Overview

Dataset statistics

Number of variables7
Number of observations31
Missing cells12
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory66.1 B

Variable types

Numeric6
Text1

Dataset

Description광주광역시 전체 특정소방대상물 현황입니다. 31가지로 구분되는 업종별, 5개 관할소방서별 특정소방대상물 개소수를 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15055247/fileData.do

Alerts

동부소방서 is highly overall correlated with 서부소방서 and 3 other fieldsHigh correlation
서부소방서 is highly overall correlated with 동부소방서 and 3 other fieldsHigh correlation
남부소방서 is highly overall correlated with 동부소방서 and 3 other fieldsHigh correlation
북부소방서 is highly overall correlated with 동부소방서 and 3 other fieldsHigh correlation
광산소방서 is highly overall correlated with 동부소방서 and 3 other fieldsHigh correlation
동부소방서 has 4 (12.9%) missing valuesMissing
서부소방서 has 2 (6.5%) missing valuesMissing
남부소방서 has 4 (12.9%) missing valuesMissing
북부소방서 has 1 (3.2%) missing valuesMissing
광산소방서 has 1 (3.2%) missing valuesMissing
연번 has unique valuesUnique
업종별 has unique valuesUnique

Reproduction

Analysis started2024-03-14 12:59:18.261206
Analysis finished2024-03-14 12:59:28.047043
Duration9.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T21:59:28.244950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q18.5
median16
Q323.5
95-th percentile29.5
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)0.56825757
Kurtosis-1.2
Mean16
Median Absolute Deviation (MAD)8
Skewness0
Sum496
Variance82.666667
MonotonicityStrictly increasing
2024-03-14T21:59:28.645062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1
 
3.2%
2 1
 
3.2%
31 1
 
3.2%
30 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1 1
3.2%
2 1
3.2%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 1
3.2%
ValueCountFrequency (%)
31 1
3.2%
30 1
3.2%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%

업종별
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size376.0 B
2024-03-14T21:59:29.327027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.6774194
Min length2

Characters and Unicode

Total characters176
Distinct characters84
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

Unique31 ?
Unique (%)100.0%

Sample

1st row공동주택(아파트)
2nd row공동주택(기숙사)
3rd row근린생활
4th row문화 및 집회시설
5th row종교시설
ValueCountFrequency (%)
및 6
 
13.6%
공동주택(아파트 1
 
2.3%
군사 1
 
2.3%
자동차 1
 
2.3%
동물 1
 
2.3%
식물 1
 
2.3%
관련 1
 
2.3%
자원순환(분뇨 1
 
2.3%
쓰레기 1
 
2.3%
교정 1
 
2.3%
Other values (29) 29
65.9%
2024-03-14T21:59:30.643309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
설 18
 
10.2%
시 18
 
10.2%
13
 
7.4%
및 6
 
3.4%
동 5
 
2.8%
물 4
 
2.3%
공 4
 
2.3%
장 4
 
2.3%
) 3
 
1.7%
기 3
 
1.7%
Other values (74) 98
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
89.2%
Space Separator 13
 
7.4%
Close Punctuation 3
 
1.7%
Open Punctuation 3
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 18
 
11.5%
시 18
 
11.5%
및 6
 
3.8%
동 5
 
3.2%
물 4
 
2.5%
공 4
 
2.5%
장 4
 
2.5%
기 3
 
1.9%
관 3
 
1.9%
련 3
 
1.9%
Other values (71) 89
56.7%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 157
89.2%
Common 19
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 18
 
11.5%
시 18
 
11.5%
및 6
 
3.8%
동 5
 
3.2%
물 4
 
2.5%
공 4
 
2.5%
장 4
 
2.5%
기 3
 
1.9%
관 3
 
1.9%
련 3
 
1.9%
Other values (71) 89
56.7%
Common
ValueCountFrequency (%)
13
68.4%
) 3
 
15.8%
( 3
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157
89.2%
ASCII 19
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
설 18
 
11.5%
시 18
 
11.5%
및 6
 
3.8%
동 5
 
3.2%
물 4
 
2.5%
공 4
 
2.5%
장 4
 
2.5%
기 3
 
1.9%
관 3
 
1.9%
련 3
 
1.9%
Other values (71) 89
56.7%
ASCII
ValueCountFrequency (%)
13
68.4%
) 3
 
15.8%
( 3
 
15.8%

동부소방서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)77.8%
Missing4
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean217.07407
Minimum1
Maximum4651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T21:59:31.014156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q15
median12
Q357
95-th percentile282.4
Maximum4651
Range4650
Interquartile range (IQR)52

