Overview

Dataset statistics

Number of variables8
Number of observations228
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.7 KiB
Average record size in memory70.6 B

Variable types

Categorical2
Text1
Numeric5

Dataset

Description* 부문별 어린이 교통사고(2018)
Author도로교통공단
URLhttps://www.data.go.kr/data/15094169/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 is highly imbalanced (59.9%)Imbalance
중상 has 16 (7.0%) zerosZeros
부상신고 has 66 (28.9%) zerosZeros

Reproduction

Analysis started2023-12-12 02:28:38.376104
Analysis finished2023-12-12 02:28:41.609805
Duration3.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기
31 
서울
25 
경북
23 
전남
22 
강원
18 
Other values (12)
109 

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
13.6%
서울 25
11.0%
경북 23
10.1%
전남 22
9.6%
강원 18
7.9%
경남 17
7.5%
부산 16
7.0%
충남 15
6.6%
전북 14
 
6.1%
충북 11
 
4.8%
Other values (7) 36
15.8%

Length

2023-12-12T11:28:41.669982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 31
13.6%
서울 25
11.0%
경북 23
10.1%
전남 22
9.6%
강원 18
7.9%
경남 17
7.5%
부산 16
7.0%
충남 15
6.6%
전북 14
 
6.1%
충북 11
 
4.8%
Other values (7) 36
15.8%

시군구
Text

Distinct205
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T11:28:41.985902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9385965
Min length2

Characters and Unicode

Total characters670
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

Unique198 ?
Unique (%)86.8%

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
 
1.8%
강서구 2
 
0.9%
고성군 2
 
0.9%
곡성군 1
 
0.4%
화순군 1
 
0.4%
보성군 1
 
0.4%
Other values (195) 195
85.5%
2023-12-12T11:28:42.513528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 84
 
12.5%
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%
남 13
 
1.9%
Other values (123) 310
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 668
99.7%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 84
 
12.6%
시 78
 
11.7%
구 74
 
11.1%
천 22
 
3.3%
주 20
 
3.0%
양 18
 
2.7%
성 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
남 13
 
1.9%
Other values (121) 308
46.1%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 668
99.7%
Common 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 84
 
12.6%
시 78
 
11.7%
구 74
 
11.1%
천 22
 
3.3%
주 20
 
3.0%
양 18
 
2.7%
성 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
남 13
 
1.9%
Other values (121) 308
46.1%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 668
99.7%
ASCII 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 84
 
12.6%
시 78
 
11.7%
구 74
 
11.1%
천 22
 
3.3%
주 20
 
3.0%
양 18
 
2.7%
성 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
남 13
 
1.9%
Other values (121) 308
46.1%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

발생건수
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.899123
Minimum1
Maximum266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T11:28:42.668809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median28.5
Q358.5
95-th percentile150.75
Maximum266
Range265
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation46.76206
Coefficient of variation (CV)1.0652163
Kurtosis4.0170525
Mean43.899123
Median Absolute Deviation (MAD)19.5
Skewness1.9270699
Sum10009
Variance2186.6902
MonotonicityNot monotonic
2023-12-12T11:28:42.841127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 10
 
4.4%
9 9
 
3.9%
5 8
 
3.5%
7 7
 
3.1%
6 6
 
2.6%
3 6
 
2.6%
47 6
 
2.6%
21 6
 
2.6%
29 5
 
2.2%
13 5
 
2.2%
Other values (85) 160
70.2%
ValueCountFrequency (%)
1 1
 
0.4%
2 3
 
1.3%
3 6
2.6%
4 10
4.4%
5 8
3.5%
6 6
2.6%
7 7
3.1%
8 3
 
1.3%
9 9
3.9%
10 2
 
0.9%
ValueCountFrequency (%)
266 1
0.4%
217 1
0.4%
200 1
0.4%
192 1
0.4%
188 2
0.9%
181 2
0.9%
168 1
0.4%
162 1
0.4%
160 1
0.4%
156 1
0.4%

