Overview

Dataset statistics

Number of variables8
Number of observations229
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.8 KiB
Average record size in memory70.6 B

Variable types

Categorical1
Text1
Numeric6

Dataset

Description* 부문별 노인운전자 교통사고(2018)
Author도로교통공단
URLhttps://www.data.go.kr/data/15094163/fileData.do

Alerts

발생건수 is highly overall correlated with 부상자수 and 3 other fieldsHigh correlation
부상자수 is highly overall correlated with 발생건수 and 3 other fieldsHigh correlation
중상 is highly overall correlated with 발생건수 and 3 other fieldsHigh correlation
경상 is highly overall correlated with 발생건수 and 3 other fieldsHigh correlation
부상신고 is highly overall correlated with 발생건수 and 3 other fieldsHigh correlation
사망자수 has 20 (8.7%) zerosZeros
부상신고 has 30 (13.1%) zerosZeros

Reproduction

Analysis started2023-12-12 11:47:22.840725
Analysis finished2023-12-12 11:47:28.178977
Duration5.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기
31 
서울
25 
경북
23 
전남
22 
강원
18 
Other values (12)
110 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기 31
13.5%
서울 25
10.9%
경북 23
10.0%
전남 22
9.6%
강원 18
7.9%
경남 18
7.9%
부산 16
7.0%
충남 15
6.6%
전북 14
 
6.1%
충북 11
 
4.8%
Other values (7) 36
15.7%

Length

2023-12-12T20:47:28.273903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 31
13.5%
서울 25
10.9%
경북 23
10.0%
전남 22
9.6%
강원 18
7.9%
경남 18
7.9%
부산 16
7.0%
충남 15
6.6%
전북 14
 
6.1%
충북 11
 
4.8%
Other values (7) 36
15.7%

시군구
Text

Distinct206
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T20:47:28.653905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9388646
Min length2

Characters and Unicode

Total characters673
Distinct characters133
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

Unique199 ?
Unique (%)86.9%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row동대문구
ValueCountFrequency (%)
중구 6
 
2.6%
동구 6
 
2.6%
서구 5
 
2.2%
남구 5
 
2.2%
북구 4
 
1.7%
강서구 2
 
0.9%
고성군 2
 
0.9%
곡성군 1
 
0.4%
화순군 1
 
0.4%
보성군 1
 
0.4%
Other values (196) 196
85.6%
2023-12-12T20:47:29.200677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 85
 
12.6%
시 78
 
11.6%
구 74
 
11.0%
천 22
 
3.3%
주 20
 
3.0%
양 18
 
2.7%
성 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (123) 312
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 671
99.7%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 85
 
12.7%
시 78
 
11.6%
구 74
 
11.0%
천 22
 
3.3%
주 20
 
3.0%
양 18
 
2.7%
성 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (121) 310
46.2%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 671
99.7%
Common 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 85
 
12.7%
시 78
 
11.6%
구 74
 
11.0%
천 22
 
3.3%
주 20
 
3.0%
양 18
 
2.7%
성 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (121) 310
46.2%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 671
99.7%
ASCII 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 85
 
12.7%
시 78
 
11.6%
구 74
 
11.0%
천 22
 
3.3%
주 20
 
3.0%
양 18
 
2.7%
성 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 13
 
1.9%
Other values (121) 310
46.2%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

발생건수
Real number (ℝ)

HIGH CORRELATION 

Distinct162
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.05677
Minimum3
Maximum625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T20:47:29.373272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile21.6
Q156
median100
Q3185
95-th percentile344.6
Maximum625
Range622
Interquartile range (IQR)129

Descriptive statistics

Standard deviation103.01827
Coefficient of variation (CV)0.78605838
Kurtosis2.8471318
Mean131.05677
Median Absolute Deviation (MAD)55
Skewness1.5077165
Sum30012
Variance10612.764
MonotonicityNot monotonic
2023-12-12T20:47:29.503513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 6
 
2.6%
31 5
 
2.2%
45 4
 
1.7%
41 4
 
1.7%
86 3
 
1.3%
106 3
 
1.3%
19 3
 
1.3%
93 3
 
1.3%
30 3
 
1.3%
87 2
 
0.9%
Other values (152) 193
84.3%
ValueCountFrequency (%)
3 1
 
0.4%
5 1
 
0.4%
10 1
 
0.4%
12 2
0.9%
13 1
 
0.4%
18 2
0.9%
19 3
1.3%
20 1
 
0.4%
24 1
 
0.4%
26 1
 
0.4%
ValueCountFrequency (%)
625 1
0.4%
504 1
0.4%
457 1
0.4%
431 1
0.4%
421 1
0.4%
402 1
0.4%
384 1
0.4%
371 1
0.4%
363 1
0.4%
354 1
0.4%

