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 노인 교통사고(2018)
Author도로교통공단
URLhttps://www.data.go.kr/data/15094162/fileData.do

Alerts

발생건수 is highly overall correlated with 사망자수 and 4 other fieldsHigh correlation
사망자수 is highly overall correlated with 발생건수 and 2 other fieldsHigh correlation
부상자수 is highly overall correlated with 발생건수 and 4 other fieldsHigh correlation
중상 is highly overall correlated with 발생건수 and 4 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 6 (2.6%) zerosZeros
부상신고 has 32 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-11 23:57:42.713376
Analysis finished2023-12-11 23:57:46.896169
Duration4.18 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

2023-12-12T08:57:46.949416image/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
2023-12-12T08:57:47.228484image/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%
2023-12-12T08:57:47.652143image/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 

Distinct173
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.76419
Minimum5
Maximum785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T08:57:47.797813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile36
Q177
median138
Q3234
95-th percentile416
Maximum785
Range780
Interquartile range (IQR)157

Descriptive statistics

Standard deviation123.56335
Coefficient of variation (CV)0.73216565
Kurtosis3.6390955
Mean168.76419
Median Absolute Deviation (MAD)67
Skewness1.6089293
Sum38647
Variance15267.9
MonotonicityNot monotonic
2023-12-12T08:57:47.933168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 4
 
1.7%
191 3
 
1.3%
277 3
 
1.3%
169 3
 
1.3%
56 3
 
1.3%
90 3
 
1.3%
234 3
 
1.3%
290 3
 
1.3%
77 3
 
1.3%
228 3
 
1.3%
Other values (163) 198
86.5%
ValueCountFrequency (%)
5 1
0.4%
8 1
0.4%
18 1
0.4%
19 1
0.4%
21 1
0.4%
22 1
0.4%
24 1
0.4%
25 1
0.4%
33 1
0.4%
35 2
0.9%
ValueCountFrequency (%)
785 1
0.4%
666 1
0.4%
541 1
0.4%
536 1
0.4%
527 1
0.4%
522 1
0.4%
460 1
0.4%
459 1
0.4%
446 1
0.4%
423 1
0.4%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3449782
Minimum0
Maximum32
Zeros6
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T08:57:48.078974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q39
95-th percentile18
Maximum32
Range32
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.6388948
Coefficient of variation (CV)0.76772111
Kurtosis3.4118812
Mean7.3449782
Median Absolute Deviation (MAD)3
Skewness1.6132996
Sum1682
Variance31.797135
MonotonicityNot monotonic
2023-12-12T08:57:48.213847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4 28
12.2%
3 27
11.8%
6 23
10.0%
5 20
8.7%
7 19
 
8.3%
8 19
 
8.3%
1 17
 
7.4%
10 11
 
4.8%
9 9
 
3.9%
17 7
 
3.1%
Other values (17) 49
21.4%
ValueCountFrequency (%)
0 6
 
2.6%
1 17
7.4%
2 5
 
2.2%
3 27
11.8%
4 28
12.2%
5 20
8.7%
6 23
10.0%
7 19
8.3%
8 19
8.3%
9 9
 
3.9%
ValueCountFrequency (%)
32 1
 
0.4%
31 1
 
0.4%
30 1
 
0.4%
25 1
 
0.4%
23 1
 
0.4%
22 1
 
0.4%
20 2
 
0.9%
19 1
 
0.4%
18 5
2.2%
17 7
3.1%

부상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct166
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.67686
Minimum4
Maximum863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T08:57:48.378564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile37.4
Q184
median150
Q3248
95-th percentile441.8
Maximum863
Range859
Interquartile range (IQR)164

Descriptive statistics

Standard deviation132.7307
Coefficient of variation (CV)0.7265874
Kurtosis3.8102246
Mean182.67686
Median Absolute Deviation (MAD)73
Skewness1.6267881
Sum41833
Variance17617.439
MonotonicityNot monotonic
2023-12-12T08:57:48.549808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136 3
 
1.3%
121 3
 
1.3%
264 3
 
1.3%
76 3
 
1.3%
159 3
 
1.3%
56 3
 
1.3%
72 3
 
1.3%
102 3
 
1.3%
88 3
 
1.3%
37 3
 
1.3%
Other values (156) 199
86.9%
ValueCountFrequency (%)
4 1
 
0.4%
8 1
 
0.4%
18 1
 
0.4%
19 1
 
0.4%
24 2
0.9%
31 1
 
0.4%
34 1
 
0.4%
35 1
 
0.4%
37 3
1.3%
38 1
 
0.4%
ValueCountFrequency (%)
863 1
0.4%
697 1
0.4%
600 1
0.4%
588 1
0.4%
568 1
0.4%
538 1
0.4%
501 1
0.4%
500 1
0.4%
471 1
0.4%
455 1
0.4%

중상
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.567686
Minimum4
Maximum321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T08:57:48.727104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile18
Q141
median65
Q394
95-th percentile165
Maximum321
Range317
Interquartile range (IQR)53

Descriptive statistics

Standard deviation49.858405
Coefficient of variation (CV)0.66863286
Kurtosis3.8002011
Mean74.567686
Median Absolute Deviation (MAD)27
Skewness1.6392454
Sum17076
Variance2485.8605
MonotonicityNot monotonic
2023-12-12T08:57:48.893558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 6
 
