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 이륜차 교통사고(2018)
Author도로교통공단
URLhttps://www.data.go.kr/data/15094171/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 56 (24.5%) zerosZeros
중상 has 4 (1.7%) zerosZeros
경상 has 6 (2.6%) zerosZeros
부상신고 has 42 (18.3%) zerosZeros

Reproduction

Analysis started2023-12-12 08:58:44.904130
Analysis finished2023-12-12 08:58:49.365945
Duration4.46 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

2023-12-12T17:58:49.439776image/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
2023-12-12T17:58:49.760783image/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%
2023-12-12T17:58:50.236909image/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 

Distinct125
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.641921
Minimum1
Maximum329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T17:58:50.466388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median45
Q393
95-th percentile201.4
Maximum329
Range328
Interquartile range (IQR)77

Descriptive statistics

Standard deviation65.670384
Coefficient of variation (CV)1.0004336
Kurtosis2.5762989
Mean65.641921
Median Absolute Deviation (MAD)32
Skewness1.611056
Sum15032
Variance4312.5993
MonotonicityNot monotonic
2023-12-12T17:58:50.631748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 9
 
3.9%
11 7
 
3.1%
13 6
 
2.6%
27 5
 
2.2%
15 5
 
2.2%
34 4
 
1.7%
1 4
 
1.7%
20 4
 
1.7%
60 4
 
1.7%
12 4
 
1.7%
Other values (115) 177
77.3%
ValueCountFrequency (%)
1 4
1.7%
2 3
 
1.3%
3 3
 
1.3%
4 1
 
0.4%
5 2
 
0.9%
6 3
 
1.3%
7 3
 
1.3%
8 1
 
0.4%
9 3
 
1.3%
10 9
3.9%
ValueCountFrequency (%)
329 1
0.4%
328 1
0.4%
282 1
0.4%
281 1
0.4%
246 1
0.4%
244 1
0.4%
233 1
0.4%
231 1
0.4%
228 1
0.4%
224 1
0.4%

사망자수
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.790393
Minimum0
Maximum13
Zeros56
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T17:58:50.780954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0043143
Coefficient of variation (CV)1.1194829
Kurtosis8.4568361
Mean1.790393
Median Absolute Deviation (MAD)1
Skewness2.4277835
Sum410
Variance4.0172757
MonotonicityNot monotonic
2023-12-12T17:58:50.936131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 73
31.9%
0 56
24.5%
2 46
20.1%
3 24
 
10.5%
4 12
 
5.2%
5 8
 
3.5%
6 4
 
1.7%
11 2
 
0.9%
13 1
 
0.4%
10 1
 
0.4%
Other values (2) 2
 
0.9%
ValueCountFrequency (%)
0 56
24.5%
1 73
31.9%
2 46
20.1%
3 24
 
10.5%
4 12
 
5.2%
5 8
 
3.5%
6 4
 
1.7%
7 1
 
0.4%
9 1
 
0.4%
10 1
 
0.4%
ValueCountFrequency (%)
13 1
 
0.4%
11 2
 
0.9%
10 1
 
0.4%
9 1
 
0.4%
7 1
 
0.4%
6 4
 
1.7%
5 8
 
3.5%
4 12
 
5.2%
3 24
10.5%
2 46
20.1%

부상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.31441
Minimum0
Maximum397
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T17:58:51.088253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q119
median51
Q3117
95-th percentile258.6
Maximum397
Range397
Interquartile range (IQR)98

Descriptive statistics

Standard deviation83.191107
Coefficient of variation (CV)1.0230795
Kurtosis2.1191159
Mean81.31441
Median Absolute Deviation (MAD)38
Skewness1.5437767
Sum18621
Variance6920.7604
MonotonicityNot monotonic
2023-12-12T17:58:51.280998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 6
 
2.6%
17 6
 
2.6%
13 6
 
2.6%
19 5
 
2.2%
21 4
 
1.7%
1 4
 
1.7%
5 4
 
1.7%
12 4
 
1.7%
14 4
 
1.7%
34 4
 
1.7%
Other values (126) 182
79.5%
ValueCountFrequency (%)
0 1
 
0.4%
1 4
1.7%
2 2
0.9%
3 2
0.9%
4 1
 
0.4%
5 4
1.7%
6 1
 
0.4%
7 2
0.9%
8 3
1.3%
9 3
1.3%
ValueCountFrequency (%)
397 1
0.4%
386 1
0.4%
360 1
0.4%
344 1
0.4%
314 1
0.4%
310 1
0.4%
304 1
0.4%
301 1
0.4%
291 1
0.4%
280 1
0.4%

중상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.620087
Minimum0
Maximum115
Zeros4
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T17:58:51.434004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median17
Q332
95-th percentile64.6
Maximum115
Range115
Interquartile range (IQR)25

Descriptive statistics

Standard deviation20.467341
Coefficient of variation (CV)0.90483033
Kurtosis2.515856
Mean22.620087
Median Absolute Deviation (MAD)12
Skewness1.4981394
Sum5180
Variance418.91205
MonotonicityNot monotonic
2023-12-12T17:58:51.599872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 12
 
