Overview

Dataset statistics

Number of variables8
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory73.3 B

Variable types

Numeric4
Text2
Categorical1
DateTime1

Dataset

Description광주광역시 북구 관내 소방통로확보 필요대상 지역 현황에 대한 데이터로 광주광역시 북부소방서에서 관리 중인 출동통로 확포 필요지역 현황(노선명, 위치, 구간거리, 폭, 세대수 등)을 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15045616/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 비고High correlation
비고 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-23 04:19:38.887550
Analysis finished2024-03-23 04:19:46.434838
Duration7.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-03-23T04:19:46.672637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly increasing
2024-03-23T04:19:47.223712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
24 1
 
4.0%
23 1
 
4.0%
22 1
 
4.0%
21 1
 
4.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

소방차진입취약지역(노선명)
Text

Distinct23
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-03-23T04:19:47.773649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length11.16
Min length4

Characters and Unicode

Total characters279
Distinct characters92
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

Unique21 ?
Unique (%)84.0%

Sample

1st row두암시장 주변
2nd row서방시장 주변
3rd row말바우시장
4th row각화동 농산물 도매시장 주변
5th row대원시장 주변
ValueCountFrequency (%)
주변 16
24.2%
전대후문 2
 
3.0%
문흥동 2
 
3.0%
우체국 2
 
3.0%
2
 
3.0%
용흥1길 2
 
3.0%
전남대학교 2
 
3.0%
상대쪽문길 2
 
3.0%
화정맨션 1
 
1.5%
신안동 1
 
1.5%
Other values (34) 34
51.5%
2024-03-23T04:19:48.988557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
 
16.8%
주 19
 
6.8%
변 17
 
6.1%
대 10
 
3.6%
흥 9
 
3.2%
동 9
 
3.2%
장 9
 
3.2%
시 8
 
2.9%
문 8
 
2.9%
길 6
 
2.2%
Other values (82) 137
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 220
78.9%
Space Separator 47
 
16.8%
Decimal Number 5
 
1.8%
Math Symbol 3
 
1.1%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 19
 
8.6%
변 17
 
7.7%
대 10
 
4.5%
흥 9
 
4.1%
동 9
 
4.1%
장 9
 
4.1%
시 8
 
3.6%
문 8
 
3.6%
길 6
 
2.7%
우 5
 
2.3%
Other values (76) 120
54.5%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
2 2
40.0%
Space Separator
ValueCountFrequency (%)
47
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 220
78.9%
Common 59
 
21.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 19
 
8.6%
변 17
 
7.7%
대 10
 
4.5%
흥 9
 
4.1%
동 9
 
4.1%
장 9
 
4.1%
시 8
 
3.6%
문 8
 
3.6%
길 6
 
2.7%
우 5
 
2.3%
Other values (76) 120
54.5%
Common
ValueCountFrequency (%)
47
79.7%
~ 3
 
5.1%
1 3
 
5.1%
2 2
 
3.4%
( 2
 
3.4%
) 2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 220
78.9%
ASCII 59
 
21.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
79.7%
~ 3
 
5.1%
1 3
 
5.1%
2 2
 
3.4%
( 2
 
3.4%
) 2
 
3.4%
Hangul
ValueCountFrequency (%)
주 19
 
8.6%
변 17
 
7.7%
대 10
 
4.5%
흥 9
 
4.1%
동 9
 
4.1%
장 9
 
4.1%
시 8
 
3.6%
문 8
 
3.6%
길 6
 
2.7%
우 5
 
2.3%
Other values (76) 120
54.5%

위치(도로명)
Text

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-03-23T04:19:49.683147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length20.04
Min length9

Characters and Unicode

Total characters501
Distinct characters94
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)92.0%

