Overview

Dataset statistics

Number of variables6
Number of observations44
Missing cells2
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory57.0 B

Variable types

Numeric6

Dataset

Description동부, 서부, 남부, 중부 광산안전사무소 관할 광산(전국)에서 발생한 광산 재해에 대하여 연도별로 정리한 재해 현황의 정보 제공
URLhttps://www.data.go.kr/data/15054409/fileData.do

Alerts

연도별 is highly overall correlated with 광산수 and 4 other fieldsHigh correlation
광산수 is highly overall correlated with 연도별 and 4 other fieldsHigh correlation
재해발생자수 is highly overall correlated with 연도별 and 4 other fieldsHigh correlation
사망 is highly overall correlated with 연도별 and 4 other fieldsHigh correlation
중상 is highly overall correlated with 연도별 and 4 other fieldsHigh correlation
경상 is highly overall correlated with 연도별 and 4 other fieldsHigh correlation
광산수 has 2 (4.5%) missing valuesMissing
연도별 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:54:49.670120
Analysis finished2023-12-12 12:54:53.790696
Duration4.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도별
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.5
Minimum1979
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T21:54:53.875574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1979
5-th percentile1981.15
Q11989.75
median2000.5
Q32011.25
95-th percentile2019.85
Maximum2022
Range43
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation12.845233
Coefficient of variation (CV)0.006421011
Kurtosis-1.2
Mean2000.5
Median Absolute Deviation (MAD)11
Skewness0
Sum88022
Variance165
MonotonicityStrictly increasing
2023-12-12T21:54:54.031775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1979 1
 
2.3%
2002 1
 
2.3%
2004 1
 
2.3%
2005 1
 
2.3%
2006 1
 
2.3%
2007 1
 
2.3%
2008 1
 
2.3%
2009 1
 
2.3%
2010 1
 
2.3%
2011 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
1979 1
2.3%
1980 1
2.3%
1981 1
2.3%
1982 1
2.3%
1983 1
2.3%
1984 1
2.3%
1985 1
2.3%
1986 1
2.3%
1987 1
2.3%
1988 1
2.3%
ValueCountFrequency (%)
2022 1
2.3%
2021 1
2.3%
2020 1
2.3%
2019 1
2.3%
2018 1
2.3%
2017 1
2.3%
2016 1
2.3%
2015 1
2.3%
2014 1
2.3%
2013 1
2.3%

광산수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)85.7%
Missing2
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean588.57143
Minimum310
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T21:54:54.199097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum310
5-th percentile331.25
Q1384.25
median440
Q3683.75
95-th percentile1176
Maximum1200
Range890
Interquartile range (IQR)299.5

Descriptive statistics

Standard deviation293.97156
Coefficient of variation (CV)0.49946623
Kurtosis-0.22967631
Mean588.57143
Median Absolute Deviation (MAD)67
Skewness1.1812816
Sum24720
Variance86419.275
MonotonicityNot monotonic
2023-12-12T21:54:54.362343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
378 3
 
6.8%
439 2
 
4.5%
371 2
 
4.5%
422 2
 
4.5%
416 2
 
4.5%
492 1
 
2.3%
472 1
 
2.3%
464 1
 
2.3%
451 1
 
2.3%
441 1
 
2.3%
Other values (26) 26
59.1%
(Missing) 2
 
4.5%
ValueCountFrequency (%)
310 1
 
2.3%
325 1
 
2.3%
330 1
 
2.3%
355 1
 
2.3%
362 1
 
2.3%
371 2
4.5%
375 1
 
2.3%
378 3
6.8%
403 1
 
2.3%
411 1
 
2.3%
ValueCountFrequency (%)
1200 1
2.3%
1191 1
2.3%
1177 1
2.3%
1157 1
2.3%
1097 1
2.3%
1094 1
2.3%
1089 1
2.3%
1036 1
2.3%
958 1
2.3%
798 1
2.3%

재해발생자수
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1840.3409
Minimum22
Maximum6550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T21:54:54.500646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26.9
Q146
median155.5
Q34399
95-th percentile6474.4
Maximum6550
Range6528
Interquartile range (IQR)4353

Descriptive statistics

Standard deviation2588.5321
Coefficient of variation (CV)1.4065503
Kurtosis-0.90113255
Mean1840.3409
Median Absolute Deviation (MAD)126.5
Skewness0.98407777
Sum80975
Variance6700498.5
MonotonicityNot monotonic
2023-12-12T21:54:54.649952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
32 3
 
6.8%
57 2
 
4.5%
34 2
 
4.5%
59 2
 
4.5%
63 1
 
2.3%
139 1
 
2.3%
88 1
 
2.3%
65 1
 
2.3%
42 1
 
2.3%
43 1
 
2.3%
Other values (29) 29
65.9%
ValueCountFrequency (%)
22 1
 
2.3%
24 1
 
2.3%
26 1
 
2.3%
32 3
6.8%
34 2
4.5%
37 1
 
2.3%
42 1
 
2.3%
43 1
 
2.3%
47 1
 
2.3%
54 1
 
2.3%
ValueCountFrequency (%)
6550 1
2.3%
6529 1
2.3%
6508 1
2.3%
6284 1
2.3%
6216 1
2.3%
6050 1
2.3%
5883 1
2.3%
5879 1
2.3%
5776 1
2.3%
5467 1
2.3%

사망
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.795455
Minimum1
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T21:54:54.789604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median16
Q3130.5
95-th percentile204.1
Maximum247
Range246
Interquartile range (IQR)124.5

Descriptive statistics

Standard deviation81.201323
Coefficient of variation (CV)1.2531947
Kurtosis-0.6351411
Mean64.795455
Median Absolute Deviation (MAD)12
Skewness1.0222301
Sum2851
Variance6593.6549
MonotonicityNot monotonic
2023-12-12T21:54:54.922718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4 4
 