사망자수
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6812227
Minimum0
Maximum17
Zeros20
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T20:47:29.612396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9.6
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2006916
Coefficient of variation (CV)0.86946426
Kurtosis2.4880574
Mean3.6812227
Median Absolute Deviation (MAD)2
Skewness1.4398387
Sum843
Variance10.244427
MonotonicityNot monotonic
2023-12-12T20:47:29.739975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 48
21.0%
2 34
14.8%
3 32
14.0%
5 26
11.4%
4 22
9.6%
0 20
8.7%
8 10
 
4.4%
7 9
 
3.9%
6 8
 
3.5%
9 8
 
3.5%
Other values (6) 12
 
5.2%
ValueCountFrequency (%)
0 20
8.7%
1 48
21.0%
2 34
14.8%
3 32
14.0%
4 22
9.6%
5 26
11.4%
6 8
 
3.5%
7 9
 
3.9%
8 10
 
4.4%
9 8
 
3.5%
ValueCountFrequency (%)
17 1
 
0.4%
16 1
 
0.4%
15 2
 
0.9%
13 1
 
0.4%
11 4
 
1.7%
10 3
 
1.3%
9 8
3.5%
8 10
4.4%
7 9
3.9%
6 8
3.5%

부상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.82096
Minimum5
Maximum1031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T20:47:29.909861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile31
Q181
median140
Q3267
95-th percentile493.6
Maximum1031
Range1026
Interquartile range (IQR)186

Descriptive statistics

Standard deviation152.95667
Coefficient of variation (CV)0.80579444
Kurtosis4.2753409
Mean189.82096
Median Absolute Deviation (MAD)76
Skewness1.6853803
Sum43469
Variance23395.744
MonotonicityNot monotonic
2023-12-12T20:47:30.077121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117 4
 
1.7%
89 4
 
1.7%
93 3
 
1.3%
180 3
 
1.3%
88 3
 
1.3%
70 3
 
1.3%
326 3
 
1.3%
96 3
 
1.3%
176 2
 
0.9%
87 2
 
0.9%
Other values (172) 199
86.9%
ValueCountFrequency (%)
5 1
0.4%
7 1
0.4%
15 2
0.9%
20 2
0.9%
24 1
0.4%
26 2
0.9%
27 1
0.4%
29 1
0.4%
31 2
0.9%
33 1
0.4%
ValueCountFrequency (%)
1031 1
0.4%
742 1
0.4%
650 1
0.4%
628 1
0.4%
618 1
0.4%
556 1
0.4%
540 1
0.4%
531 1
0.4%
526 1
0.4%
515 1
0.4%

중상
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.353712
Minimum1
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T20:47:30.249336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q127
median41
Q362
95-th percentile108.2
Maximum230
Range229
Interquartile range (IQR)35

Descriptive statistics

Standard deviation32.141979
Coefficient of variation (CV)0.66472619
Kurtosis4.759134
Mean48.353712
Median Absolute Deviation (MAD)16
Skewness1.6385555
Sum11073
Variance1033.1068
MonotonicityNot monotonic
2023-12-12T20:47:30.417075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 7
 
3.1%
32 7
 
3.1%
21 6
 
2.6%
34 6
 
2.6%
41 6
 
2.6%
37 5
 
2.2%
49 5
 
2.2%
43 5
 
2.2%
36 5
 
2.2%
53 5
 
2.2%
Other values (86) 172
75.1%
ValueCountFrequency (%)
1 2
0.9%
3 1
 
0.4%
4 1
 
0.4%
5 1
 
0.4%
6 1
 
0.4%
7 3
1.3%
9 1
 
0.4%
10 1
 
0.4%
12 4
1.7%
13 2
0.9%
ValueCountFrequency (%)
230 1
0.4%
158 1
0.4%
152 2
0.9%
130 1
0.4%
125 1
0.4%
124 1
0.4%
118 1
0.4%
115 1
0.4%
113 1
0.4%
111 1
0.4%

경상
Real number (ℝ)

HIGH CORRELATION 

Distinct156
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.75109
Minimum2
Maximum774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T20:47:30.625333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16.4
Q150
median90
Q3187
95-th percentile350.8
Maximum774
Range772
Interquartile range (IQR)137

Descriptive statistics

Standard deviation114.48138
Coefficient of variation (CV)0.88231534
Kurtosis4.5999599
Mean129.75109
Median Absolute Deviation (MAD)58
Skewness1.7488427
Sum29713
Variance13105.986
MonotonicityNot monotonic
2023-12-12T20:47:30.831736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 7
 
