
 
2.3%
건너편1 1
 
2.3%
건너편2 1
 
2.3%
대명역1번출구 1
 
2.3%
국민보험공단남부지사 1
 
2.3%
흥아아파트 1
 
2.3%
Other values (24) 24
55.8%
2023-12-12T21:50:08.181366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
7.6%
대 12
 
5.7%
리 11
 
5.2%
앞 10
 
4.7%
거 10
 
4.7%
네 8
 
3.8%
구 7
 
3.3%
명 5
 
2.4%
1 5
 
2.4%
출 4
 
1.9%
Other values (84) 123
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 186
88.2%
Space Separator 16
 
7.6%
Decimal Number 9
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 12
 
6.5%
리 11
 
5.9%
앞 10
 
5.4%
거 10
 
5.4%
네 8
 
4.3%
구 7
 
3.8%
명 5
 
2.7%
출 4
 
2.2%
성 4
 
2.2%
덕 4
 
2.2%
Other values (80) 111
59.7%
Decimal Number
ValueCountFrequency (%)
1 5
55.6%
2 3
33.3%
9 1
 
11.1%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 186
88.2%
Common 25
 
11.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 12
 
6.5%
리 11
 
5.9%
앞 10
 
5.4%
거 10
 
5.4%
네 8
 
4.3%
구 7
 
3.8%
명 5
 
2.7%
출 4
 
2.2%
성 4
 
2.2%
덕 4
 
2.2%
Other values (80) 111
59.7%
Common
ValueCountFrequency (%)
16
64.0%
1 5
 
20.0%
2 3
 
12.0%
9 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 186
88.2%
ASCII 25
 
11.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16
64.0%
1 5
 
20.0%
2 3
 
12.0%
9 1
 
4.0%
Hangul
ValueCountFrequency (%)
대 12
 
6.5%
리 11
 
5.9%
앞 10
 
5.4%
거 10
 
5.4%
네 8
 
4.3%
구 7
 
3.8%
명 5
 
2.7%
출 4
 
2.2%
성 4
 
2.2%
덕 4
 
2.2%
Other values (80) 111
59.7%

소재지도로명주소
Text

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T21:50:08.405743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length16.857143
Min length14

Characters and Unicode

Total characters472
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)85.7%

Sample

1st row대구광역시 남구 안지랑로 50
2nd row대구광역시 남구 이천로 5
3rd row대구광역시 남구 큰골길 53
4th row대구광역시 남구 현충로 1 맞은편
5th row대구광역시 남구 이천로 91 맞은편
ValueCountFrequency (%)
대구광역시 28
23.9%
남구 28
23.9%
맞은편 6
 
5.1%
현충로 4
 
3.4%
중앙대로 4
 
3.4%
대명로 4
 
3.4%
1 3
 
2.6%
앞산순환로 3
 
2.6%
이천로 3
 
2.6%
40길 2
 
1.7%
Other values (30) 32
27.4%
2023-12-12T21:50:08.747923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
19.1%
구 56
11.9%
대 40
 
8.5%
남 29
 
6.1%
광 28
 
5.9%
역 28
 
5.9%
시 28
 
5.9%
로 26
 
5.5%
5 14
 
3.0%
1 14
 
3.0%
Other values (33) 119
25.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 314
66.5%
Space Separator 90
 
19.1%
Decimal Number 68
 
14.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 56
17.8%
대 40
12.7%
남 29
9.2%
광 28
8.9%
역 28
8.9%
시 28
8.9%
로 26
8.3%
은 6
 
1.9%
맞 6
 
1.9%
명 6
 
1.9%
Other values (22) 61
19.4%
Decimal Number
ValueCountFrequency (%)
5 14
20.6%
1 14
20.6%
4 10
14.7%
3 7
10.3%
2 6
8.8%
0 5
 
7.4%
6 4
 
5.9%
9 4
 
5.9%
7 2
 
2.9%
8 2
 
2.9%
Space Separator
ValueCountFrequency (%)
90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 314
66.5%
Common 158
33.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 56
17.8%
대 40
12.7%
남 29
9.2%
광 28
8.9%
역 28
8.9%
시 28
8.9%
로 26
8.3%
은 6
 
1.9%
맞 6
 
1.9%
명 6
 
1.9%
Other values (22) 61
19.4%
Common
ValueCountFrequency (%)
90
57.0%
5 14
 
8.9%
1 14
 
8.9%
4 10
 
6.3%
3 7
 
4.4%
2 6
 
3.8%
0 5
 
3.2%
6 4
 
2.5%
9 4
 
2.5%
7 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 314
66.5%
ASCII 158
33.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
57.0%
5 14
 
8.9%
1 14
 
8.9%
4 10
 
6.3%
3 7
 
4.4%
2 6
 
3.8%
0 5
 
3.2%
6 4
 
2.5%
9 4
 
2.5%
7 2
 
1.3%
Hangul
ValueCountFrequency (%)
구 56
17.8%
대 40
12.7%
남 29
9.2%
광 28
8.9%
역 28
8.9%
시 28
8.9%
로 26
8.3%
은 6
 
