Overview

Dataset statistics

Number of variables7
Number of observations300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 KiB
Average record size in memory62.4 B

Variable types

Categorical1
Text1
Numeric5

Dataset

Description2010년부터 2013년까지 한국전기안전공사 사업소별(지사별) 전기화재현황을 제공하는 데이터로 사업소, 총화재, 전기화재, 점유율, 전년동기점유율, 전년동기대비실적을 제공합니다.
Author한국전기안전공사
URLhttps://www.data.go.kr/data/15043827/fileData.do

Alerts

총화재 is highly overall correlated with 전기화재High correlation
전기화재 is highly overall correlated with 총화재High correlation
점유율 is highly overall correlated with 전년동기점유율High correlation
전년동기점유율 is highly overall correlated with 점유율High correlation
총화재 has 9 (3.0%) zerosZeros
전기화재 has 12 (4.0%) zerosZeros
점유율 has 12 (4.0%) zerosZeros
전년동기점유율 has 12 (4.0%) zerosZeros
전년동기대비실적 has 17 (5.7%) zerosZeros

Reproduction

Analysis started2023-12-12 13:34:43.720583
Analysis finished2023-12-12 13:34:46.768600
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2013
75 
2012
75 
2011
75 
2010
75 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 75
25.0%
2012 75
25.0%
2011 75
25.0%
2010 75
25.0%

Length

2023-12-12T22:34:46.834200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:34:46.939383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 75
25.0%
2012 75
25.0%
2011 75
25.0%
2010 75
25.0%

사업소
Text

Distinct75
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T22:34:47.167875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.0666667
Min length4

Characters and Unicode

Total characters1820
Distinct characters57
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울지역본부
2nd row서울본부직할
3rd row서울동부지사
4th row서울서부지사
5th row서울남부지사
ValueCountFrequency (%)
서울지역본부 4
 
1.3%
경기중부지사 4
 
1.3%
강원북부지사 4
 
1.3%
원주횡성지사 4
 
1.3%
강원동부지사 4
 
1.3%
강원본부직할 4
 
1.3%
강원지역본부 4
 
1.3%
경기북동부지사 4
 
1.3%
파주고양지사 4
 
1.3%
경기북부본부직할 4
 
1.3%
Other values (65) 260
86.7%
2023-12-12T22:34:47.559578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 248
 
13.6%
부 236
 
13.0%
사 200
 
11.0%
본 100
 
5.5%
남 88
 
4.8%
경 76
 
4.2%
북 68
 
3.7%
서 64
 
3.5%
할 52
 
2.9%
직 52
 
2.9%
Other values (47) 636
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1820
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 248
 
13.6%
부 236
 
13.0%
사 200
 
11.0%
본 100
 
5.5%
남 88
 
4.8%
경 76
 
4.2%
북 68
 
3.7%
서 64
 
3.5%
할 52
 
2.9%
직 52
 
2.9%
Other values (47) 636
34.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1820
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 248
 
13.6%
부 236
 
13.0%
사 200
 
11.0%
본 100
 
5.5%
남 88
 
4.8%
경 76
 
4.2%
북 68
 
3.7%
서 64
 
3.5%
할 52
 
2.9%
직 52
 
2.9%
Other values (47) 636
34.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1820
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 248
 
13.6%
부 236
 
13.0%
사 200
 
11.0%
본 100
 
5.5%
남 88
 
4.8%
경 76
 
4.2%
북 68
 
3.7%
서 64
 
3.5%
할 52
 
2.9%
직 52
 
2.9%
Other values (47) 636
34.9%

총화재
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct258
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1063.56
Minimum0
Maximum6866
Zeros9
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T22:34:47.708840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile132.95
Q1378.75
median687
Q31120
95-th percentile3647.45
Maximum6866
Range6866
Interquartile range (IQR)741.25

Descriptive statistics

Standard deviation1218.5372
Coefficient of variation (CV)1.1457155
Kurtosis6.4914185
Mean1063.56
Median Absolute Deviation (MAD)356
Skewness2.5054552
Sum319068
Variance1484832.9
MonotonicityNot monotonic
2023-12-12T22:34:47.853517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
3.0%
635 4
 