5.2%
7 9
 
3.9%
6 9
 
3.9%
5 9
 
3.9%
10 9
 
3.9%
29 8
 
3.5%
3 7
 
3.1%
19 7
 
3.1%
14 7
 
3.1%
9 7
 
3.1%
Other values (53) 145
63.3%
ValueCountFrequency (%)
0 4
 
1.7%
1 6
2.6%
2 6
2.6%
3 7
3.1%
4 12
5.2%
5 9
3.9%
6 9
3.9%
7 9
3.9%
8 5
2.2%
9 7
3.1%
ValueCountFrequency (%)
115 1
0.4%
95 1
0.4%
87 1
0.4%
85 1
0.4%
79 2
0.9%
76 1
0.4%
74 1
0.4%
73 1
0.4%
71 2
0.9%
67 1
0.4%

경상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct103
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.650655
Minimum0
Maximum231
Zeros6
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T17:58:51.773489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median28
Q364
95-th percentile157
Maximum231
Range231
Interquartile range (IQR)54

Descriptive statistics

Standard deviation49.400088
Coefficient of variation (CV)1.0821332
Kurtosis2.7081058
Mean45.650655
Median Absolute Deviation (MAD)22
Skewness1.6813796
Sum10454
Variance2440.3687
MonotonicityNot monotonic
2023-12-12T17:58:52.297356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10
 
4.4%
3 9
 
3.9%
4 8
 
3.5%
12 7
 
3.1%
8 7
 
3.1%
25 6
 
2.6%
0 6
 
2.6%
13 5
 
2.2%
5 5
 
2.2%
1 5
 
2.2%
Other values (93) 161
70.3%
ValueCountFrequency (%)
0 6
2.6%
1 5
2.2%
2 3
 
1.3%
3 9
3.9%
4 8
3.5%
5 5
2.2%
6 4
1.7%
7 4
1.7%
8 7
3.1%
9 4
1.7%
ValueCountFrequency (%)
231 1
0.4%
229 1
0.4%
228 1
0.4%
207 1
0.4%
201 1
0.4%
192 1
0.4%
179 1
0.4%
175 1
0.4%
173 1
0.4%
166 1
0.4%

부상신고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.043668
Minimum0
Maximum109
Zeros42
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T17:58:52.473413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q318
95-th percentile48
Maximum109
Range109
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.357269
Coefficient of variation (CV)1.4073701
Kurtosis6.5104425
Mean13.043668
Median Absolute Deviation (MAD)5
Skewness2.2851343
Sum2987
Variance336.98931
MonotonicityNot monotonic
2023-12-12T17:58:52.669451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
18.3%
1 33
14.4%
4 17
 
7.4%
3 10
 
4.4%
6 10
 
4.4%
5 9
 
3.9%
8 9
 
3.9%
2 8
 
3.5%
12 5
 
2.2%
10 5
 
2.2%
Other values (41) 81
35.4%
ValueCountFrequency (%)
0 42
18.3%
1 33
14.4%
2 8
 
3.5%
3 10
 
4.4%
4 17
7.4%
5 9
 
3.9%
6 10
 
4.4%
7 3
 
1.3%
8 9
 
3.9%
9 1
 
0.4%
ValueCountFrequency (%)
109 1
0.4%
104 1
0.4%
88 1
0.4%
65 2
0.9%
63 1
0.4%
60 1
0.4%
58 2
0.9%
56 1
0.4%
53 1
0.4%
48 2
0.9%

Interactions

2023-12-12T17:58:48.465818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:45.270736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:45.836045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.504299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.142063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.853301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.556496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:45.370993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:45.941596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.613701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.254814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.975074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.645279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:45.470445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.037048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.724160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.371819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.061686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.783079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:45.562623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.155512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.841423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.501917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.173722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.871821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:45.651592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.310859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.939687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.602505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.288426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.959052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:45.735879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:46.412099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.033372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:47.721242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:48.372100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:58:52.773295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도발생건수사망자수부상자수중상경상부상신고
시도1.0000.5060.1270.4900.4030.4880.538
발생건수0.5061.0000.7570.9850.9480.9460.755
사망자수0.1270.7571.0000.7090.8380.7320.409
부상자수0.4900.9850.7091.0000.9280.9610.767
중상0.4030.9480.8380.9281.0000.9220.678
경상0.4880.9460.7320.9610.9221.0000.664
부상신고0.5380.7550.4090.7670.6780.6641.000
2023-12-12T17:58:52.930396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생건수사망자수부상자수중상경상부상신고시도
발생건수1.0000.3850.9960.9590.9760.8650.219
사망자수0.3851.0000.3610.3950.3590.2360.045
부상자수0.9960.3611.0000.9560.9830.8650.210
중상0.9590.3950.9561.0000.9130.7900.169
경상0.9760.3590.9830.9131.0000.8050.209
부상신고0.8650.2360.8650.7900.8051.0000.253
시도0.2190.0450.2100.1690.2090.2531.000

Missing values

2023-12-12T17:58:49.121572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:58:49.300949image/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
0서울종로구931114285234
1서울중구72187333816
2서울용산구1033118386614
3서울성동구1263150409317
4서울동대문구19812296113731
5서울성북구16431965010442
6서울도봉구79089263924
7서울은평구961126236736
8서울서대문구1041121216337
9서울마포구992129327819
시도시군구발생건수사망자수부상자수중상경상부상신고
219대전중구2914310285
220대전서구92311825858
221대전유성구6819826693
222대전대덕구270357217
223울산중구820110293942
224울산남구77290265410
225울산동구112313649861
226울산북구60278164319
227울산울주군5326229276
228세종세종6347529415