Overview

Dataset statistics

Number of variables5
Number of observations5844
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory256.9 KiB
Average record size in memory45.0 B

Variable types

Categorical1
Numeric4

Dataset

Description2022년 남해지역의 월별 해양사고 발생건수를 예측한 정보로 향후 안전 대책 마련 등과 같은 통계 자료에 기초 자료로 활용 가능합니다.
Author한국해양교통안전공단
URLhttps://www.data.go.kr/data/15121021/fileData.do

Alerts

연도 has constant value ""Constant
사고예측위도 is highly overall correlated with 사고예측경도High correlation
사고예측경도 is highly overall correlated with 사고예측위도High correlation
사고예측건수 has 5217 (89.3%) zerosZeros

Reproduction

Analysis started2023-12-12 03:34:43.789632
Analysis finished2023-12-12 03:34:47.098698
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 KiB
2022
5844 

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 5844
100.0%

Length

2023-12-12T12:34:47.168074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:34:47.269201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 5844
100.0%

월
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2023-12-12T12:34:47.355504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4523479
Coefficient of variation (CV)0.53113045
Kurtosis-1.2167975
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum37986
Variance11.918706
MonotonicityNot monotonic
2023-12-12T12:34:47.458909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 487
8.3%
2 487
8.3%
3 487
8.3%
4 487
8.3%
5 487
8.3%
6 487
8.3%
7 487
8.3%
8 487
8.3%
9 487
8.3%
10 487
8.3%
Other values (2) 974
16.7%
ValueCountFrequency (%)
1 487
8.3%
2 487
8.3%
3 487
8.3%
4 487
8.3%
5 487
8.3%
6 487
8.3%
7 487
8.3%
8 487
8.3%
9 487
8.3%
10 487
8.3%
ValueCountFrequency (%)
12 487
8.3%
11 487
8.3%
10 487
8.3%
9 487
8.3%
8 487
8.3%
7 487
8.3%
6 487
8.3%
5 487
8.3%
4 487
8.3%
3 487
8.3%

사고예측위도
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.746239
Minimum34.031762
Maximum35.681762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2023-12-12T12:34:47.598891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.031762
5-th percentile34.231762
Q134.481762
median34.731762
Q334.981762
95-th percentile35.481762
Maximum35.681762
Range1.65
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.35808677
Coefficient of variation (CV)0.010305771
Kurtosis-0.10041546
Mean34.746239
Median Absolute Deviation (MAD)0.25
Skewness0.47263053
Sum203057.02
Variance0.12822613
MonotonicityNot monotonic
2023-12-12T12:34:47.769635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
34.63176245 360
 
6.2%
34.68176245 324
 
5.5%
34.58176245 324
 
5.5%
34.48176245 300
 
5.1%
34.93176245 300
 
5.1%
34.88176245 300
 
5.1%
34.53176245 288
 
4.9%
34.98176245 288
 
4.9%
34.43176245 276
 
4.7%
34.83176245 264
 
4.5%
Other values (24) 2820
48.3%
ValueCountFrequency (%)
34.03176245 24
 
0.4%
34.08176245 60
 
1.0%
34.13176245 60
 
1.0%
34.18176245 96
 
1.6%
34.23176245 204
3.5%
34.28176245 216
3.7%
34.33176245 180
3.1%
34.38176245 192
3.3%
34.43176245 276
4.7%
34.48176245 300
5.1%
ValueCountFrequency (%)
35.68176245 72
1.2%
35.63176245 60
1.0%
35.58176245 60
1.0%
35.53176245 72
1.2%
35.48176245 48
0.8%
35.43176245 36
0.6%
35.38176245 48
0.8%
35.33176245 48
0.8%
35.28176245 48
0.8%
35.23176245 60
1.0%

사고예측경도
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.17705
Minimum126.70344
Maximum129.70344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2023-12-12T12:34:47.911351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.70344
5-th percentile126.85344
Q1127.60344
median128.25344
Q3128.75344
95-th percentile129.45344
Maximum129.70344
Range3
Interquartile range (IQR)1.15