Sample

1st row두암시장 주변 북구 금녕길 23
2nd row서방시장 주변 북구 동문대로14-14
3rd row북구 동문대로 73, 81, 85, 97번길(말바우시장)
4th row북구 동문대로 260
5th row북구 임동 190-1 (서림초등뒤1길)
ValueCountFrequency (%)
북구 19
 
18.8%
주변 7
 
6.9%
용봉동 4
 
4.0%
전대후문 2
 
2.0%
문흥동 2
 
2.0%
주변북구 2
 
2.0%
동문대로 2
 
2.0%
6(용흥1길 2
 
2.0%
158-11 2
 
2.0%
무등경기장 1
 
1.0%
Other values (58) 58
57.4%
2024-03-23T04:19:50.856737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
15.4%
1 29
 
5.8%
길 23
 
4.6%
북 21
 
4.2%
구 21
 
4.2%
동 17
 
3.4%
로 16
 
3.2%
문 13
 
2.6%
번 12
 
2.4%
주 11
 
2.2%
Other values (84) 261
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 279
55.7%
Decimal Number 103
 
20.6%
Space Separator 77
 
15.4%
Close Punctuation 10
 
2.0%
Other Punctuation 10
 
2.0%
Open Punctuation 10
 
2.0%
Dash Punctuation 6
 
1.2%
Math Symbol 6
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 23
 
8.2%
북 21
 
7.5%
구 21
 
7.5%
동 17
 
6.1%
로 16
 
5.7%
문 13
 
4.7%
번 12
 
4.3%
주 11
 
3.9%
대 11
 
3.9%
변 10
 
3.6%
Other values (67) 124
44.4%
Decimal Number
ValueCountFrequency (%)
1 29
28.2%
9 10
 
9.7%
5 10
 
9.7%
8 10
 
9.7%
3 9
 
8.7%
2 8
 
7.8%
0 8
 
7.8%
6 7
 
6.8%
4 7
 
6.8%
7 5
 
4.9%
Math Symbol
ValueCountFrequency (%)
~ 5
83.3%
～ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 279
55.7%
Common 222
44.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 23
 
8.2%
북 21
 
7.5%
구 21
 
7.5%
동 17
 
6.1%
로 16
 
5.7%
문 13
 
4.7%
번 12
 
4.3%
주 11
 
3.9%
대 11
 
3.9%
변 10
 
3.6%
Other values (67) 124
44.4%
Common
ValueCountFrequency (%)
77
34.7%
1 29
 
13.1%
9 10
 
4.5%
) 10
 
4.5%
5 10
 
4.5%
8 10
 
4.5%
, 10
 
4.5%
( 10
 
4.5%
3 9
 
4.1%
2 8
 
3.6%
Other values (7) 39
17.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 279
55.7%
ASCII 221
44.1%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
34.8%
1 29
 
13.1%
9 10
 
4.5%
) 10
 
4.5%
5 10
 
4.5%
8 10
 
4.5%
, 10
 
4.5%
( 10
 
4.5%
3 9
 
4.1%
2 8
 
3.6%
Other values (6) 38
17.2%
Hangul
ValueCountFrequency (%)
길 23
 
8.2%
북 21
 
7.5%
구 21
 
7.5%
동 17
 
6.1%
로 16
 
5.7%
문 13
 
4.7%
번 12
 
4.3%
주 11
 
3.9%
대 11
 
3.9%
변 10
 
3.6%
Other values (67) 124
44.4%
None
ValueCountFrequency (%)
～ 1
100.0%

구간거리(미터)
Real number (ℝ)

Distinct19
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.64
Minimum80
Maximum875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-03-23T04:19:51.425136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile100
Q1200
median250
Q3300
95-th percentile392.2
Maximum875
Range795
Interquartile range (IQR)100

Descriptive statistics

Standard deviation152.89045
Coefficient of variation (CV)0.59342668
Kurtosis11.149506
Mean257.64
Median Absolute Deviation (MAD)50
Skewness2.8091522
Sum6441
Variance23375.49
MonotonicityNot monotonic
2024-03-23T04:19:51.974527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
250 4
16.0%
200 3
 