사망자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0
198 
1
26 
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 198
86.8%
1 26
 
11.4%
2 4
 
1.8%

Length

2023-12-12T11:28:43.004329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:28:43.103914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 198
86.8%
1 26
 
11.4%
2 4
 
1.8%

부상자수
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.013158
Minimum1
Maximum356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T11:28:43.241156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q114.75
median35
Q373
95-th percentile180.65
Maximum356
Range355
Interquartile range (IQR)58.25

Descriptive statistics

Standard deviation59.317965
Coefficient of variation (CV)1.0782505
Kurtosis4.7095154
Mean55.013158
Median Absolute Deviation (MAD)24.5
Skewness2.0312527
Sum12543
Variance3518.621
MonotonicityNot monotonic
2023-12-12T11:28:43.420080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 9
 
3.9%
5 8
 
3.5%
6 7
 
3.1%
25 6
 
2.6%
15 6
 
2.6%
79 5
 
2.2%
11 5
 
2.2%
4 5
 
2.2%
9 4
 
1.8%
8 4
 
1.8%
Other values (98) 169
74.1%
ValueCountFrequency (%)
1 1
 
0.4%
2 3
 
1.3%
3 1
 
0.4%
4 5
2.2%
5 8
3.5%
6 7
3.1%
7 4
1.8%
8 4
1.8%
9 4
1.8%
10 9
3.9%
ValueCountFrequency (%)
356 1
0.4%
268 1
0.4%
262 1
0.4%
252 1
0.4%
240 1
0.4%
235 1
0.4%
234 1
0.4%
220 1
0.4%
214 1
0.4%
200 2
0.9%

중상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5701754
Minimum0
Maximum49
Zeros16
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T11:28:43.599749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q310
95-th percentile24.65
Maximum49
Range49
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.2006439
Coefficient of variation (CV)1.0832832
Kurtosis5.1248402
Mean7.5701754
Median Absolute Deviation (MAD)3
Skewness2.0817404
Sum1726
Variance67.25056
MonotonicityNot monotonic
2023-12-12T11:28:43.806191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 29
12.7%
2 22
 
9.6%
3 20
 
8.8%
5 19
 
8.3%
4 16
 
7.0%
0 16
 
7.0%
6 15
 
6.6%
8 13
 
5.7%
7 11
 
4.8%
9 9
 
3.9%
Other values (22) 58
25.4%
ValueCountFrequency (%)
0 16
7.0%
1 29
12.7%
2 22
9.6%
3 20
8.8%
4 16
7.0%
5 19
8.3%
6 15
6.6%
7 11
 
4.8%
8 13
5.7%
9 9
 
3.9%
ValueCountFrequency (%)
49 1
 
0.4%
40 2
0.9%
35 1
 
0.4%
33 1
 
0.4%
30 1
 
0.4%
29 4
1.8%
28 1
 
0.4%
25 1
 
0.4%
24 2
0.9%
23 1
 
0.4%

경상
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.092105
Minimum0
Maximum295
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T11:28:43.958483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19.75
median25
Q351.25
95-th percentile142.6
Maximum295
Range295
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation43.874323
Coefficient of variation (CV)1.0943382
Kurtosis6.0253356
Mean40.092105
Median Absolute Deviation (MAD)17
Skewness2.1478582
Sum9141
Variance1924.9562
MonotonicityNot monotonic
2023-12-12T11:28:44.126580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 12
 
