Overview

Dataset statistics

Number of variables3
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory934.0 B
Average record size in memory30.1 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description2022년 서울특별시경찰청 관할 31개 경찰서별 방화 발생건수입니다. (2022년, 서울경찰청 관할 31개 경찰서, 방화발생건수 현황 입니다.)
Author경찰청 서울특별시경찰청
URLhttps://www.data.go.kr/data/15005637/fileData.do

Alerts

연도 has constant value ""Constant
관서명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 12:23:02.986304
Analysis finished2024-03-14 12:23:03.752563
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size376.0 B
2022
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 31
100.0%

Length

2024-03-14T21:23:03.960609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:23:04.445251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 31
100.0%

관서명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size376.0 B
2024-03-14T21:23:05.124178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1290323
Min length3

Characters and Unicode

Total characters97
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row중부서
2nd row종로서
3rd row남대문서
4th row서대문서
5th row혜화서
ValueCountFrequency (%)
중부서 1
 
3.2%
중랑서 1
 
3.2%
도봉서 1
 
3.2%
은평서 1
 
3.2%
방배서 1
 
3.2%
노원서 1
 
3.2%
송파서 1
 
3.2%
양천서 1
 
3.2%
서초서 1
 
3.2%
구로서 1
 
3.2%
Other values (21) 21
67.7%
2024-03-14T21:23:06.139413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
37.1%
4
 
4.1%
4
 
4.1%
3
 
3.1%
3
 
3.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (33) 37
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
36
37.1%
4
 
4.1%
4
 
4.1%
3
 
3.1%
3
 
3.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (33) 37
38.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
36
37.1%
4
 
4.1%
4
 
4.1%
3
 
3.1%
3
 
3.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (33) 37
38.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
36
37.1%
4
 
4.1%
4
 
4.1%
3
 
3.1%
3
 
3.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (33) 37
38.1%

발생건수
Real number (ℝ)

Distinct13
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T21:23:06.329209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q14.5
median7
Q39
95-th percentile13.5
Maximum15
Range14
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.4928498
Coefficient of variation (CV)0.49897855
Kurtosis0.045071045
Mean7
Median Absolute Deviation (MAD)2
Skewness0.47160869
Sum217
Variance12.2
MonotonicityNot monotonic
2024-03-14T21:23:06.611272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7 6
19.4%
4 5
16.1%
9 4
12.9%
6 3
9.7%
5 3
9.7%
10 2
 
6.5%
1 2
 
6.5%
2 1
 
3.2%
14 1
 
3.2%
8 1
 
3.2%
Other values (3) 3
9.7%
ValueCountFrequency (%)
1 2
 
6.5%
2 1
 
3.2%
4 5
16.1%
5 3
9.7%
6 3
9.7%
7 6
19.4%
8 1
 
3.2%
9 4
12.9%
10 2
 
6.5%
12 1
 
3.2%
ValueCountFrequency (%)
15 1
 
3.2%
14 1
 
3.2%
13 1
 
3.2%
12 1
 
3.2%
10 2
 
6.5%
9 4
12.9%
8 1
 
3.2%
7 6
19.4%
6 3
9.7%
5 3
9.7%

Interactions

2024-03-14T21:23:03.096960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:23:06.752768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관서명발생건수
관서명1.0001.000
발생건수1.0001.000

Missing values

2024-03-14T21:23:03.415664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:23:03.656971image/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

연도관서명발생건수
02022중부서7
12022종로서6
22022남대문서6
32022서대문서9
42022혜화서2
52022용산서10
62022성북서4
72022동대문서5
82022마포서5
92022영등포서14
연도관서명발생건수
212022종암서1
222022구로서12
232022서초서9
242022양천서4
252022송파서10
262022노원서15
272022방배서4
282022은평서6
292022도봉서4
302022수서서9