Overview

Dataset statistics

Number of variables5
Number of observations217
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory44.6 B

Variable types

Numeric4
Categorical1

Dataset

Description경기도_교통사고 집계 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=ESFYUGPHCO3ZGP58V1K018796267&infSeq=1

Alerts

발생건수(건) is highly overall correlated with 사망자수(명) and 2 other fieldsHigh correlation
사망자수(명) is highly overall correlated with 발생건수(건) and 1 other fieldsHigh correlation
부상자수(명) is highly overall correlated with 발생건수(건) and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 발생건수(건) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 22:47:02.851274
Analysis finished2023-12-10 22:47:04.943864
Duration2.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Real number (ℝ)

Distinct7
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T07:47:04.999626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0046243
Coefficient of variation (CV)0.00099287978
Kurtosis-1.2511193
Mean2019
Median Absolute Deviation (MAD)2
Skewness0
Sum438123
Variance4.0185185
MonotonicityDecreasing
2023-12-11T07:47:05.116376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2022 31
14.3%
2021 31
14.3%
2020 31
14.3%
2019 31
14.3%
2018 31
14.3%
2017 31
14.3%
2016 31
14.3%
ValueCountFrequency (%)
2016 31
14.3%
2017 31
14.3%
2018 31
14.3%
2019 31
14.3%
2020 31
14.3%
2021 31
14.3%
2022 31
14.3%
ValueCountFrequency (%)
2022 31
14.3%
2021 31
14.3%
2020 31
14.3%
2019 31
14.3%
2018 31
14.3%
2017 31
14.3%
2016 31
14.3%

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
가평군
 
7
고양시
 
7
과천시
 
7
광명시
 
7
광주시
 
7
Other values (26)
182 

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시

Common Values

ValueCountFrequency (%)
가평군 7
 
3.2%
고양시 7
 
3.2%
과천시 7
 
3.2%
광명시 7
 
3.2%
광주시 7
 
3.2%
구리시 7
 
3.2%
군포시 7
 
3.2%
김포시 7
 
3.2%
남양주시 7
 
3.2%
동두천시 7
 
3.2%
Other values (21) 147
67.7%

Length

2023-12-11T07:47:05.232417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가평군 7
 
3.2%
안양시 7
 
3.2%
하남시 7
 
3.2%
포천시 7
 
3.2%
평택시 7
 
3.2%
파주시 7
 
3.2%
이천시 7
 
3.2%
의정부시 7
 
3.2%
의왕시 7
 
3.2%
용인시 7
 
3.2%
Other values (21) 147
67.7%

발생건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct210
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1701.576
Minimum168
Maximum5121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T07:47:05.354591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168
5-th percentile227
Q1659
median1133
Q32716
95-th percentile3885.6
Maximum5121
Range4953
Interquartile range (IQR)2057

Descriptive statistics

Standard deviation1276.9799
Coefficient of variation (CV)0.75046889
Kurtosis-0.54007774
Mean1701.576
Median Absolute Deviation (MAD)663
Skewness0.78458904
Sum369242
Variance1630677.6
MonotonicityNot monotonic
2023-12-11T07:47:05.488315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1192 2
 
0.9%
1038 2
 
0.9%
1682 2
 
0.9%
997 2
 
0.9%
1693 2
 
0.9%
1690 2
 
0.9%
227 2
 
0.9%
432 1
 
0.5%
513 1
 
0.5%
2434 1
 
0.5%
Other values (200) 200
92.2%
ValueCountFrequency (%)
168 1
0.5%
178 1
0.5%
187 1
0.5%
189 1
0.5%
196 1
0.5%
199 1
0.5%
203 1
0.5%
207 1
0.5%
208 1
0.5%
218 1
0.5%
ValueCountFrequency (%)
5121 1
0.5%
4997 1
0.5%
4920 1
0.5%
4842 1
0.5%
4705 1
0.5%
4615 1
0.5%
4480 1
0.5%
3965 1
0.5%
3950 1
0.5%
3931 1
0.5%

사망자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.0553
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T07:47:05.637120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median18
Q331
95-th percentile46
Maximum61
Range60
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.687203
Coefficient of variation (CV)0.65005975
Kurtosis-0.071791142
Mean21.0553
Median Absolute Deviation (MAD)9
Skewness0.73401534
Sum4569
Variance187.33952
MonotonicityNot monotonic
2023-12-11T07:47:05.821724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 11
 
