Overview

Dataset statistics

Number of variables5
Number of observations31
Missing cells19
Missing cells (%)12.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory46.3 B

Variable types

Numeric2
Text3

Dataset

Description광주광역시 동구 특정소방대상물 증감현황에 대한 데이터로 작년과 올해의 특정소방대상물의 업종별 증감율을 설명하는 자료입니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15055241/fileData.do

Alerts

2022년 has 4 (12.9%) missing valuesMissing
2021년 has 4 (12.9%) missing valuesMissing
증감 has 11 (35.5%) missing valuesMissing
연번 has unique valuesUnique
업종 has unique valuesUnique
증감 has 1 (3.2%) zerosZeros

Reproduction

Analysis started2023-12-11 23:44:10.564374
Analysis finished2023-12-11 23:44:11.390460
Duration0.83 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
2023-12-12T08:44:11.448930image/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
2023-12-12T08:44:11.583365image/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
2023-12-12T08:44:11.765301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.4516129
Min length3

Characters and Unicode

Total characters169
Distinct characters81
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%
분뇨 1
 
2.2%
쓰레기 1
 
2.2%
교정 1
 
2.2%
Other values (30) 30
66.7%
2023-12-12T08:44:12.102893image/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 (71) 92
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
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
 
2.6%
물 4
 
2.6%
장 4
 
2.6%
관 3
 
2.0%
련 3
 
2.0%
교 3
 
2.0%
Other values (68) 85
56.3%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
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
 
2.6%
물 4
 
2.6%
장 4
 
2.6%
관 3
 
2.0%
련 3
 
2.0%
교 3
 
2.0%
Other values (68) 85
56.3%
Common
ValueCountFrequency (%)
14
77.8%
( 2
 
11.1%
) 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
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
 
2.6%
물 4
 
2.6%
장 4
 
2.6%
관 3
 
2.0%
련 3
 
2.0%
교 3
 
2.0%
Other values (68) 85
56.3%
ASCII
ValueCountFrequency (%)
14
77.8%
( 2
 
11.1%
) 2
 
11.1%

2022년
Text

MISSING 

Distinct25
Distinct (%)92.6%
Missing4
Missing (%)12.9%
Memory size380.0 B
2023-12-12T08:44:12.275661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.037037
Min length1

Characters and Unicode

Total characters55
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)85.2%

Sample

1st row90
2nd row8
3rd row7,918
4th row47
5th row43
ValueCountFrequency (%)
2 2
 
7.4%
4 2
 
7.4%
237 1
 
3.7%
90 1
 
3.7%
92 1
 
3.7%
12 1
 
3.7%
9 1
 
3.7%
5 1
 
3.7%
13 1
 
3.7%
6 1
 
3.7%
Other values (15) 15
55.6%
2023-12-12T08:44:12.576181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
18.2%
4 9
16.4%
1 7
12.7%
3 6
10.9%
0 6
10.9%
7 4
 
7.3%
9 4
 
7.3%
6 4
 
7.3%
8 3
 
5.5%
, 1
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54
98.2%
Other Punctuation 1
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10
18.5%
4 9
16.7%
1 7
13.0%
3 6
11.1%
0 6
11.1%
7 4
 
7.4%
9 4
 
7.4%
6 4
 
7.4%
8 3
 
5.6%
5 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10
18.2%
4 9
16.4%
1 7
12.7%
3 6
10.9%
0 6
10.9%
7 4
 
7.3%
9 4
 
7.3%
6 4
 
7.3%
8 3
 
5.5%
, 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10
18.2%
4 9
16.4%
1 7
12.7%
3 6
10.9%
0 6
10.9%
7 4
 
7.3%
9 4
 
7.3%
6 4
 
7.3%
8 3
 
5.5%
, 1
 
1.8%

2021년
Text

MISSING 

Distinct23
Distinct (%)85.2%
Missing4
Missing (%)12.9%
Memory size380.0 B
2023-12-12T08:44:12.763777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length1.962963
Min length1

Characters and Unicode

Total characters53
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)70.4%

Sample

1st row326
2nd row22
3rd row7,678
4th row37
5th row86
ValueCountFrequency (%)
6 2
 