1.3%
258 3
 
1.0%
1120 3
 
1.0%
687 3
 
1.0%
791 3
 
1.0%
500 2
 
0.7%
703 2
 
0.7%
375 2
 
0.7%
291 2
 
0.7%
Other values (248) 267
89.0%
ValueCountFrequency (%)
0 9
3.0%
82 1
 
0.3%
88 1
 
0.3%
109 1
 
0.3%
120 1
 
0.3%
125 1
 
0.3%
132 1
 
0.3%
133 1
 
0.3%
138 1
 
0.3%
139 1
 
0.3%
ValueCountFrequency (%)
6866 1
0.3%
6588 1
0.3%
6445 1
0.3%
5724 1
0.3%
5526 1
0.3%
5321 1
0.3%
5221 1
0.3%
5052 1
0.3%
4880 1
0.3%
4631 1
0.3%

전기화재
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct205
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.23
Minimum0
Maximum1560
Zeros12
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T22:34:48.062211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q171
median135.5
Q3265.5
95-th percentile738.75
Maximum1560
Range1560
Interquartile range (IQR)194.5

Descriptive statistics

Standard deviation273.38814
Coefficient of variation (CV)1.2084522
Kurtosis8.9703728
Mean226.23
Median Absolute Deviation (MAD)79.5
Skewness2.7923937
Sum67869
Variance74741.074
MonotonicityNot monotonic
2023-12-12T22:34:48.229161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
4.0%
61 6
 
2.0%
71 6
 
2.0%
111 5
 
1.7%
196 4
 
1.3%
99 4
 
1.3%
96 3
 
1.0%
107 3
 
1.0%
116 3
 
1.0%
97 3
 
1.0%
Other values (195) 251
83.7%
ValueCountFrequency (%)
0 12
4.0%
16 1
 
0.3%
21 3
 
1.0%
23 3
 
1.0%
24 1
 
0.3%
27 1
 
0.3%
29 1
 
0.3%
30 2
 
0.7%
32 1
 
0.3%
33 1
 
0.3%
ValueCountFrequency (%)
1560 1
0.3%
1558 1
0.3%
1518 1
0.3%
1502 1
0.3%
1473 1
0.3%
1415 1
0.3%
1087 1
0.3%
1070 1
0.3%
1006 1
0.3%
1005 1
0.3%

점유율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.853333
Minimum0
Maximum45.5
Zeros12
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T22:34:48.392624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.995
Q117.575
median20.2
Q322.5
95-th percentile27.91
Maximum45.5
Range45.5
Interquartile range (IQR)4.925

Descriptive statistics

Standard deviation5.6958962
Coefficient of variation (CV)0.28689873
Kurtosis5.1984614
Mean19.853333
Median Absolute Deviation (MAD)2.5
Skewness-1.0960696
Sum5956
Variance32.443233
MonotonicityNot monotonic
2023-12-12T22:34:48.571727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
4.0%
19.6 9
 
3.0%
18.0 9
 
3.0%
17.4 8
 
2.7%
21.0 7
 
2.3%
21.7 6
 
2.0%
21.8 6
 
2.0%
21.1 5
 
1.7%
19.8 5
 
1.7%
22.8 5
 
1.7%
Other values (119) 228
76.0%
ValueCountFrequency (%)
0.0 12
4.0%
9.4 1
 
0.3%
12.1 1
 
0.3%
12.9 1
 
0.3%
13.0 1
 
0.3%
13.2 1
 
0.3%
13.3 1
 
0.3%
13.7 1
 
0.3%
13.9 1
 
0.3%
14.0 1
 
0.3%
ValueCountFrequency (%)
45.5 1
0.3%
32.9 1
0.3%
31.6 1
0.3%
31.1 1
0.3%
30.9 1
0.3%
30.3 1
0.3%
29.8 1
0.3%
29.5 1
0.3%
29.1 1
0.3%
29.0 1
0.3%

전년동기점유율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct138
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.800333
Minimum0
Maximum32.9
Zeros12
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T22:34:48.720175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.975
Q117.5
median20.2
Q323.3
95-th percentile28.1
Maximum32.9
Range32.9
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation5.7533849
Coefficient of variation (CV)0.2905701
Kurtosis3.6366055
Mean19.800333
Median Absolute Deviation (MAD)2.8
Skewness-1.3384651
Sum5940.1
Variance33.101438
MonotonicityNot monotonic
2023-12-12T22:34:48.886449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
4.0%
21.8 7
 
