Overview

Dataset statistics

Number of variables4
Number of observations479
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 KiB
Average record size in memory35.3 B

Variable types

Text1
Categorical1
Numeric2

Dataset

Description한국전기안전공사에서 제공하는 2021년 지역별(시, 군, 구) 전기화재 건수 및 전기화재로 인한 재산피해 금액을 확인 할 수 있는 데이터입니다.
Author한국전기안전공사
URLhttps://www.data.go.kr/data/15069683/fileData.do

Alerts

전기화재 건수 is highly overall correlated with 재산피해(천원)High correlation
재산피해(천원) is highly overall correlated with 전기화재 건수High correlation
재산피해(천원) is highly skewed (γ1 = 21.78443833)Skewed
재산피해(천원) has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:09:32.600915
Analysis finished2023-12-12 21:09:33.354348
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역별
Text

Distinct239
Distinct (%)49.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-13T06:09:33.694746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.2296451
Min length2

Characters and Unicode

Total characters1547
Distinct characters147
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)9.0%

Sample

1st row강남구
2nd row강동구
3rd row강북구
4th row강서구
5th row관악구
ValueCountFrequency (%)
동구 12
 
2.3%
중구 12
 
2.3%
서구 10
 
2.0%
남구 9
 
1.8%
북구 9
 
1.8%
창원시 6
 
1.2%
수원시 5
 
1.0%
청주시 5
 
1.0%
용인시 4
 
0.8%
성남시 4
 
0.8%
Other values (227) 435
85.1%
2023-12-13T06:09:34.229330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 180
 
11.6%
시 179
 
11.6%
군 170
 
11.0%
천 45
 
2.9%
주 44
 
2.8%
양 40
 
2.6%
산 39
 
2.5%
성 39
 
2.5%
동 37
 
2.4%
32
 
2.1%
Other values (137) 742
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1515
97.9%
Space Separator 32
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 180
 
11.9%
시 179
 
11.8%
군 170
 
11.2%
천 45
 
3.0%
주 44
 
2.9%
양 40
 
2.6%
산 39
 
2.6%
성 39
 
2.6%
동 37
 
2.4%
서 29
 
1.9%
Other values (136) 713
47.1%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1515
97.9%
Common 32
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 180
 
11.9%
시 179
 
11.8%
군 170
 
11.2%
천 45
 
3.0%
주 44
 
2.9%
양 40
 
2.6%
산 39
 
2.6%
성 39
 
2.6%
동 37
 
2.4%
서 29
 
1.9%
Other values (136) 713
47.1%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1515
97.9%
ASCII 32
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 180
 
11.9%
시 179
 
11.8%
군 170
 
11.2%
천 45
 
3.0%
주 44
 
2.9%
양 40
 
2.6%
산 39
 
2.6%
성 39
 
2.6%
동 37
 
2.4%
서 29
 
1.9%
Other values (136) 713
47.1%
ASCII
ValueCountFrequency (%)
32
100.0%

연도
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2021
250 
2022
229 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 250
52.2%
2022 229
47.8%

Length

2023-12-13T06:09:34.402174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:09:34.507334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 250
52.2%
2022 229
47.8%

전기화재 건수
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.580376
Minimum1
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T06:09:34.641872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q117
median29
Q345
95-th percentile92.2
Maximum184
Range183
Interquartile range (IQR)28

Descriptive statistics

Standard deviation26.359228
Coefficient of variation (CV)0.74083613
Kurtosis4.9659519
Mean35.580376
Median Absolute Deviation (MAD)13
Skewness1.8808985
Sum17043
Variance694.8089
MonotonicityNot monotonic
2023-12-13T06:09:34.811861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 16
 
3.3%
15 16
 
3.3%
41 15
 
3.1%
17 15
 
3.1%
18 14
 
2.9%
35 14
 
2.9%
12 14
 
2.9%
31 11
 
2.3%
27 11
 
2.3%
26 11
 
2.3%
Other values (91) 342
71.4%
ValueCountFrequency (%)
1 1
 
0.2%
2 4
0.8%
3 2
 
0.4%
4 5
1.0%
5 3
0.6%
6 3
0.6%
7 3
0.6%
8 7
1.5%
9 6
1.3%
10 6
1.3%
ValueCountFrequency (%)
184 1
0.2%
168 1
0.2%
152 1
0.2%
128 1
0.2%
127 1
0.2%
126 1
0.2%
124 1
0.2%
117 1
0.2%
116 1
0.2%
115 1
0.2%

재산피해(천원)
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct479
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1568341.3
Minimum300
Maximum4.7571297 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-13T06:09:34.980998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile21976.8
Q189517.5
median196763
Q3441307.5
95-th percentile2682676.4
Maximum4.7571297 × 108
Range4.7571267 × 108
Interquartile range (IQR)351790

Descriptive statistics

Standard deviation21743365
Coefficient of variation (CV)13.863924
Kurtosis476.00808
Mean1568341.3
Median Absolute Deviation (MAD)138848
Skewness21.784438
Sum7.5123549 × 108
Variance4.7277391 × 1014
MonotonicityNot monotonic
2023-12-13T06:09:35.112075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216595 1
 
0.2%
62107 1
 
0.2%
569207 1
 
0.2%
272044 1
 
0.2%
10753493 1
 
0.2%
541925 1
 
0.2%
863089 1
 
0.2%
44191 1
 
0.2%
476314 1
 
0.2%
130874 1
 
0.2%
Other values (469) 469
97.9%
ValueCountFrequency (%)
300 1
0.2%
618 1
0.2%
1817 1
0.2%
2256 1
0.2%
2634 1
0.2%
2658 1
0.2%
2818 1
0.2%
5063 1
0.2%
6417 1
0.2%
6479 1
0.2%
ValueCountFrequency (%)
475712969 1
0.2%
10753493 1
0.2%
8449525 1
0.2%
8208981 1
0.2%
7118580 1
0.2%
6549800 1
0.2%
6216401 1
0.2%
6196715 1
0.2%
5689454 1
0.2%
5601959 1
0.2%

Interactions

2023-12-13T06:09:32.978895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:32.775152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:33.064641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:32.891511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:09:35.200012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도전기화재 건수재산피해(천원)
연도1.0000.0690.000
전기화재 건수0.0691.0000.221
재산피해(천원)0.0000.2211.000
2023-12-13T06:09:35.288693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기화재 건수재산피해(천원)연도
전기화재 건수1.0000.5170.068
재산피해(천원)0.5171.0000.000
연도0.0680.0001.000

Missing values

2023-12-13T06:09:33.184005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:09:33.302280image/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

지역별연도전기화재 건수재산피해(천원)
0강남구202195216595
1강동구20214162107
2강북구20213935222
3강서구202166245748
4관악구20216598981
5광진구202154215210
6구로구202151688551
7금천구20213997536
8노원구202147185436
9도봉구202146107993
지역별연도전기화재 건수재산피해(천원)
469진주시202241177579
470창녕군202234331201
471창원시2022127475531
472통영시202215157634
473하동군202217209497
474함안군202218246771
475함양군202214223965
476합천군2022281086501
477서귀포시202266553373
478제주시20221002331042