Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory664.1 KiB
Average record size in memory68.0 B

Variable types

Categorical4
DateTime1
Numeric2

Dataset

Description*최근3년 특별시, 광역시 시도시군구별 일별 시간별 교통사고 현황(2017~2019년)
Author도로교통공단
URLhttps://www.data.go.kr/data/15094148/fileData.do

Alerts

발생지_시도 has constant value ""Constant
사고건수 is highly imbalanced (71.4%)Imbalance
사망자수 is highly imbalanced (95.7%)Imbalance
발생시간 has 374 (3.7%) zerosZeros

Reproduction

Analysis started2023-12-12 14:26:01.756327
Analysis finished2023-12-12 14:26:02.696672
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발생지_시도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울
10000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 10000
100.0%

Length

2023-12-12T23:26:02.760731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:26:02.856108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 10000
100.0%

발생지_시군구
Categorical

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강남구
814 
송파구
691 
영등포구
 
663
서초구
 
641
강서구
 
442
Other values (19)
6749 

Length

Max length4
Median length3
Mean length3.104
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용산구
2nd row도봉구
3rd row강동구
4th row구로구
5th row양천구

Common Values

ValueCountFrequency (%)
강남구 814
 
8.1%
송파구 691
 
6.9%
영등포구 663
 
6.6%
서초구 641
 
6.4%
강서구 442
 
4.4%
동대문구 431
 
4.3%
구로구 424
 
4.2%
마포구 424
 
4.2%
양천구 412
 
4.1%
노원구 409
 
4.1%
Other values (14) 4649
46.5%

Length

2023-12-12T23:26:02.950255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 814
 
8.1%
송파구 691
 
6.9%
영등포구 663
 
6.6%
서초구 641
 
6.4%
강서구 442
 
4.4%
동대문구 431
 
4.3%
구로구 424
 
4.2%
마포구 424
 
4.2%
양천구 412
 
4.1%
노원구 409
 
4.1%
Other values (14) 4649
46.5%

발생일
Date

Distinct1095
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-01-01 00:00:00
Maximum2019-12-31 00:00:00
2023-12-12T23:26:03.088141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:26:03.246211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

발생시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.9993
Minimum0
Maximum23
Zeros374
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:26:03.360115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median14
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.4939138
Coefficient of variation (CV)0.49955873
Kurtosis-0.86966032
Mean12.9993
Median Absolute Deviation (MAD)5
Skewness-0.35851255
Sum129993
Variance42.170917
MonotonicityNot monotonic
2023-12-12T23:26:03.816293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18 587
 
5.9%
19 572
 
5.7%
17 553
 
5.5%
16 530
 
5.3%
20 518
 
5.2%
13 514
 
5.1%
15 506
 
5.1%
14 495
 
5.0%
8 494
 
4.9%
22 476
 
4.8%
Other values (14) 4755
47.5%
ValueCountFrequency (%)
0 374
3.7%
1 312
3.1%
2 238
2.4%
3 192
 
1.9%
4 207
2.1%
5 234
2.3%
6 244
2.4%
7 302
3.0%
8 494
4.9%
9 461
4.6%
ValueCountFrequency (%)
23 395
4.0%
22 476
4.8%
21 474
4.7%
20 518
5.2%
19 572
5.7%
18 587
5.9%
17 553
5.5%
16 530
5.3%
15 506
5.1%
14 495
5.0%

사고건수
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8862 
2
1022 
3
 
101
4
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8862
88.6%
2 1022
 
10.2%
3 101
 
1.0%
4 15
 
0.1%

Length

2023-12-12T23:26:03.939951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:26:04.037094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8862
88.6%
2 1022
 
10.2%
3 101
 
1.0%
4 15
 
0.1%

사망자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9919 
1
 
80
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9919
99.2%
1 80
 
0.8%
2 1
 
< 0.1%

Length

2023-12-12T23:26:04.167709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:26:04.277574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9919
99.2%
1 80
 
0.8%
2 1
 
< 0.1%

부상자수
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5722
Minimum0
Maximum51
Zeros43
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:26:04.405787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum51
Range51
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.332278
Coefficient of variation (CV)0.84739727
Kurtosis415.97182
Mean1.5722
Median Absolute Deviation (MAD)0
Skewness13.28298
Sum15722
Variance1.7749647
MonotonicityNot monotonic
2023-12-12T23:26:04.535788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 6671
66.7%
2 2041
 
20.4%
3 684
 
6.8%
4 295
 
2.9%
5 136
 
1.4%
6 55
 
0.5%
0 43
 
0.4%
7 34
 
0.3%
8 18
 
0.2%
10 8
 
0.1%
Other values (6) 15
 
0.1%
ValueCountFrequency (%)
0 43
 
0.4%
1 6671
66.7%
2 2041
 
20.4%
3 684
 
6.8%
4 295
 
2.9%
5 136
 
1.4%
6 55
 
0.5%
7 34
 
0.3%
8 18
 
0.2%
9 6
 
0.1%
ValueCountFrequency (%)
51 2
 
< 0.1%
33 1
 
< 0.1%
15 2
 
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%
10 8
 
0.1%
9 6
 
0.1%
8 18
 
0.2%
7 34
0.3%
6 55
0.5%

Interactions

2023-12-12T23:26:02.329633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:26:02.154279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:26:02.414488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:26:02.232435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:26:04.624665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생지_시군구발생시간사고건수사망자수부상자수
발생지_시군구1.0000.0660.1560.0000.004
발생시간0.0661.0000.0500.0290.024
사고건수0.1560.0501.0000.0790.125
사망자수0.0000.0290.0791.0000.000
부상자수0.0040.0240.1250.0001.000
2023-12-12T23:26:04.749018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생지_시군구사고건수사망자수
발생지_시군구1.0000.0750.000
사고건수0.0751.0000.075
사망자수0.0000.0751.000
2023-12-12T23:26:04.839426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생시간부상자수발생지_시군구사고건수사망자수
발생시간1.000-0.0150.0240.0300.017
부상자수-0.0151.0000.0020.1020.000
발생지_시군구0.0240.0021.0000.0750.000
사고건수0.0300.1020.0751.0000.075
사망자수0.0170.0000.0000.0751.000

Missing values

2023-12-12T23:26:02.528637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:26:02.643128image/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

발생지_시도발생지_시군구발생일발생시간사고건수사망자수부상자수
7077서울용산구2017-07-1212101
23118서울도봉구2018-10-0214101
67661서울강동구2018-12-187101
42723서울구로구2019-09-2616101
82797서울양천구2017-06-2415101
57060서울강남구2017-02-0615101
27252서울서대문구2017-02-029202
26893서울은평구2019-10-061101
94965서울광진구2017-04-1823101
34669서울강서구2017-01-126101
발생지_시도발생지_시군구발생일발생시간사고건수사망자수부상자수
23744서울도봉구2019-07-309101
80867서울서초구2019-05-1512101
18482서울성북구2017-08-2617202
35519서울강서구2017-08-0917101
11576서울성동구2018-07-0814101
80376서울서초구2019-02-0821103
66933서울강동구2018-06-0314103
52249서울동작구2019-04-265101
41277서울구로구2018-09-097101
76400서울서초구2017-03-0416103