3.1%
31 4
 
1.7%
101 4
 
1.7%
50 4
 
1.7%
63 4
 
1.7%
25 3
 
1.3%
98 3
 
1.3%
38 3
 
1.3%
186 3
 
1.3%
21 3
 
1.3%
Other values (146) 191
83.4%
ValueCountFrequency (%)
2 1
0.4%
5 1
0.4%
8 1
0.4%
10 2
0.9%
12 1
0.4%
13 2
0.9%
15 2
0.9%
16 2
0.9%
17 2
0.9%
19 1
0.4%
ValueCountFrequency (%)
774 1
0.4%
545 1
0.4%
469 1
0.4%
463 1
0.4%
423 1
0.4%
410 1
0.4%
403 1
0.4%
386 1
0.4%
384 1
0.4%
362 1
0.4%

부상신고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.716157
Minimum0
Maximum73
Zeros30
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T20:47:31.023958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q317
95-th percentile37.6
Maximum73
Range73
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.464468
Coefficient of variation (CV)1.1492222
Kurtosis3.8075466
Mean11.716157
Median Absolute Deviation (MAD)6
Skewness1.8160002
Sum2683
Variance181.29189
MonotonicityNot monotonic
2023-12-12T20:47:31.227426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 30
 
13.1%
1 18
 
7.9%
3 17
 
7.4%
2 15
 
6.6%
4 13
 
5.7%
6 11
 
4.8%
12 9
 
3.9%
9 9
 
3.9%
7 8
 
3.5%
8 8
 
3.5%
Other values (36) 91
39.7%
ValueCountFrequency (%)
0 30
13.1%
1 18
7.9%
2 15
6.6%
3 17
7.4%
4 13
5.7%
5 7
 
3.1%
6 11
 
4.8%
7 8
 
3.5%
8 8
 
3.5%
9 9
 
3.9%
ValueCountFrequency (%)
73 1
0.4%
72 1
0.4%
57 1
0.4%
55 1
0.4%
51 1
0.4%
49 1
0.4%
46 2
0.9%
44 1
0.4%
43 1
0.4%
39 1
0.4%

Interactions

2023-12-12T20:47:27.066036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:23.198639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:23.799949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.388664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:25.491790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:26.178783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:27.200398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:23.296223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:23.897808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.480098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:25.599532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:26.320377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:27.338161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:23.400585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.004798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.626956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:25.739614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:26.486256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:27.484114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:23.511109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.094285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.751875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:25.853728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:26.629465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:27.611723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:23.593717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.196511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.856377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:25.941042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:26.781999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:27.753820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:23.708001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.292940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:24.998461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:26.055732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:47:26.941488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:47:31.342342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도발생건수사망자수부상자수중상경상부상신고
시도1.0000.4320.3770.4300.4570.4040.625
발생건수0.4321.0000.7710.9480.8440.9220.725
사망자수0.3770.7711.0000.5730.6450.5270.223
부상자수0.4300.9480.5731.0000.8320.9930.839
중상0.4570.8440.6450.8321.0000.8330.610
경상0.4040.9220.5270.9930.8331.0000.831
부상신고0.6250.7250.2230.8390.6100.8311.000
2023-12-12T20:47:31.469908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생건수사망자수부상자수중상경상부상신고시도
발생건수1.0000.2610.9920.9100.9800.7600.180
사망자수0.2611.0000.2750.3720.2520.0400.153
부상자수0.9920.2751.0000.9090.9890.7660.182
중상0.9100.3720.9091.0000.8550.6610.206
경상0.9800.2520.9890.8551.0000.7280.169
부상신고0.7600.0400.7660.6610.7281.0000.300
시도0.1800.1530.1820.2060.1690.3001.000

Missing values

2023-12-12T20:47:27.920403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:47:28.103310image/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

시도시군구발생건수사망자수부상자수중상경상부상신고
0서울종로구20502956619138
1서울중구20832766518328
2서울용산구2311297812097
3서울성동구1541223491668
4서울동대문구26713349021727
5서울성북구24343789325926
6서울도봉구14721803612024
7서울은평구19322515717816
8서울서대문구17712285515617
9서울마포구24613666927522
시도시군구발생건수사망자수부상자수중상경상부상신고
219대전중구17122544420010
220대전서구23213265625614
221대전유성구1982314492569
222대전대덕구8529826666
223울산중구1141138418017
224울산남구1332185741056
225울산동구5105825321
226울산북구6418935513
227울산울주군864140379310
228세종세종90711135751