Descriptive statistics

Standard deviation889.18329
Coefficient of variation (CV)4.0962206
Kurtosis26.58584
Mean217.07407
Median Absolute Deviation (MAD)9
Skewness5.1400136
Sum5861
Variance790646.92
MonotonicityNot monotonic
2024-03-14T21:59:31.373476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 3
 
9.7%
11 2
 
6.5%
21 2
 
6.5%
5 2
 
6.5%
6 2
 
6.5%
4651 1
 
3.2%
2 1
 
3.2%
328 1
 
3.2%
3 1
 
3.2%
10 1
 
3.2%
Other values (11) 11
35.5%
(Missing) 4
 
12.9%
ValueCountFrequency (%)
1 1
 
3.2%
2 1
 
3.2%
3 1
 
3.2%
4 3
9.7%
5 2
6.5%
6 2
6.5%
10 1
 
3.2%
11 2
6.5%
12 1
 
3.2%
20 1
 
3.2%
ValueCountFrequency (%)
4651 1
3.2%
328 1
3.2%
176 1
3.2%
149 1
3.2%
123 1
3.2%
98 1
3.2%
59 1
3.2%
55 1
3.2%
53 1
3.2%
23 1
3.2%

서부소방서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)86.2%
Missing2
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean219.10345
Minimum1
Maximum4116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T21:59:31.730950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median31
Q3100
95-th percentile583.2
Maximum4116
Range4115
Interquartile range (IQR)95

Descriptive statistics

Standard deviation765.85318
Coefficient of variation (CV)3.4953954
Kurtosis26.34239
Mean219.10345
Median Absolute Deviation (MAD)28
Skewness5.0588664
Sum6354
Variance586531.1
MonotonicityNot monotonic
2024-03-14T21:59:32.103188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5 3
 
9.7%
1 3
 
9.7%
834 1
 
3.2%
3 1
 
3.2%
2 1
 
3.2%
13 1
 
3.2%
22 1
 
3.2%
9 1
 
3.2%
91 1
 
3.2%
50 1
 
3.2%
Other values (15) 15
48.4%
(Missing) 2
 
6.5%
ValueCountFrequency (%)
1 3
9.7%
2 1
 
3.2%
3 1
 
3.2%
5 3
9.7%
9 1
 
3.2%
10 1
 
3.2%
13 1
 
3.2%
17 1
 
3.2%
21 1
 
3.2%
22 1
 
3.2%
ValueCountFrequency (%)
4116 1
3.2%
834 1
3.2%
207 1
3.2%
200 1
3.2%
166 1
3.2%
146 1
3.2%
117 1
3.2%
100 1
3.2%
91 1
3.2%
73 1
3.2%

남부소방서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)85.2%
Missing4
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean217
Minimum1
Maximum4009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T21:59:32.455089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16.5
median18
Q381.5
95-th percentile614.2
Maximum4009
Range4008
Interquartile range (IQR)75

Descriptive statistics

Standard deviation773.16259
Coefficient of variation (CV)3.5629612
Kurtosis24.679423
Mean217
Median Absolute Deviation (MAD)17
Skewness4.9022863
Sum5859
Variance597780.38
MonotonicityNot monotonic
2024-03-14T21:59:32.854491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 3
 
9.7%
2 2
 
6.5%
6 2
 
6.5%
21 1
 
3.2%
787 1
 
3.2%
12 1
 
3.2%
7 1
 
3.2%
15 1
 
3.2%
9 1
 
3.2%
85 1
 
3.2%
Other values (13) 13
41.9%
(Missing) 4
 
12.9%
ValueCountFrequency (%)
1 3
9.7%
2 2
6.5%
6 2
6.5%
7 1
 
3.2%
8 1
 
3.2%
9 1
 
3.2%
10 1
 
3.2%
12 1
 
3.2%
15 1
 
3.2%
18 1
 
3.2%
ValueCountFrequency (%)
4009 1
3.2%
787 1
3.2%
211 1
3.2%
186 1
3.2%
98 1
3.2%
92 1
3.2%
85 1
3.2%
78 1
3.2%
67 1
3.2%
58 1
3.2%

북부소방서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)90.0%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean390.23333
Minimum1
Maximum8187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T21:59:33.239788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q16.75
median23.5
Q3196
95-th percentile584.25
Maximum8187
Range8186
Interquartile range (IQR)189.25

Descriptive statistics

Standard deviation1482.4941
Coefficient of variation (CV)3.798994
Kurtosis29.11871
Mean390.23333
Median Absolute Deviation (MAD)21
Skewness5.3632242
Sum11707
Variance2197788.7
MonotonicityNot monotonic
2024-03-14T21:59:33.611092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 3
 