2.6%
94 5
 
2.2%
48 5
 
2.2%
52 5
 
2.2%
25 5
 
2.2%
66 5
 
2.2%
51 5
 
2.2%
68 4
 
1.7%
64 4
 
1.7%
92 4
 
1.7%
Other values (103) 181
79.0%
ValueCountFrequency (%)
4 1
 
0.4%
5 1
 
0.4%
6 1
 
0.4%
9 1
 
0.4%
12 1
 
0.4%
15 1
 
0.4%
16 3
1.3%
17 1
 
0.4%
18 4
1.7%
19 1
 
0.4%
ValueCountFrequency (%)
321 1
0.4%
255 2
0.9%
229 1
0.4%
220 1
0.4%
218 1
0.4%
212 1
0.4%
200 1
0.4%
199 1
0.4%
192 1
0.4%
171 1
0.4%

경상
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.371179
Minimum0
Maximum525
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T08:57:49.085432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q140
median77
Q3127
95-th percentile243.2
Maximum525
Range525
Interquartile range (IQR)87

Descriptive statistics

Standard deviation78.288867
Coefficient of variation (CV)0.80402505
Kurtosis4.272068
Mean97.371179
Median Absolute Deviation (MAD)42
Skewness1.6980137
Sum22298
Variance6129.1467
MonotonicityNot monotonic
2023-12-12T08:57:49.303641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 5
 
2.2%
29 5
 
2.2%
35 5
 
2.2%
23 4
 
1.7%
84 4
 
1.7%
57 4
 
1.7%
74 4
 
1.7%
30 4
 
1.7%
53 3
 
1.3%
33 3
 
1.3%
Other values (134) 188
82.1%
ValueCountFrequency (%)
0 1
 
0.4%
2 1
 
0.4%
6 1
 
0.4%
7 1
 
0.4%
10 1
 
0.4%
11 2
0.9%
13 1
 
0.4%
14 1
 
0.4%
15 1
 
0.4%
16 3
1.3%
ValueCountFrequency (%)
525 1
0.4%
382 1
0.4%
354 1
0.4%
327 1
0.4%
295 1
0.4%
287 1
0.4%
278 2
0.9%
267 1
0.4%
261 1
0.4%
258 1
0.4%

부상신고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.737991
Minimum0
Maximum80
Zeros32
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T08:57:49.455642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q315
95-th percentile32
Maximum80
Range80
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.209839
Coefficient of variation (CV)1.1370692
Kurtosis6.2402622
Mean10.737991
Median Absolute Deviation (MAD)5
Skewness2.125328
Sum2459
Variance149.08017
MonotonicityNot monotonic
2023-12-12T08:57:49.971059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 32
 
14.0%
2 18
 
7.9%
5 16
 
7.0%
6 15
 
6.6%
3 13
 
5.7%
4 13
 
5.7%
7 10
 
4.4%
13 10
 
4.4%
1 10
 
4.4%
8 10
 
4.4%
Other values (29) 82
35.8%
ValueCountFrequency (%)
0 32
14.0%
1 10
 
4.4%
2 18
7.9%
3 13
5.7%
4 13
5.7%
5 16
7.0%
6 15
6.6%
7 10
 
4.4%
8 10
 
4.4%
9 4
 
1.7%
ValueCountFrequency (%)
80 1
 
0.4%
60 1
 
0.4%
55 2
 
0.9%
45 1
 
0.4%
44 3
1.3%
40 1
 
0.4%
33 1
 
0.4%
32 6
2.6%
31 1
 
0.4%
30 2
 
0.9%

Interactions

2023-12-12T08:57:46.169424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:43.026097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:43.864612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.396140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.007035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.647299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:46.268404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:43.126137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:43.983233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.505356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.148320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.730085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:46.354422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:43.253297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.070728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.624784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.248863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.823159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:46.430884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:43.351998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.143385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.719659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.359653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.906371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:46.516843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:43.679784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.225167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.820263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.475497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.993701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:46.600281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:43.764066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.300634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:44.916261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:45.557302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:46.070431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:57:50.096963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도발생건수사망자수부상자수중상경상부상신고
시도1.0000.4210.3820.4410.4120.4520.499
발생건수0.4211.0000.8390.9980.9580.9800.885
사망자수0.3820.8391.0000.8230.8010.8100.627
부상자수0.4410.9980.8231.0000.9580.9850.884
중상0.4120.9580.8010.9581.0000.9470.801
경상0.4520.9800.8100.9850.9471.0000.873
부상신고0.4990.8850.6270.8840.8010.8731.000
2023-12-12T08:57:50.207039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생건수사망자수부상자수중상경상부상신고시도
발생건수1.0000.5330.9950.9540.9670.7380.177
사망자수0.5331.0000.5290.5890.4820.2040.186
부상자수0.9950.5291.0000.9520.9750.7430.188
중상0.9540.5890.9521.0000.8770.6540.173
경상0.9670.4820.9750.8771.0000.7010.189
부상신고0.7380.2040.7430.6540.7011.0000.256
시도0.1770.1860.1880.1730.1890.2561.000

Missing values

2023-12-12T08:57:46.735056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:57:46.852651image/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
0서울종로구19112087011325
1서울중구1643166668119
2서울용산구17071775611110
3서울성동구1583161531035
4서울동대문구327534014616925
5서울성북구22892279011918
6서울도봉구1901199829720
7서울은평구244724711211817
8서울서대문구20332098210324
9서울마포구19142016212613
시도시군구발생건수사망자수부상자수중상경상부상신고
219대전중구23442559215112
220대전서구290830810019612
221대전유성구2395275941765
222대전대덕구134613451776
223울산중구1515159588120
224울산남구169817478888
225울산동구7037136350
226울산북구6747632404
227울산울주군1327159558420
228세종세종128813661705