7.4%
9 2
 
7.4%
4 2
 
7.4%
2 2
 
7.4%
105 1
 
3.7%
326 1
 
3.7%
243 1
 
3.7%
12 1
 
3.7%
5 1
 
3.7%
94 1
 
3.7%
Other values (13) 13
48.1%
2023-12-12T08:44:13.111033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 15
28.3%
6 6
 
11.3%
3 6
 
11.3%
9 5
 
9.4%
4 5
 
9.4%
7 4
 
7.5%
1 4
 
7.5%
5 3
 
5.7%
8 2
 
3.8%
0 2
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52
98.1%
Other Punctuation 1
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15
28.8%
6 6
 
11.5%
3 6
 
11.5%
9 5
 
9.6%
4 5
 
9.6%
7 4
 
7.7%
1 4
 
7.7%
5 3
 
5.8%
8 2
 
3.8%
0 2
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15
28.3%
6 6
 
11.3%
3 6
 
11.3%
9 5
 
9.4%
4 5
 
9.4%
7 4
 
7.5%
1 4
 
7.5%
5 3
 
5.7%
8 2
 
3.8%
0 2
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15
28.3%
6 6
 
11.3%
3 6
 
11.3%
9 5
 
9.4%
4 5
 
9.4%
7 4
 
7.5%
1 4
 
7.5%
5 3
 
5.7%
8 2
 
3.8%
0 2
 
3.8%

증감
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)95.0%
Missing11
Missing (%)35.5%
Infinite0
Infinite (%)0.0%
Mean9.2
Minimum-236
Maximum240
Zeros1
Zeros (%)3.2%
Negative7
Negative (%)22.6%
Memory size411.0 B
2023-12-12T08:44:13.273973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-236
5-th percentile-52.65
Q1-3
median6.5
Q315.25
95-th percentile101.3
Maximum240
Range476
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation82.281673
Coefficient of variation (CV)8.9436601
Kurtosis6.7428285
Mean9.2
Median Absolute Deviation (MAD)9
Skewness-0.18959297
Sum184
Variance6770.2737
MonotonicityNot monotonic
2023-12-12T08:44:13.407867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
15 2
 
6.5%
-6 1
 
3.2%
94 1
 
3.2%
7 1
 
3.2%
6 1
 
3.2%
-2 1
 
3.2%
8 1
 
3.2%
0 1
 
3.2%
-13 1
 
3.2%
-236 1
 
3.2%
Other values (9) 9
29.0%
(Missing) 11
35.5%
ValueCountFrequency (%)
-236 1
3.2%
-43 1
3.2%
-14 1
3.2%
-13 1
3.2%
-6 1
3.2%
-2 1
3.2%
-1 1
3.2%
0 1
3.2%
1 1
3.2%
6 1
3.2%
ValueCountFrequency (%)
240 1
3.2%
94 1
3.2%
68 1
3.2%
19 1
3.2%
16 1
3.2%
15 2
6.5%
10 1
3.2%
8 1
3.2%
7 1
3.2%
6 1
3.2%

Interactions

2023-12-12T08:44:10.960916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:10.777161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:11.058082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:44:10.880493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:44:13.490398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종2022년2021년증감
연번1.0001.0000.5080.8350.720
업종1.0001.0001.0001.0001.000
2022년0.5081.0001.0001.0001.000
2021년0.8351.0001.0001.0001.000
증감0.7201.0001.0001.0001.000
2023-12-12T08:44:13.593531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번증감
연번1.0000.155
증감0.1551.000

Missing values

2023-12-12T08:44:11.162575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:44:11.255113image/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.
2023-12-12T08:44:11.342030image/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

연번업종2022년2021년증감
01공동주택(아파트)90326-236
12공동주택(기숙사)822-14
23근린생활7,9187,678240
34문화 및 집회시설473710
45종교시설4386-43
56판매시설2021-1
67운수시설23716
78의료시설463115
89교육연구시설1609268
910노유자시설24122219
연번업종2022년2021년증감
2122교정 및 군사<NA><NA><NA>
2223방송통신시설55<NA>
2324발전시설<NA><NA><NA>
2425묘지관련시설<NA><NA><NA>
2526관광휴게시설<NA><NA><NA>
2627장례식장44<NA>
2728지하가99<NA>
2829지하구44<NA>
2930문화재1212<NA>
3031복합건축물34324994