5.3%
7 9
 
3.9%
9 9
 
3.9%
17 8
 
3.5%
3 8
 
3.5%
15 6
 
2.6%
16 6
 
2.6%
8 6
 
2.6%
24 6
 
2.6%
33 5
 
2.2%
Other values (86) 153
67.1%
ValueCountFrequency (%)
0 2
 
0.9%
1 2
 
0.9%
2 2
 
0.9%
3 8
3.5%
4 12
5.3%
5 4
 
1.8%
6 3
 
1.3%
7 9
3.9%
8 6
2.6%
9 9
3.9%
ValueCountFrequency (%)
295 1
0.4%
195 1
0.4%
180 1
0.4%
171 1
0.4%
161 1
0.4%
160 1
0.4%
159 1
0.4%
153 1
0.4%
150 1
0.4%
145 2
0.9%

부상신고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3508772
Minimum0
Maximum62
Zeros66
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-12T11:28:44.315244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q39.25
95-th percentile33.3
Maximum62
Range62
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation11.128787
Coefficient of variation (CV)1.5139401
Kurtosis7.0723518
Mean7.3508772
Median Absolute Deviation (MAD)3
Skewness2.5221099
Sum1676
Variance123.84991
MonotonicityNot monotonic
2023-12-12T11:28:44.454095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 66
28.9%
1 23
 
10.1%
2 15
 
6.6%
5 14
 
6.1%
3 13
 
5.7%
4 12
 
5.3%
8 9
 
3.9%
7 8
 
3.5%
6 8
 
3.5%
11 7
 
3.1%
Other values (28) 53
23.2%
ValueCountFrequency (%)
0 66
28.9%
1 23
 
10.1%
2 15
 
6.6%
3 13
 
5.7%
4 12
 
5.3%
5 14
 
6.1%
6 8
 
3.5%
7 8
 
3.5%
8 9
 
3.9%
9 3
 
1.3%
ValueCountFrequency (%)
62 1
 
0.4%
59 1
 
0.4%
57 1
 
0.4%
45 1
 
0.4%
44 1
 
0.4%
43 1
 
0.4%
41 2
0.9%
35 3
1.3%
34 1
 
0.4%
32 1
 
0.4%

Interactions

2023-12-12T11:28:40.845132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:38.715436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.256040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.779373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.359156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.938669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:38.816666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.364813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.875846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.446792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:41.065043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:38.921193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.476027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.007878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.557937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:41.194087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.026154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.570156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.113173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.659518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:41.289010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.149512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:39.666980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.225079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:28:40.756422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:28:44.541657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도발생건수사망자수부상자수중상경상부상신고
시도1.0000.4480.2100.4720.3680.4000.577
발생건수0.4481.0000.4130.9460.9380.9200.702
사망자수0.2100.4131.0000.5650.3720.3950.426
부상자수0.4720.9460.5651.0000.8560.9430.849
중상0.3680.9380.3720.8561.0000.8290.572
경상0.4000.9200.3950.9430.8291.0000.687
부상신고0.5770.7020.4260.8490.5720.6871.000
2023-12-12T11:28:44.648551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도사망자수
시도1.0000.109
사망자수0.1091.000
2023-12-12T11:28:44.766527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생건수부상자수중상경상부상신고시도사망자수
발생건수1.0000.9900.8760.9750.7950.1890.275
부상자수0.9901.0000.8600.9870.8070.2050.295
중상0.8760.8601.0000.8150.6130.1490.237
경상0.9750.9870.8151.0000.7450.1750.271
부상신고0.7950.8070.6130.7451.0000.2660.212
시도0.1890.2050.1490.1750.2661.0000.109
사망자수0.2750.2950.2370.2710.2120.1091.000

Missing values

2023-12-12T11:28:41.421920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:28:41.561094image/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
0서울종로구280331275
1서울중구230251168
2서울용산구340396258
3서울성동구280314207
4서울동대문구4204633310
5서울성북구6408216579
6서울도봉구340382297
7서울은평구5608455425
8서울서대문구320367227
9서울마포구450507376
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218대전중구470607458
219대전서구1171142181177
220대전유성구960133139525
221대전대덕구250345245
222울산중구2602710161
223울산남구430499364
224울산동구210216150
225울산북구290306240
226울산울주군35051112911
227세종세종4906118394