Overview

Dataset statistics

Number of variables8
Number of observations2741
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory187.5 KiB
Average record size in memory70.0 B

Variable types

Categorical1
Text1
Numeric6

Dataset

Description- 시군구별, 월별 교통사고 통계 - 경찰에서 조사, 처리한 교통사고에 대한 통계 정보로 인적 피해가 있는 사고만 집계 됨 - 교통사고분석시스템(http://taas.koroad.or.kr)의 데이터를 바탕으로 함
URLhttps://www.data.go.kr/data/15070295/fileData.do

Alerts

사고건수 is highly overall correlated with 중상자수 and 2 other fieldsHigh correlation
중상자수 is highly overall correlated with 사고건수 and 2 other fieldsHigh correlation
경상자수 is highly overall correlated with 사고건수 and 2 other fieldsHigh correlation
부상신고자수 is highly overall correlated with 사고건수 and 2 other fieldsHigh correlation
사망자수 has 1252 (45.7%) zerosZeros
중상자수 has 40 (1.5%) zerosZeros
부상신고자수 has 682 (24.9%) zerosZeros

Reproduction

Analysis started2023-12-12 08:55:56.221831
Analysis finished2023-12-12 08:56:03.335648
Duration7.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
경기
372 
서울
300 
경북
270 
전남
264 
강원
216 
Other values (12)
1319 

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 (%)
경기 372
13.6%
서울 300
10.9%
경북 270
9.9%
전남 264
9.6%
강원 216
7.9%
경남 216
7.9%
부산 192
7.0%
충남 180
6.6%
전북 168
 
6.1%
충북 132
 
4.8%
Other values (7) 431
15.7%

Length

2023-12-12T17:56:03.447253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 372
13.6%
서울 300
10.9%
경북 270
9.9%
전남 264
9.6%
강원 216
7.9%
경남 216
7.9%
부산 192
7.0%
충남 180
6.6%
전북 168
 
6.1%
충북 132
 
4.8%
Other values (7) 431
15.7%

시군구
Text

Distinct207
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
2023-12-12T17:56:04.009355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9518424
Min length2

Characters and Unicode

Total characters8091
Distinct characters135
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row종로구
2nd row종로구
3rd row종로구
4th row종로구
5th row종로구
ValueCountFrequency (%)
중구 72
 
2.6%
동구 72
 
2.6%
서구 60
 
2.2%
북구 48
 
1.8%
남구 48
 
1.8%
강서구 24
 
0.9%
고성군 24
 
0.9%
담양군 12
 
0.4%
보성군 12
 
0.4%
고흥군 12
 
0.4%
Other values (197) 2357
86.0%
2023-12-12T17:56:04.729934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 1013
 
12.5%
시 948
 
11.7%
구 888
 
11.0%
천 264
 
3.3%
주 240
 
3.0%
양 216
 
2.7%
성 216
 
2.7%
동 204
 
2.5%
산 192
 
2.4%
서 156
 
1.9%
Other values (125) 3754
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8067
99.7%
Close Punctuation 12
 
0.1%
Open Punctuation 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 1013
 
12.6%
시 948
 
11.8%
구 888
 
11.0%
천 264
 
3.3%
주 240
 
3.0%
양 216
 
2.7%
성 216
 
2.7%
동 204
 
2.5%
산 192
 
2.4%
서 156
 
1.9%
Other values (123) 3730
46.2%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8067
99.7%
Common 24
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 1013
 
12.6%
시 948
 
11.8%
구 888
 
11.0%
천 264
 
3.3%
주 240
 
3.0%
양 216
 
2.7%
성 216
 
2.7%
동 204
 
2.5%
산 192
 
2.4%
서 156
 
1.9%
Other values (123) 3730
46.2%
Common
ValueCountFrequency (%)
) 12
50.0%
( 12
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8067
99.7%
ASCII 24
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 1013
 
12.6%
시 948
 
11.8%
구 888
 
11.0%
천 264
 
3.3%
주 240
 
3.0%
양 216
 
2.7%
성 216
 
2.7%
동 204
 
2.5%
산 192
 
2.4%
서 156
 
1.9%
Other values (123) 3730
46.2%
ASCII
ValueCountFrequency (%)
) 12
50.0%
( 12
50.0%

발생월
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5016417
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-12-12T17:56:04.982248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4501266
Coefficient of variation (CV)0.53065467
Kurtosis-1.214655
Mean6.5016417
Median Absolute Deviation (MAD)3
Skewness-0.00080494677
Sum17821
Variance11.903373
MonotonicityNot monotonic
2023-12-12T17:56:05.201393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 229
8.4%
5 229
8.4%
6 229
8.4%
7 229
8.4%
8 229
8.4%
10 229
8.4%
1 228
8.3%
2 228
8.3%
9 228
8.3%
11 228
8.3%
Other values (2) 455
16.6%
ValueCountFrequency (%)
1 228
8.3%
2 228
8.3%
3 227
8.3%
4 229
8.4%
5 229
8.4%
6 229
8.4%
7 229
8.4%
8 229
8.4%
9 228
8.3%
10 229
8.4%
ValueCountFrequency (%)
12 228
8.3%
11 228
8.3%
10 229
8.4%
9 228
8.3%
8 229
8.4%
7 229
8.4%
6 229
8.4%
5 229
8.4%
4 229
8.4%
3 227
8.3%

