Overview

Dataset statistics

Number of variables6
Number of observations31
Missing cells27
Missing cells (%)14.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory57.3 B

Variable types

Numeric5
Text1

Dataset

Description광주광역시 동부소방서 관내 특별조사대상 현황에 대한 데이터로동부소방서 관할 소방특별조사대상을 119안전센터별로 구분한 자료를 설명합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15055239/fileData.do

Alerts

계 is highly overall correlated with 대인119안전센터 and 2 other fieldsHigh correlation
대인119안전센터 is highly overall correlated with 계 and 2 other fieldsHigh correlation
용산119안전센터 is highly overall correlated with 계 and 2 other fieldsHigh correlation
지산119안전센터 is highly overall correlated with 계 and 2 other fieldsHigh correlation
계 has 4 (12.9%) missing valuesMissing
대인119안전센터 has 8 (25.8%) missing valuesMissing
용산119안전센터 has 4 (12.9%) missing valuesMissing
지산119안전센터 has 11 (35.5%) missing valuesMissing
연번 has unique valuesUnique
업종 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:12:13.372345
Analysis finished2023-12-12 12:12:16.485306
Duration3.11 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 size411.0 B
2023-12-12T21:12:16.574835image/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
2023-12-12T21:12:16.751494image/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%

업종
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-12T21:12:16.987599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.4193548
Min length3

Characters and Unicode

Total characters168
Distinct characters80
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
 
13.3%
공동주택(아파트 1
 
2.2%
군사 1
 
2.2%
자동차 1
 
2.2%
동물 1
 
2.2%
식물 1
 
2.2%
관련 1
 
2.2%
자원 1
 
2.2%
순환 1
 
2.2%
교정 1
 
2.2%
Other values (30) 30
66.7%
2023-12-12T21:12:17.443243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
설 17
 
10.1%
시 17
 
10.1%
14
 
8.3%
및 6
 
3.6%
동 5
 
3.0%
공 4
 
2.4%
물 4
 
2.4%
장 4
 
2.4%
관 3
 
1.8%
련 3
 
1.8%
Other values (70) 91
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150
89.3%
Space Separator 14
 
8.3%
Open Punctuation 2
 
1.2%
Close Punctuation 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 17
 
11.3%
시 17
 
11.3%
및 6
 
4.0%
동 5
 
3.3%
공 4
 
2.7%
물 4
 
2.7%
장 4
 
2.7%
관 3
 
2.0%
련 3
 
2.0%
자 3
 
2.0%
Other values (67) 84
56.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150
89.3%
Common 18
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 17
 
11.3%
시 17
 
11.3%
및 6
 
4.0%
동 5
 
3.3%
공 4
 
2.7%
물 4
 
2.7%
장 4
 
2.7%
관 3
 
2.0%
련 3
 
2.0%
자 3
 
2.0%
Other values (67) 84
56.0%
Common
ValueCountFrequency (%)
14
77.8%
( 2
 
11.1%
) 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150
89.3%
ASCII 18
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
설 17
 
11.3%
시 17
 
11.3%
및 6
 
4.0%
동 5
 
3.3%
공 4
 
2.7%
물 4
 
2.7%
장 4
 
2.7%
관 3
 
2.0%
련 3
 
2.0%
자 3
 
2.0%
Other values (67) 84
56.0%
ASCII
ValueCountFrequency (%)
14
77.8%
( 2
 
11.1%
) 2
 
11.1%

계
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 size411.0 B
2023-12-12T21:12:17.616069image/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
2023-12-12T21:12:17.778260image/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%

대인119안전센터
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)60.9%
Missing8
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean135.34783
Minimum1
Maximum2540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:12:17.924313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q328
95-th percentile183.3
Maximum2540
Range2539
Interquartile range (IQR)26

Descriptive statistics

Standard deviation526.14271
Coefficient of variation (CV)3.8873377
Kurtosis22.611953
Mean135.34783
Median Absolute Deviation (MAD)7
Skewness4.7395071
Sum3113
Variance276826.15
MonotonicityNot monotonic
2023-12-12T21:12:18.036402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
9 4
12.9%
1 4
12.9%
2 3
 
9.7%
6 2
 
6.5%
13 1
 
3.2%
2540 1
 
3.2%
18 1
 
3.2%
15 1
 
3.2%
38 1
 
3.2%
88 1
 
3.2%
Other values (4) 4
12.9%
(Missing) 8
25.8%
ValueCountFrequency (%)
1 4
12.9%
2 3
9.7%
5 1
 