2.3%
18.0 7
 
2.3%
19.6 6
 
2.0%
17.6 6
 
2.0%
22.5 5
 
1.7%
24.3 5
 
1.7%
20.8 4
 
1.3%
17.7 4
 
1.3%
17.8 4
 
1.3%
Other values (128) 240
80.0%
ValueCountFrequency (%)
0.0 12
4.0%
8.3 1
 
0.3%
9.4 1
 
0.3%
11.5 1
 
0.3%
12.0 1
 
0.3%
13.1 1
 
0.3%
13.3 1
 
0.3%
13.4 2
 
0.7%
13.6 1
 
0.3%
13.7 1
 
0.3%
ValueCountFrequency (%)
32.9 1
0.3%
31.1 2
0.7%
30.9 1
0.3%
30.3 1
0.3%
30.1 1
0.3%
29.8 1
0.3%
29.5 1
0.3%
29.1 2
0.7%
28.8 1
0.3%
28.6 1
0.3%

전년동기대비실적
Real number (ℝ)

ZEROS 

Distinct121
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.053
Minimum-12.9
Maximum25
Zeros17
Zeros (%)5.7%
Negative142
Negative (%)47.3%
Memory size2.8 KiB
2023-12-12T22:34:49.354221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12.9
5-th percentile-6.31
Q1-2.1
median0
Q32.525
95-th percentile6
Maximum25
Range37.9
Interquartile range (IQR)4.625

Descriptive statistics

Standard deviation3.8966958
Coefficient of variation (CV)73.522563
Kurtosis5.2455694
Mean0.053
Median Absolute Deviation (MAD)2.4
Skewness0.68909901
Sum15.9
Variance15.184238
MonotonicityNot monotonic
2023-12-12T22:34:49.501476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
5.7%
2.6 7
 
2.3%
0.5 7
 
2.3%
-2.0 7
 
2.3%
-0.5 6
 
2.0%
0.7 5
 
1.7%
1.5 5
 
1.7%
4.0 5
 
1.7%
-1.2 5
 
1.7%
-0.9 5
 
1.7%
Other values (111) 231
77.0%
ValueCountFrequency (%)
-12.9 1
0.3%
-9.6 1
0.3%
-9.2 1
0.3%
-8.4 1
0.3%
-7.9 2
0.7%
-7.5 1
0.3%
-7.4 2
0.7%
-7.2 2
0.7%
-7.1 1
0.3%
-6.8 1
0.3%
ValueCountFrequency (%)
25.0 1
 
0.3%
11.5 1
 
0.3%
9.0 1
 
0.3%
8.8 1
 
0.3%
7.8 1
 
0.3%
7.4 2
0.7%
7.1 1
 
0.3%
7.0 1
 
0.3%
6.5 1
 
0.3%
6.4 3
1.0%

Interactions

2023-12-12T22:34:46.070234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.028198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.545304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.120048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.612033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:46.173697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.129972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.644061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.207889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.709069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:46.277830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.225277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.779668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.297931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.818165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:46.379145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.325898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.881950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.398956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.900693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:46.477640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:44.440346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.000100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.511181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:34:45.985446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:34:49.597812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년사업소총화재전기화재점유율전년동기점유율전년동기대비실적
년1.0000.0000.0000.0000.3070.0620.527
사업소0.0001.0000.9430.9450.8420.8330.000
총화재0.0000.9431.0000.9170.2290.2370.000
전기화재0.0000.9450.9171.0000.5210.3050.000
점유율0.3070.8420.2290.5211.0000.7080.826
전년동기점유율0.0620.8330.2370.3050.7081.0000.599
전년동기대비실적0.5270.0000.0000.0000.8260.5991.000
2023-12-12T22:34:49.719116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총화재전기화재점유율전년동기점유율전년동기대비실적년
총화재1.0000.9780.2750.280-0.0500.000
전기화재0.9781.0000.4300.3550.0320.000
점유율0.2750.4301.0000.6060.3370.135
전년동기점유율0.2800.3550.6061.000-0.4920.038
전년동기대비실적-0.0500.0320.337-0.4921.0000.255
년0.0000.0000.1350.0380.2551.000

Missing values

2023-12-12T22:34:46.609654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:34:46.720384image/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

년사업소총화재전기화재점유율전년동기점유율전년동기대비실적
02013서울지역본부4631100521.725.6-3.9
12013서울본부직할106423922.525.9-3.4
22013서울동부지사98324524.929.5-4.6
32013서울서부지사82016119.627.1-7.5
42013서울남부지사95717418.225.3-7.1
52013서울북부지사80718623.020.42.6
62013부산울산지역본부226746620.718.12.6
72013부산울산본부직할112023320.820.30.5
82013울산지사68713018.914.54.4
92013부산동부지사46010322.419.52.9
년사업소총화재전기화재점유율전년동기점유율전년동기대비실적
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