9.7%
22 2
 
6.5%
361 1
 
3.2%
411 1
 
3.2%
726 1
 
3.2%
4 1
 
3.2%
12 1
 
3.2%
1 1
 
3.2%
2 1
 
3.2%
9 1
 
3.2%
Other values (17) 17
54.8%
ValueCountFrequency (%)
1 1
 
3.2%
2 1
 
3.2%
3 3
9.7%
4 1
 
3.2%
5 1
 
3.2%
6 1
 
3.2%
9 1
 
3.2%
12 1
 
3.2%
15 1
 
3.2%
17 1
 
3.2%
ValueCountFrequency (%)
8187 1
3.2%
726 1
3.2%
411 1
3.2%
376 1
3.2%
361 1
3.2%
311 1
3.2%
273 1
3.2%
200 1
3.2%
184 1
3.2%
151 1
3.2%

광산소방서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)86.7%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean304.46667
Minimum1
Maximum4112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T21:59:33.970929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q18.75
median21
Q3144.75
95-th percentile1589.7
Maximum4112
Range4111
Interquartile range (IQR)136

Descriptive statistics

Standard deviation820.70893
Coefficient of variation (CV)2.6955625
Kurtosis16.83548
Mean304.46667
Median Absolute Deviation (MAD)19.5
Skewness3.9404788
Sum9134
Variance673563.15
MonotonicityNot monotonic
2024-03-14T21:59:34.280411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
14 2
 
6.5%
12 2
 
6.5%
1 2
 
6.5%
8 2
 
6.5%
266 1
 
3.2%
1596 1
 
3.2%
1582 1
 
3.2%
2 1
 
3.2%
4 1
 
3.2%
77 1
 
3.2%
Other values (16) 16
51.6%
ValueCountFrequency (%)
1 2
6.5%
2 1
3.2%
3 1
3.2%
4 1
3.2%
6 1
3.2%
8 2
6.5%
11 1
3.2%
12 2
6.5%
14 2
6.5%
15 1
3.2%
ValueCountFrequency (%)
4112 1
3.2%
1596 1
3.2%
1582 1
3.2%
387 1
3.2%
266 1
3.2%
170 1
3.2%
152 1
3.2%
150 1
3.2%
129 1
3.2%
116 1
3.2%

Interactions

2024-03-14T21:59:25.673635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:18.514595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:19.964558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:21.378398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:22.782797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:24.274726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:25.915236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:18.761319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:20.209950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:21.620281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:23.038267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:24.513372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:26.140153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:18.992974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:20.439953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:21.848417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:23.280194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:24.738612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:26.368264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:19.233288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:20.666045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:22.071987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:23.522057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:24.966106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:26.620336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:19.493615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:20.921477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:22.324881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:23.790477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:25.222386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:26.846075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:19.731216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:21.148613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:22.550009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:24.033211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:59:25.447896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:59:34.553200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종별동부소방서서부소방서남부소방서북부소방서광산소방서
연번1.0001.0000.0000.0000.0000.1590.000
업종별1.0001.0001.0001.0001.0001.0001.000
동부소방서0.0001.0001.0001.0001.0000.6481.000
서부소방서0.0001.0001.0001.0001.0001.0000.985
남부소방서0.0001.0001.0001.0001.0001.0000.985
북부소방서0.1591.0000.6481.0001.0001.0001.000
광산소방서0.0001.0001.0000.9850.9851.0001.000
2024-03-14T21:59:34.841851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동부소방서서부소방서남부소방서북부소방서광산소방서
연번1.000-0.342-0.437-0.303-0.379-0.371
동부소방서-0.3421.0000.8920.8510.7400.694
서부소방서-0.4370.8921.0000.8750.8850.894
남부소방서-0.3030.8510.8751.0000.8930.856
북부소방서-0.3790.7400.8850.8931.0000.940
광산소방서-0.3710.6940.8940.8560.9401.000

Missing values

2024-03-14T21:59:27.161752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:59:27.547503image/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.
2024-03-14T21:59:27.871194image/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

연번업종별동부소방서서부소방서남부소방서북부소방서광산소방서
01공동주택(아파트)98200211361266
12공동주택(기숙사)65182211
23근린생활46514116400981874112
34문화 및 집회시설2039101714
45종교시설5510098184105
56판매시설123382418
67운수시설11101314
78의료시설2131336138
89교육연구시설537378137116
910노유자시설123207186376387
연번업종별동부소방서서부소방서남부소방서북부소방서광산소방서
2122교정 및 군사<NA>1<NA>228
2223방송통신시설513698
2324발전시설<NA>1<NA><NA>1
2425묘지관련시설<NA><NA><NA>3<NA>
2526관광휴게시설<NA><NA>221
2627장례식장32134
2728지하가51712
2829지하구4521212
2930문화재4312412
3031복합건축물3288347877261582