9.1%
6 4
 
9.1%
5 4
 
9.1%
7 2
 
4.5%
193 2
 
4.5%
9 2
 
4.5%
90 2
 
4.5%
17 2
 
4.5%
247 1
 
2.3%
15 1
 
2.3%
Other values (20) 20
45.5%
ValueCountFrequency (%)
1 1
 
2.3%
3 1
 
2.3%
4 4
9.1%
5 4
9.1%
6 4
9.1%
7 2
4.5%
9 2
4.5%
10 1
 
2.3%
11 1
 
2.3%
12 1
 
2.3%
ValueCountFrequency (%)
247 1
2.3%
226 1
2.3%
205 1
2.3%
199 1
2.3%
193 2
4.5%
191 1
2.3%
190 1
2.3%
187 1
2.3%
173 1
2.3%
132 1
2.3%

중상
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean838.02273
Minimum13
Maximum2873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T21:54:55.055830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15.15
Q123.5
median62.5
Q32196.75
95-th percentile2807.5
Maximum2873
Range2860
Interquartile range (IQR)2173.25

Descriptive statistics

Standard deviation1141.7863
Coefficient of variation (CV)1.3624765
Kurtosis-1.0681057
Mean838.02273
Median Absolute Deviation (MAD)48
Skewness0.90973266
Sum36873
Variance1303676
MonotonicityNot monotonic
2023-12-12T21:54:55.185971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
24 3
 
6.8%
25 2
 
4.5%
19 2
 
4.5%
16 2
 
4.5%
29 1
 
2.3%
56 1
 
2.3%
38 1
 
2.3%
28 1
 
2.3%
18 1
 
2.3%
14 1
 
2.3%
Other values (29) 29
65.9%
ValueCountFrequency (%)
13 1
 
2.3%
14 1
 
2.3%
15 1
 
2.3%
16 2
4.5%
17 1
 
2.3%
18 1
 
2.3%
19 2
4.5%
20 1
 
2.3%
22 1
 
2.3%
24 3
6.8%
ValueCountFrequency (%)
2873 1
2.3%
2822 1
2.3%
2809 1
2.3%
2799 1
2.3%
2708 1
2.3%
2605 1
2.3%
2556 1
2.3%
2545 1
2.3%
2493 1
2.3%
2459 1
2.3%

경상
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean983.22727
Minimum4
Maximum3757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T21:54:55.335415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10.15
Q123.5
median64
Q32167.5
95-th percentile3562.9
Maximum3757
Range3753
Interquartile range (IQR)2144

Descriptive statistics

Standard deviation1433.2191
Coefficient of variation (CV)1.4576681
Kurtosis-0.74655048
Mean983.22727
Median Absolute Deviation (MAD)52.5
Skewness1.0514827
Sum43262
Variance2054116.9
MonotonicityNot monotonic
2023-12-12T21:54:55.460370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
26 3
 
6.8%
11 2
 
4.5%
13 2
 
4.5%
3361 1
 
2.3%
29 1
 
2.3%
44 1
 
2.3%
40 1
 
2.3%
39 1
 
2.3%
22 1
 
2.3%
28 1
 
2.3%
Other values (30) 30
68.2%
ValueCountFrequency (%)
4 1
2.3%
5 1
2.3%
10 1
2.3%
11 2
4.5%
12 1
2.3%
13 2
4.5%
15 1
2.3%
19 1
2.3%
22 1
2.3%
24 1
2.3%
ValueCountFrequency (%)
3757 1
2.3%
3671 1
2.3%
3565 1
2.3%
3551 1
2.3%
3388 1
2.3%
3361 1
2.3%
3271 1
2.3%
3240 1
2.3%
3172 1
2.3%
2946 1
2.3%

Interactions

2023-12-12T21:54:52.567530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:49.853399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.380058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.852981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.453138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.962475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:52.675731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:49.949041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.459142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.923820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.560127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:52.033303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:52.764546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.035602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.528395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.023224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.636810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:52.114001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:53.248935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.115495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.608355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.110208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.719221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:52.214705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:53.348192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.190901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.683316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.212861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.793036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:52.317076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:53.435818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.272640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:50.768171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.318056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:51.870465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:52.449033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:54:55.541472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별광산수재해발생자수사망중상경상
연도별1.0000.8170.5690.7310.6380.646
광산수0.8171.0000.8200.8730.8240.879
재해발생자수0.5690.8201.0000.8510.9990.947
사망0.7310.8730.8511.0000.8500.814
중상0.6380.8240.9990.8501.0000.947
경상0.6460.8790.9470.8140.9471.000
2023-12-12T21:54:55.649815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별광산수재해발생자수사망중상경상
연도별1.000-0.744-0.968-0.933-0.935-0.981
광산수-0.7441.0000.7440.6430.6980.751
재해발생자수-0.9680.7441.0000.9380.9830.991
사망-0.9330.6430.9381.0000.9360.939
중상-0.9350.6980.9830.9361.0000.963
경상-0.9810.7510.9910.9390.9631.000

Missing values

2023-12-12T21:54:53.590018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:54:53.731995image/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

연도별광산수재해발생자수사망중상경상
01979<NA>577624724933361
11980<NA>652918727083757
219811036655022628093671
319821094587919925453240
419831097546719024592946
519841200588319325563271
619851191621620526053551
719861177650819328733565
819871157628419127993388
919881089605017328223172
연도별광산수재해발생자수사망중상경상
3420134165762724
3520143713241713
3620153783251611
3720163623761915
3820173752651512
3920183553272010
4020193303442213
412020310226134
422021325243165
4320224773441911