12.0%
100 2
 
8.0%
260 1
 
4.0%
170 1
 
4.0%
80 1
 
4.0%
210 1
 
4.0%
875 1
 
4.0%
361 1
 
4.0%
340 1
 
4.0%
Other values (9) 9
36.0%
ValueCountFrequency (%)
80 1
 
4.0%
100 2
8.0%
120 1
 
4.0%
150 1
 
4.0%
170 1
 
4.0%
200 3
12.0%
210 1
 
4.0%
230 1
 
4.0%
240 1
 
4.0%
250 4
16.0%
ValueCountFrequency (%)
875 1
 
4.0%
400 1
 
4.0%
361 1
 
4.0%
340 1
 
4.0%
320 1
 
4.0%
315 1
 
4.0%
300 1
 
4.0%
270 1
 
4.0%
260 1
 
4.0%
250 4
16.0%

폭(미터)
Real number (ℝ)

Distinct7
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-03-23T04:19:52.409707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median6
Q36
95-th percentile7.8
Maximum10
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5275252
Coefficient of variation (CV)0.27277236
Kurtosis1.5523917
Mean5.6
Median Absolute Deviation (MAD)1
Skewness0.8233998
Sum140
Variance2.3333333
MonotonicityNot monotonic
2024-03-23T04:19:52.776110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 9
36.0%
4 6
24.0%
5 4
16.0%
7 3
 
12.0%
10 1
 
4.0%
3 1
 
4.0%
8 1
 
4.0%
ValueCountFrequency (%)
3 1
 
4.0%
4 6
24.0%
5 4
16.0%
6 9
36.0%
7 3
 
12.0%
8 1
 
4.0%
10 1
 
4.0%
ValueCountFrequency (%)
10 1
 
4.0%
8 1
 
4.0%
7 3
 
12.0%
6 9
36.0%
5 4
16.0%
4 6
24.0%
3 1
 
4.0%

세대수
Real number (ℝ)

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.76
Minimum17
Maximum1226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-03-23T04:19:53.235333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile48.4
Q1100
median180
Q3300
95-th percentile956.4
Maximum1226
Range1209
Interquartile range (IQR)200

Descriptive statistics

Standard deviation304.74324
Coefficient of variation (CV)1.1423873
Kurtosis4.2764643
Mean266.76
Median Absolute Deviation (MAD)80
Skewness2.1940201
Sum6669
Variance92868.44
MonotonicityNot monotonic
2024-03-23T04:19:53.710034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100 6
24.0%
90 3
12.0%
300 2
 
8.0%
195 2
 
8.0%
200 2
 
8.0%
180 1
 
4.0%
984 1
 
4.0%
365 1
 
4.0%
17 1
 
4.0%
38 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
17 1
 
4.0%
38 1
 
4.0%
90 3
12.0%
100 6
24.0%
103 1
 
4.0%
180 1
 
4.0%
195 2
 
8.0%
200 2
 
8.0%
250 1
 
4.0%
300 2
 
8.0%
ValueCountFrequency (%)
1226 1
4.0%
984 1
4.0%
846 1
4.0%
400 1
4.0%
365 1
4.0%
300 2
8.0%
250 1
4.0%
200 2
8.0%
195 2
8.0%
180 1
4.0%

비고
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
상가주변
13 
전통시장
6 
주거밀집
4 
유사시장
2 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상가주변 13
52.0%
전통시장 6
24.0%
주거밀집 4
 
16.0%
유사시장 2
 
8.0%

Length

2024-03-23T04:19:54.075331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T04:19:54.530907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상가주변 13
52.0%
전통시장 6
24.0%
주거밀집 4
 