Descriptive statistics

Standard deviation0.80103489
Coefficient of variation (CV)0.0062494408
Kurtosis-0.98232186
Mean128.17705
Median Absolute Deviation (MAD)0.6
Skewness-0.059717095
Sum749066.69
Variance0.6416569
MonotonicityNot monotonic
2023-12-12T12:34:48.060878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.703437 168
 
2.9%
127.753437 168
 
2.9%
128.503437 168
 
2.9%
128.453437 168
 
2.9%
127.803437 168
 
2.9%
128.653437 156
 
2.7%
128.553437 156
 
2.7%
128.603437 144
 
2.5%
127.653437 144
 
2.5%
128.753437 144
 
2.5%
Other values (51) 4260
72.9%
ValueCountFrequency (%)
126.703437 36
 
0.6%
126.753437 108
1.8%
126.803437 96
1.6%
126.853437 96
1.6%
126.903437 108
1.8%
126.953437 60
1.0%
127.003437 108
1.8%
127.053437 84
1.4%
127.103437 108
1.8%
127.153437 84
1.4%
ValueCountFrequency (%)
129.703437 12
 
0.2%
129.653437 48
 
0.8%
129.603437 36
 
0.6%
129.553437 60
1.0%
129.503437 72
1.2%
129.453437 132
2.3%
129.403437 120
2.1%
129.353437 132
2.3%
129.303437 72
1.2%
129.253437 60
1.0%

사고예측건수
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14784394
Minimum0
Maximum6
Zeros5217
Zeros (%)89.3%
Negative0
Negative (%)0.0%
Memory size51.5 KiB
2023-12-12T12:34:48.206007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4978583
Coefficient of variation (CV)3.3674582
Kurtosis30.240295
Mean0.14784394
Median Absolute Deviation (MAD)0
Skewness4.7264775
Sum864
Variance0.24786288
MonotonicityNot monotonic
2023-12-12T12:34:48.337515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 5217
89.3%
1 466
 
8.0%
2 115
 
2.0%
3 26
 
0.4%
4 13
 
0.2%
5 4
 
0.1%
6 3
 
0.1%
ValueCountFrequency (%)
0 5217
89.3%
1 466
 
8.0%
2 115
 
2.0%
3 26
 
0.4%
4 13
 
0.2%
5 4
 
0.1%
6 3
 
0.1%
ValueCountFrequency (%)
6 3
 
0.1%
5 4
 
0.1%
4 13
 
0.2%
3 26
 
0.4%
2 115
 
2.0%
1 466
 
8.0%
0 5217
89.3%

Interactions

2023-12-12T12:34:46.343991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:44.227898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:45.162617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:45.746826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:46.488499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:44.368177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:45.293844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:45.882305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:46.630831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:44.515327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:45.462483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:46.057082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:46.746764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:45.009462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:45.599388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:34:46.220726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:34:48.456302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월사고예측위도사고예측경도사고예측건수
월1.0000.0000.0000.000
사고예측위도0.0001.0000.8810.159
사고예측경도0.0000.8811.0000.112
사고예측건수0.0000.1590.1121.000
2023-12-12T12:34:48.619144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월사고예측위도사고예측경도사고예측건수
월1.0000.0000.0000.029
사고예측위도0.0001.0000.8300.129
사고예측경도0.0000.8301.0000.086
사고예측건수0.0290.1290.0861.000

Missing values

2023-12-12T12:34:46.911103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:34:47.039455image/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

연도월사고예측위도사고예측경도사고예측건수
02022135.181762129.2534370
12022135.031762129.1034370
22022134.331762126.9534370
32022135.231762129.2534370
42022135.081762129.1034371
52022134.531762127.1034370
62022134.381762126.9534370
72022135.281762129.2534370
82022134.781762128.2534371
92022134.431762126.9534370
연도월사고예측위도사고예측경도사고예측건수
58342022934.981762127.9534370
58352022934.831762127.8034370
58362022934.881762127.8034370
58372022934.531762127.8034370
58382022934.731762127.9534370
58392022934.581762127.8034370
58402022934.631762127.8034370
58412022934.831762127.9534370
58422022934.681762127.8034370
58432022934.881762127.9534370