사고건수
Real number (ℝ)

HIGH CORRELATION 

Distinct313
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.811748
Minimum1
Maximum484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-12-12T17:56:05.465116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q118
median47
Q397
95-th percentile232
Maximum484
Range483
Interquartile range (IQR)79

Descriptive statistics

Standard deviation74.735424
Coefficient of variation (CV)1.0407131
Kurtosis3.7218739
Mean71.811748
Median Absolute Deviation (MAD)34
Skewness1.8476771
Sum196836
Variance5585.3835
MonotonicityNot monotonic
2023-12-12T17:56:05.781600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 62
 
2.3%
9 59
 
2.2%
10 58
 
2.1%
8 53
 
1.9%
16 52
 
1.9%
12 51
 
1.9%
14 50
 
1.8%
19 45
 
1.6%
15 40
 
1.5%
7 38
 
1.4%
Other values (303) 2233
81.5%
ValueCountFrequency (%)
1 15
 
0.5%
2 16
 
0.6%
3 24
0.9%
4 31
1.1%
5 28
1.0%
6 35
1.3%
7 38
1.4%
8 53
1.9%
9 59
2.2%
10 58
2.1%
ValueCountFrequency (%)
484 1
< 0.1%
471 1
< 0.1%
430 1
< 0.1%
416 1
< 0.1%
412 1
< 0.1%
398 1
< 0.1%
393 1
< 0.1%
387 1
< 0.1%
385 1
< 0.1%
384 2
0.1%

사망자수
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99781102
Minimum0
Maximum12
Zeros1252
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-12-12T17:56:05.987888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2869342
Coefficient of variation (CV)1.2897574
Kurtosis6.6466429
Mean0.99781102
Median Absolute Deviation (MAD)1
Skewness2.0068786
Sum2735
Variance1.6561996
MonotonicityNot monotonic
2023-12-12T17:56:06.196455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1252
45.7%
1 792
28.9%
2 392
 
14.3%
3 177
 
6.5%
4 67
 
2.4%
5 30
 
1.1%
6 22
 
0.8%
8 4
 
0.1%
7 2
 
0.1%
12 1
 
< 0.1%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 1252
45.7%
1 792
28.9%
2 392
 
14.3%
3 177
 
6.5%
4 67
 
2.4%
5 30
 
1.1%
6 22
 
0.8%
7 2
 
0.1%
8 4
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
0.1%
7 2
 
0.1%
6 22
 
0.8%
5 30
 
1.1%
4 67
 
2.4%
3 177
6.5%
2 392
14.3%

중상자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.867202
Minimum0
Maximum113
Zeros40
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-12-12T17:56:06.456686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median14
Q324
95-th percentile56
Maximum113
Range113
Interquartile range (IQR)17

Descriptive statistics

Standard deviation17.271635
Coefficient of variation (CV)0.91543171
Kurtosis3.9880797
Mean18.867202
Median Absolute Deviation (MAD)8
Skewness1.8212301
Sum51715
Variance298.30937
MonotonicityNot monotonic
2023-12-12T17:56:06.678533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 117
 
4.3%
4 110
 
4.0%
6 109
 
4.0%
8 107
 
3.9%
5 106
 
3.9%
9 99
 
3.6%
7 98
 
3.6%
11 96
 
3.5%
14 95
 
3.5%
15 92
 
3.4%
Other values (86) 1712
62.5%
ValueCountFrequency (%)
0 40
 
1.5%
1 87
3.2%
2 92
3.4%
3 117
4.3%
4 110
4.0%
5 106
3.9%
6 109
4.0%
7 98
3.6%
8 107
3.9%
9 99
3.6%
ValueCountFrequency (%)
113 1
 
< 0.1%
112 1
 
< 0.1%
106 4
0.1%
102 1
 
< 0.1%
97 1
 
< 0.1%
95 2
0.1%
90 1
 
< 0.1%
89 2
0.1%
87 2
0.1%
86 3
0.1%

경상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct351
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.500912
Minimum0
Maximum572
Zeros12
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-12-12T17:56:06.907360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q118
median50
Q3103
95-th percentile261
Maximum572
Range572
Interquartile range (IQR)85

Descriptive statistics

Standard deviation84.193406
Coefficient of variation (CV)1.0863537
Kurtosis3.8613825
Mean77.500912
Median Absolute Deviation (MAD)37
Skewness1.8967047
Sum212430
Variance7088.5297
MonotonicityNot monotonic
2023-12-12T17:56:07.513751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 60
 