1.9%
맞 6
 
1.9%
명 6
 
1.9%
Other values (22) 61
19.4%

위도
Real number (ℝ)

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.932455
Minimum35.400201
Maximum38.839205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T21:50:08.934053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.400201
5-th percentile35.83087
Q135.835527
median35.839977
Q335.845443
95-th percentile35.856206
Maximum38.839205
Range3.4390035
Interquartile range (IQR)0.0099165

Descriptive statistics

Standard deviation0.57576252
Coefficient of variation (CV)0.016023467
Kurtosis26.72626
Mean35.932455
Median Absolute Deviation (MAD)0.00504993
Skewness5.0965977
Sum1006.1088
Variance0.33150248
MonotonicityNot monotonic
2023-12-12T21:50:09.089251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
35.83251 2
 
7.1%
35.83960539 2
 
7.1%
35.8356126 1
 
3.6%
35.845144 1
 
3.6%
35.83527 1
 
3.6%
35.85008926 1
 
3.6%
35.84086841 1
 
3.6%
35.83504414 1
 
3.6%
35.84285569 1
 
3.6%
35.84034858 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
35.400201 1
3.6%
35.8308544 1
3.6%
35.8309 1
3.6%
35.83251 2
7.1%
35.83504414 1
3.6%
35.83527 1
3.6%
35.8356126 1
3.6%
35.8370581 1
3.6%
35.83862602 1
3.6%
35.8388468 1
3.6%
ValueCountFrequency (%)
38.8392045 1
3.6%
35.856735 1
3.6%
35.855223 1
3.6%
35.85008926 1
3.6%
35.84993 1
3.6%
35.849648 1
3.6%
35.8455768 1
3.6%
35.845399 1
3.6%
35.845144 1
3.6%
35.84285569 1
3.6%

경도
Real number (ℝ)

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58215
Minimum128.56195
Maximum128.60471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T21:50:09.239033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.56195
5-th percentile128.56436
Q1128.57145
median128.57976
Q3128.59239
95-th percentile128.60431
Maximum128.60471
Range0.0427565
Interquartile range (IQR)0.020933875

Descriptive statistics

Standard deviation0.013869179
Coefficient of variation (CV)0.0001078624
Kurtosis-1.2340765
Mean128.58215
Median Absolute Deviation (MAD)0.01082485
Skewness0.19092425
Sum3600.3002
Variance0.00019235412
MonotonicityNot monotonic
2023-12-12T21:50:09.362440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
128.57978 2
 
7.1%
128.5646444 2
 
7.1%
128.5715429 1
 
3.6%
128.590425 1
 
3.6%
128.5642 1
 
3.6%
128.5905918 1
 
3.6%
128.6047111 1
 
3.6%
128.6043857 1
 
3.6%
128.5619546 1
 
3.6%
128.5794716 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
128.5619546 1
3.6%
128.5642 1
3.6%
128.5646444 2
7.1%
128.5647858 1
3.6%
128.5648686 1
3.6%
128.5711902 1
3.6%
128.5715429 1
3.6%
128.5731262 1
3.6%
128.57328 1
3.6%
128.5769442 1
3.6%
ValueCountFrequency (%)
128.6047111 1
3.6%
128.6043857 1
3.6%
128.604176 1
3.6%
128.598648 1
3.6%
128.5982973 1
3.6%
128.598106 1
3.6%
128.597779 1
3.6%
128.5905918 1
3.6%
128.590576 1
3.6%
128.590425 1
3.6%

규격
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size356.0 B
4.5*0.9
15 
6*0.7
9 
5.1*0.7
2 
4.3*0.7
 
1
5.4*0.7
 
1

Length

Max length7
Median length7
Mean length6.3571429
Min length5

Unique

Unique2 ?
Unique (%)7.1%

Sample

1st row6*0.7
2nd row6*0.7
3rd row6*0.7
4th row6*0.7
5th row6*0.7

Common Values

ValueCountFrequency (%)
4.5*0.9 15
53.6%
6*0.7 9
32.1%
5.1*0.7 2
 
7.1%
4.3*0.7 1
 
3.6%
5.4*0.7 1
 
3.6%

Length

2023-12-12T21:50:09.514690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:50:09.637393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.5*0.9 15
53.6%
6*0.7 9
32.1%
5.1*0.7 2
 
7.1%
4.3*0.7 1
 
3.6%
5.4*0.7 1
 
3.6%

게시면수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size356.0 B
1
15 
5
4 
4
4 
3
4 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st row5
2nd row4
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
1 15
53.6%
5 4
 
14.3%
4 4
 
14.3%
3 4
 
14.3%
2 1
 
3.6%

Length

2023-12-12T21:50:09.775302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:50:09.897128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 15
53.6%
5 4
 
14.3%
4 4
 
14.3%
3 4
 
14.3%
2 1
 
3.6%

비고(총설치면수)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size356.0 B
행정1
15 
행정5
4 
행정4
4 
행정3
4 
행정2
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st row행정5
2nd row행정4
3rd row행정5
4th row행정5
5th row행정5