5.1%
15 10
 
4.6%
9 9
 
4.1%
5 9
 
4.1%
19 9
 
4.1%
10 8
 
3.7%
14 8
 
3.7%
11 7
 
3.2%
13 7
 
3.2%
17 6
 
2.8%
Other values (44) 133
61.3%
ValueCountFrequency (%)
1 3
 
1.4%
2 3
 
1.4%
3 4
 
1.8%
4 4
 
1.8%
5 9
4.1%
6 11
5.1%
7 4
 
1.8%
8 3
 
1.4%
9 9
4.1%
10 8
3.7%
ValueCountFrequency (%)
61 1
0.5%
60 1
0.5%
59 1
0.5%
56 1
0.5%
55 1
0.5%
54 1
0.5%
52 1
0.5%
51 2
0.9%
48 1
0.5%
46 2
0.9%

부상자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2557.6866
Minimum256
Maximum7485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T07:47:05.943934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile380.8
Q1983
median1734
Q34255
95-th percentile5812
Maximum7485
Range7229
Interquartile range (IQR)3272

Descriptive statistics

Standard deviation1883.5148
Coefficient of variation (CV)0.73641343
Kurtosis-0.70054115
Mean2557.6866
Median Absolute Deviation (MAD)983
Skewness0.74013772
Sum555018
Variance3547628
MonotonicityNot monotonic
2023-12-11T07:47:06.099109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1629 2
 
0.9%
1410 2
 
0.9%
1520 2
 
0.9%
1347 2
 
0.9%
2585 1
 
0.5%
2356 1
 
0.5%
826 1
 
0.5%
814 1
 
0.5%
313 1
 
0.5%
1409 1
 
0.5%
Other values (203) 203
93.5%
ValueCountFrequency (%)
256 1
0.5%
283 1
0.5%
290 1
0.5%
308 1
0.5%
313 1
0.5%
316 1
0.5%
321 1
0.5%
345 1
0.5%
354 1
0.5%
376 1
0.5%
ValueCountFrequency (%)
7485 1
0.5%
7148 1
0.5%
7044 1
0.5%
6962 1
0.5%
6667 1
0.5%
6569 1
0.5%
6459 1
0.5%
5942 1
0.5%
5909 1
0.5%
5889 1
0.5%

Interactions

2023-12-11T07:47:04.441484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:03.042682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:03.437923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:04.094187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:04.522242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:03.148835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:03.546176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:04.185546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:04.599571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:03.250663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:03.642223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:04.277824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:04.686597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:03.333098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:03.719721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:47:04.359044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:47:06.218262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도시군명발생건수(건)사망자수(명)부상자수(명)
집계년도1.0000.0000.0000.0000.000
시군명0.0001.0000.9450.8250.945
발생건수(건)0.0000.9451.0000.7980.986
사망자수(명)0.0000.8250.7981.0000.802
부상자수(명)0.0000.9450.9860.8021.000
2023-12-11T07:47:06.341501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
집계년도발생건수(건)사망자수(명)부상자수(명)시군명
집계년도1.0000.022-0.187-0.0070.000
발생건수(건)0.0221.0000.8120.9950.681
사망자수(명)-0.1870.8121.0000.8390.431
부상자수(명)-0.0070.9950.8391.0000.680
시군명0.0000.6810.4310.6801.000

Missing values

2023-12-11T07:47:04.803747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:47:04.889087image/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

집계년도시군명발생건수(건)사망자수(명)부상자수(명)
02022가평군4003675
12022고양시3588375079
22022과천시2084354
32022광명시950101300
42022광주시2178123251
52022구리시71621102
62022군포시5939867
72022김포시1933112926
82022남양주시1970242927
92022동두천시2735376
집계년도시군명발생건수(건)사망자수(명)부상자수(명)
2072016오산시808121239
2082016용인시3051525261
2092016의왕시5274821
2102016의정부시1544172261
2112016이천시1029231617
2122016파주시1596342413
2132016평택시2923554661
2142016포천시853311410
2152016하남시81261217
2162016화성시2511464122