2.2%
9 49
 
1.8%
13 49
 
1.8%
14 47
 
1.7%
5 47
 
1.7%
11 46
 
1.7%
4 44
 
1.6%
6 43
 
1.6%
12 40
 
1.5%
10 40
 
1.5%
Other values (341) 2276
83.0%
ValueCountFrequency (%)
0 12
 
0.4%
1 19
 
0.7%
2 31
1.1%
3 21
 
0.8%
4 44
1.6%
5 47
1.7%
6 43
1.6%
7 36
1.3%
8 60
2.2%
9 49
1.8%
ValueCountFrequency (%)
572 1
< 0.1%
514 1
< 0.1%
463 1
< 0.1%
452 1
< 0.1%
448 1
< 0.1%
447 1
< 0.1%
443 1
< 0.1%
440 1
< 0.1%
434 1
< 0.1%
433 1
< 0.1%

부상신고자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4421744
Minimum0
Maximum79
Zeros682
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2023-12-12T17:56:07.721941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q39
95-th percentile25
Maximum79
Range79
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.2954365
Coefficient of variation (CV)1.4429036
Kurtosis9.4549177
Mean6.4421744
Median Absolute Deviation (MAD)3
Skewness2.6686964
Sum17658
Variance86.40514
MonotonicityNot monotonic
2023-12-12T17:56:07.960501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
24.9%
1 361
13.2%
2 246
 
9.0%
3 184
 
6.7%
4 169
 
6.2%
5 133
 
4.9%
6 118
 
4.3%
9 80
 
2.9%
7 79
 
2.9%
8 71
 
2.6%
Other values (51) 618
22.5%
ValueCountFrequency (%)
0 682
24.9%
1 361
13.2%
2 246
 
9.0%
3 184
 
6.7%
4 169
 
6.2%
5 133
 
4.9%
6 118
 
4.3%
7 79
 
2.9%
8 71
 
2.6%
9 80
 
2.9%
ValueCountFrequency (%)
79 1
 
< 0.1%
78 1
 
< 0.1%
67 1
 
< 0.1%
63 2
0.1%
60 1
 
< 0.1%
57 2
0.1%
54 1
 
< 0.1%
53 3
0.1%
52 1
 
< 0.1%
51 4
0.1%

Interactions

2023-12-12T17:56:02.184109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:56.946751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:58.010681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:58.995502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:00.092055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:01.139519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:02.322243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:57.108975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:58.171211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:59.136322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:00.283853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:01.275161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:02.448150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:57.277661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:58.312677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:59.305942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:00.447267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:01.439711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:02.564590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:57.476053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:58.498419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:59.507307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:00.626314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:01.621716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:02.692391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:57.630174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:58.665357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:59.692831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:00.786554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:01.802727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:02.818980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:57.812309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:58.819448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:55:59.924869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:00.937429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:01.983690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:56:08.115382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도발생월사고건수사망자수중상자수경상자수부상신고자수
시도1.0000.0000.5720.2070.4880.5470.518
발생월0.0001.0000.0000.0530.0530.0000.000
사고건수0.5720.0001.0000.3530.9040.9690.787
사망자수0.2070.0530.3531.0000.4120.3380.380
중상자수0.4880.0530.9040.4121.0000.8730.723
경상자수0.5470.0000.9690.3380.8731.0000.775
부상신고자수0.5180.0000.7870.3800.7230.7751.000
2023-12-12T17:56:08.255683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생월사고건수사망자수중상자수경상자수부상신고자수시도
발생월1.0000.0700.1010.0650.0720.0600.000
사고건수0.0701.0000.3320.9210.9830.8040.264
사망자수0.1010.3321.0000.3290.3140.2340.082
중상자수0.0650.9210.3291.0000.8790.7350.213
경상자수0.0720.9830.3140.8791.0000.7760.247
부상신고자수0.0600.8040.2340.7350.7761.0000.230
시도0.0000.2640.0820.2130.2470.2301.000

Missing values

2023-12-12T17:56:03.026235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:56:03.248150image/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

시도시군구발생월사고건수사망자수중상자수경상자수부상신고자수
0서울종로구1650156113
1서울종로구259015684
2서울종로구370019609
3서울종로구49002110210
4서울종로구5881209812
5서울종로구6781177012
6서울종로구710902510219
7서울종로구8771237012
8서울종로구9990271047
9서울종로구1081013978
시도시군구발생월사고건수사망자수중상자수경상자수부상신고자수
2731세종세종시3630135124
2732세종세종시4784196737
2733세종세종시5851227120
2734세종세종시61033337934
2735세종세종시7781235233
2736세종세종시8773187212
2737세종세종시9871206244
2738세종세종시101052278251
2739세종세종시11880197540
2740세종세종시12781155641