Common Values

ValueCountFrequency (%)
행정1 15
53.6%
행정5 4
 
14.3%
행정4 4
 
14.3%
행정3 4
 
14.3%
행정2 1
 
3.6%

Length

2023-12-12T21:50:10.038422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:50:10.179288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
행정1 15
53.6%
행정5 4
 
14.3%
행정4 4
 
14.3%
행정3 4
 
14.3%
행정2 1
 
3.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-11-01
28 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-01
2nd row2023-11-01
3rd row2023-11-01
4th row2023-11-01
5th row2023-11-01

Common Values

ValueCountFrequency (%)
2023-11-01 28
100.0%

Length

2023-12-12T21:50:10.293011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:50:10.747164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-11-01 28
100.0%

Interactions

2023-12-12T21:50:06.924164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:06.707722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:07.036932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:06.823571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:50:10.814853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치소재지도로명주소위도경도규격게시면수비고(총설치면수)
위치1.0001.0001.0001.0001.0000.6160.616
소재지도로명주소1.0001.0001.0001.0000.9750.4220.422
위도1.0001.0001.0000.0000.0000.0000.000
경도1.0001.0000.0001.0000.3300.0000.000
규격1.0000.9750.0000.3301.0000.9290.929
게시면수0.6160.4220.0000.0000.9291.0001.000
비고(총설치면수)0.6160.4220.0000.0000.9291.0001.000
2023-12-12T21:50:10.952286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규격비고(총설치면수)게시면수
규격1.0000.6270.627
비고(총설치면수)0.6271.0001.000
게시면수0.6271.0001.000
2023-12-12T21:50:11.061245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도규격게시면수비고(총설치면수)
위도1.0000.3400.0000.0000.000
경도0.3401.0000.2220.0000.000
규격0.0000.2221.0000.6270.627
게시면수0.0000.0000.6271.0001.000
비고(총설치면수)0.0000.0000.6271.0001.000

Missing values

2023-12-12T21:50:07.180810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:50:07.343059image/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.

Sample

관리기관명위치소재지도로명주소위도경도규격게시면수비고(총설치면수)데이터기준일자
0대구광역시 남구청대덕성당 앞대구광역시 남구 안지랑로 5035.835613128.5715436*0.75행정52023-11-01
1대구광역시 남구청캠프워크 헬기장대구광역시 남구 이천로 535.840887128.5977796*0.74행정42023-11-01
2대구광역시 남구청대명9동행정복지센터대구광역시 남구 큰골길 5335.837058128.579396*0.75행정52023-11-01
3대구광역시 남구청현충삼거리대구광역시 남구 현충로 1 맞은편35.83251128.579786*0.75행정52023-11-01
4대구광역시 남구청캠프헨리대구광역시 남구 이천로 91 맞은편35.84993128.5986486*0.75행정52023-11-01
5대구광역시 남구청대구여상대구광역시 남구 현충로 40길 5035.849648128.5827786*0.73행정32023-11-01
6대구광역시 남구청남구청입구대구광역시 남구 이천로 5135.845577128.5982974.3*0.74행정42023-11-01
7대구광역시 남구청금호렌트카 앞대구광역시 남구 대덕로 40길 135.841001128.6041764.5*0.91행정12023-11-01
8대구광역시 남구청앞산맛둘레길 제1주차장 입구대구광역시 남구 앞산순환로 435 맞은편35.8309128.573284.5*0.91행정12023-11-01
9대구광역시 남구청대명역2번 출구대구광역시 남구 대명로 6635.839199128.5647864.5*0.91행정12023-11-01
관리기관명위치소재지도로명주소위도경도규격게시면수비고(총설치면수)데이터기준일자
18대구광역시 남구청대덕성당 건너편2대구광역시 남구 앞산순환로 443 맞은편35.839605128.5646446*0.73행정32023-11-01
19대구광역시 남구청대명역1번출구대구광역시 남구 대명로 6535.839605128.5646444.5*0.91행정12023-11-01
20대구광역시 남구청국민보험공단남부지사대구광역시 남구 대명로 12435.838626128.571194.5*0.91행정12023-11-01
21대구광역시 남구청앞산네거리조헌포이비인후과대구광역시 남구 대명로20535.840349128.5794724.5*0.91행정12023-11-01
22대구광역시 남구청흥아아파트 앞대구광역시 남구 성당로 6435.842856128.5619554.5*0.91행정12023-11-01
23대구광역시 남구청현충삼거리대구광역시 남구 현충로 1 맞은편35.83251128.579786*0.74행정42023-11-01
24대구광역시 남구청신천대로 삼성레미안 앞대구광역시 남구 효성중앙길 8235.835044128.6043865.1*0.72행정22023-11-01
25대구광역시 남구청중동네거리대구광역시 남구 대봉로 1235.840868128.6047115.4*0.73행정32023-11-01
26대구광역시 남구청교대역1번출구 앞대구광역시 남구 중앙대로 19435.850089128.5905925.1*0.74행정42023-11-01
27대구광역시 남구청남대명파출소 앞대구광역시 남구 대명남로 5535.83527128.56424.5*0.91행정12023-11-01