3.2%
6 2
6.5%
9 4
12.9%
13 1
 
3.2%
15 1
 
3.2%
18 1
 
3.2%
38 1
 
3.2%
41 1
 
3.2%
ValueCountFrequency (%)
2540 1
 
3.2%
192 1
 
3.2%
105 1
 
3.2%
88 1
 
3.2%
41 1
 
3.2%
38 1
 
3.2%
18 1
 
3.2%
15 1
 
3.2%
13 1
 
3.2%
9 4
12.9%

용산119안전센터
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)59.3%
Missing4
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean50.444444
Minimum1
Maximum982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:12:18.170283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q319.5
95-th percentile61.8
Maximum982
Range981
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation187.11562
Coefficient of variation (CV)3.7093405
Kurtosis26.394359
Mean50.444444
Median Absolute Deviation (MAD)5
Skewness5.1135437
Sum1362
Variance35012.256
MonotonicityNot monotonic
2023-12-12T21:12:18.300939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 5
16.1%
5 3
9.7%
1 3
9.7%
12 2
 
6.5%
3 2
 
6.5%
8 2
 
6.5%
21 1
 
3.2%
56 1
 
3.2%
6 1
 
3.2%
44 1
 
3.2%
Other values (6) 6
19.4%
(Missing) 4
12.9%
ValueCountFrequency (%)
1 3
9.7%
2 5
16.1%
3 2
 
6.5%
5 3
9.7%
6 1
 
3.2%
8 2
 
6.5%
12 2
 
6.5%
13 1
 
3.2%
18 1
 
3.2%
21 1
 
3.2%
ValueCountFrequency (%)
982 1
3.2%
63 1
3.2%
59 1
3.2%
56 1
3.2%
44 1
3.2%
26 1
3.2%
21 1
3.2%
18 1
3.2%
13 1
3.2%
12 2
6.5%

지산119안전센터
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)65.0%
Missing11
Missing (%)35.5%
Infinite0
Infinite (%)0.0%
Mean69.3
Minimum1
Maximum1129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:12:18.739761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q323
95-th percentile132.45
Maximum1129
Range1128
Interquartile range (IQR)22

Descriptive statistics

Standard deviation250.17091
Coefficient of variation (CV)3.6099698
Kurtosis19.723014
Mean69.3
Median Absolute Deviation (MAD)4
Skewness4.4290981
Sum1386
Variance62585.484
MonotonicityNot monotonic
2023-12-12T21:12:18.868079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 6
19.4%
4 2
 
6.5%
6 2
 
6.5%
22 1
 
3.2%
1129 1
 
3.2%
11 1
 
3.2%
20 1
 
3.2%
26 1
 
3.2%
40 1
 
3.2%
27 1
 
3.2%
Other values (3) 3
 
9.7%
(Missing) 11
35.5%
ValueCountFrequency (%)
1 6
19.4%
2 1
 
3.2%
3 1
 
3.2%
4 2
 
6.5%
6 2
 
6.5%
11 1
 
3.2%
20 1
 
3.2%
22 1
 
3.2%
26 1
 
3.2%
27 1
 
3.2%
ValueCountFrequency (%)
1129 1
3.2%
80 1
3.2%
40 1
3.2%
27 1
3.2%
26 1
3.2%
22 1
3.2%
20 1
3.2%
11 1
3.2%
6 2
6.5%
4 2
6.5%

Interactions

2023-12-12T21:12:15.536310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:13.637880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.169106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.606728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.072034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.627361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:13.755208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.275353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.696383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.172179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.726790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:13.863035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.352624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.774943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.277923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.825602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:13.976483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.448235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.873212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.369666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.990038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.071756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.526891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:14.963098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:15.453184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:12:18.971287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종계대인119안전센터용산119안전센터지산119안전센터
연번1.0001.0000.0000.4880.0000.000
업종1.0001.0001.0001.0001.0001.000
계0.0001.0001.0000.6360.6480.623
대인119안전센터0.4881.0000.6361.0000.6360.612
용산119안전센터0.0001.0000.6480.6361.0000.623
지산119안전센터0.0001.0000.6230.6120.6231.000
2023-12-12T21:12:19.095041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번계대인119안전센터용산119안전센터지산119안전센터
연번1.000-0.342-0.388-0.420-0.161
계-0.3421.0000.9480.9400.958
대인119안전센터-0.3880.9481.0000.8300.922
용산119안전센터-0.4200.9400.8301.0000.875
지산119안전센터-0.1610.9580.9220.8751.000

Missing values

2023-12-12T21:12:16.116941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:12:16.248532image/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.
2023-12-12T21:12:16.395946image/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

연번업종계대인119안전센터용산119안전센터지산119안전센터
01공동주택(아파트)98136322
12공동주택(기숙사)6<NA>51
23근린생활465125409821129
34문화 및 집회시설206131
45종교시설55182611
56판매시설1293<NA>
67운수시설1165<NA>
78의료시설21984
89교육연구시설53151820
910노유자시설123385926
연번업종계대인119안전센터용산119안전센터지산119안전센터
2122교정 및 군사<NA><NA><NA><NA>
2223방송통신시설5131
2324발전시설<NA><NA><NA><NA>
2425묘지관련시설<NA><NA><NA><NA>
2526관광휴게시설<NA><NA><NA><NA>
2627장례식장312<NA>
2728지하가5122
2829지하구422<NA>
2930문화재4211
3031복합건축물3281925680