16.0%
유사시장 2
 
8.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum2024-03-14 00:00:00
Maximum2024-03-14 00:00:00
2024-03-23T04:19:54.951222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:55.363006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-23T04:19:43.523805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:39.766417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:41.014128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:42.200862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:43.863987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:40.049645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:41.265476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:42.547274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:44.248152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:40.320879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:41.572310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:42.896200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:44.767869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:40.697043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:41.818514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T04:19:43.273285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T04:19:55.637473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소방차진입취약지역(노선명)위치(도로명)구간거리(미터)폭(미터)세대수비고
연번1.0001.0001.0000.5960.2940.0000.924
소방차진입취약지역(노선명)1.0001.0001.0000.9720.9681.0001.000
위치(도로명)1.0001.0001.0000.9790.9471.0001.000
구간거리(미터)0.5960.9720.9791.0000.7350.3660.000
폭(미터)0.2940.9680.9470.7351.0000.7310.000
세대수0.0001.0001.0000.3660.7311.0000.000
비고0.9241.0001.0000.0000.0000.0001.000
2024-03-23T04:19:55.985488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구간거리(미터)폭(미터)세대수비고
연번1.0000.0120.0790.0730.695
구간거리(미터)0.0121.0000.0550.2960.000
폭(미터)0.0790.0551.000-0.3200.000
세대수0.0730.296-0.3201.0000.000
비고0.6950.0000.0000.0001.000

Missing values

2024-03-23T04:19:45.244507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T04:19:46.187510image/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

연번소방차진입취약지역(노선명)위치(도로명)구간거리(미터)폭(미터)세대수비고데이터기준일자
01두암시장 주변두암시장 주변 북구 금녕길 23260690전통시장2024-03-14
12서방시장 주변서방시장 주변 북구 동문대로14-142705180전통시장2024-03-14
23말바우시장북구 동문대로 73, 81, 85, 97번길(말바우시장)3154984전통시장2024-03-14
34각화동 농산물 도매시장 주변북구 동문대로 2603204365전통시장2024-03-14
45대원시장 주변북구 임동 190-1 (서림초등뒤1길)150717전통시장2024-03-14
56중흥시장 주변북구 중흥1동 684 (중흥시장1길)230638전통시장2024-03-14
67동부시장북구 중문로9번길, 서방로9, 19번길(동부시장)2007250유사시장2024-03-14
78운암시장 주변북구 운암2동 94-72506103유사시장2024-03-14
89롯데슈퍼 주변북구 삼정로3번길 212005100상가주변2024-03-14
910우산초등학교 주변북구 서방로 63번길,동문로 11번길(우산초등학교 주변)30071226상가주변2024-03-14
연번소방차진입취약지역(노선명)위치(도로명)구간거리(미터)폭(미터)세대수비고데이터기준일자
1516전대후문 주변 (용흥1길)전대후문 주변북구 용봉동 158-11, 6(용흥1길)2406195상가주변2024-03-14
1617전대후문 주변 (용흥1길)전대후문 주변북구 용봉동 158-11, 6(용흥1길)1705195상가주변2024-03-14
1718문흥동 일신아파트~문흥2동주민자치센터문흥동 일신아파트~문흥2동주민자치센터2506300상가주변2024-03-14
1819문흥동 우체국 ~ 국민은행 문흥지점문흥동 우체국 ~ 국민은행 문흥지점2506846상가주변2024-03-14
1920두경사우나 건물 주변북구 설죽로 518 주변4001090상가주변2024-03-14
2021양산동 우체국 뒤 블록북구 연향로 40번길3406200상가주변2024-03-14
2122현대2차아파트 주변북구 동문대로100번길 163616100주거밀집2024-03-14
2223교대 뒷길교대 뒷길 북구 두방길 14~1008753400주거밀집2024-03-14
2324대흥맨션 ~ 중흥1동주민센터중흥로 173번길210890주거밀집2024-03-14
2425신안동 화정맨션 앞길북구 서양로 159번길805200주거밀집2024-03-14