Overview

Dataset statistics

Number of variables18
Number of observations2768
Missing cells30457
Missing cells (%)61.1%
Duplicate rows235
Duplicate rows (%)8.5%
Total size in memory392.1 KiB
Average record size in memory145.0 B

Variable types

Unsupported16
Text1
Categorical1

Dataset

Description2019년 전라북도 전주시 교통사고 현황에 대한 자료를 엑셀파일 형태로 작성되어 있으며 데이터 요청자에 의한 데이터 자료로 상시 제공되는 데이터는 아님
Author소방청
URLhttps://www.data.go.kr/data/15081070/fileData.do

Alerts

Dataset has 235 (8.5%) duplicate rowsDuplicates
Unnamed: 12 is highly imbalanced (50.0%)Imbalance
Unnamed: 0 has 2768 (100.0%) missing valuesMissing
Unnamed: 4 has 2041 (73.7%) missing valuesMissing
Unnamed: 5 has 2197 (79.4%) missing valuesMissing
Unnamed: 6 has 2196 (79.3%) missing valuesMissing
Unnamed: 7 has 2460 (88.9%) missing valuesMissing
Unnamed: 8 has 2285 (82.6%) missing valuesMissing
Unnamed: 9 has 2675 (96.6%) missing valuesMissing
Unnamed: 10 has 2749 (99.3%) missing valuesMissing
Unnamed: 13 has 2242 (81.0%) missing valuesMissing
Unnamed: 14 has 1546 (55.9%) missing valuesMissing
Unnamed: 15 has 1756 (63.4%) missing valuesMissing
Unnamed: 16 has 2764 (99.9%) missing valuesMissing
Unnamed: 17 has 2760 (99.7%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 16 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 17 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 23:55:52.090346
Analysis finished2023-12-11 23:55:53.286759
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2768
Missing (%)100.0%
Memory size24.5 KiB

Unnamed: 1
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)< 0.1%
Memory size21.8 KiB

Unnamed: 2
Unsupported

REJECTED  UNSUPPORTED 

Missing2
Missing (%)0.1%
Memory size21.8 KiB

Unnamed: 3
Text

Distinct2215
Distinct (%)80.1%
Missing2
Missing (%)0.1%
Memory size21.8 KiB
2023-12-12T08:55:53.551664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length52
Mean length34.165221
Min length6

Characters and Unicode

Total characters94501
Distinct characters507
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1909 ?
Unique (%)69.0%

Sample

1st row환자발생위치
2nd row전라북도 전주시완산구 서완산동1가 서완산동1가 52-9 바울교회사거리
3rd row전라북도 전주시완산구 효자동3가 효자동3가 1627-2 경찰청 사거리
4th row전라북도 전주시완산구 효자동3가 효자동3가 1627-2 경찰청 사거리
5th row전라북도 전주시완산구 서서학동 서서학동 295 뚜래쥬루 서학점 앞 도로상
ValueCountFrequency (%)
전라북도 2766
 
15.8%
전주시완산구 1474
 
8.4%
전주시덕진구 1291
 
7.4%
앞 478
 
2.7%
효자동3가 419
 
2.4%
중화산동2가 297
 
1.7%
인후동1가 290
 
1.7%
서신동 267
 
1.5%
효자동1가 266
 
1.5%
도로 257
 
1.5%
Other values (3117) 9698
55.4%
2023-12-12T08:55:54.137650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16359
 
17.3%
전 5936
 
6.3%
동 5816
 
6.2%
가 3813
 
4.0%
1 3748
 
4.0%
도 3508
 
3.7%
주 3045
 
3.2%
북 3031
 
3.2%
구 2854
 
3.0%
시 2834
 
3.0%
Other values (497) 43557
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60712
64.2%
Space Separator 16359
 
17.3%
Decimal Number 15138
 
16.0%
Dash Punctuation 1876
 
2.0%
Other Punctuation 146
 
0.2%
Uppercase Letter 132
 
0.1%
Lowercase Letter 62
 
0.1%
Open Punctuation 34
 
< 0.1%
Close Punctuation 34
 
< 0.1%
Math Symbol 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 5936
 
9.8%
동 5816
 
9.6%
가 3813
 
6.3%
도 3508
 
5.8%
주 3045
 
5.0%
북 3031
 
5.0%
구 2854
 
4.7%
시 2834
 
4.7%
라 2797
 
4.6%
산 2138
 
3.5%
Other values (450) 24940
41.1%
Uppercase Letter
ValueCountFrequency (%)
C 36
27.3%
I 31
23.5%
K 21
15.9%
S 8
 
6.1%
G 8
 
6.1%
B 8
 
6.1%
L 6
 
4.5%
P 3
 
2.3%
Y 2
 
1.5%
J 2
 
1.5%
Other values (7) 7
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
m 24
38.7%
k 13
21.0%
c 8
 
12.9%
i 4
 
6.5%
s 4
 
6.5%
b 2
 
3.2%
t 2
 
3.2%
h 1
 
1.6%
l 1
 
1.6%
y 1
 
1.6%
Other values (2) 2
 
3.2%
Decimal Number
ValueCountFrequency (%)
1 3748
24.8%
2 2730
18.0%
3 1769
11.7%
6 1166
 
7.7%
4 1086
 
7.2%
5 1070
 
7.1%
7 1056
 
7.0%
8 912
 
6.0%
0 824
 
5.4%
9 777
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 141
96.6%
. 5
 
3.4%
Math Symbol
ValueCountFrequency (%)
> 5
62.5%
~ 3
37.5%
Space Separator
ValueCountFrequency (%)
16359
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1876
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60712
64.2%
Common 33595
35.5%
Latin 194
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 5936
 
9.8%
동 5816
 
9.6%
가 3813
 
6.3%
도 3508
 
5.8%
주 3045
 
5.0%
북 3031
 
5.0%
구 2854
 
4.7%
시 2834
 
4.7%
라 2797
 
4.6%
산 2138
 
3.5%
Other values (450) 24940
41.1%
Latin
ValueCountFrequency (%)
C 36
18.6%
I 31
16.0%
m 24
12.4%
K 21
10.8%
k 13
 
6.7%
S 8
 
4.1%
c 8
 
4.1%
G 8
 
4.1%
B 8
 
4.1%
L 6
 
3.1%
Other values (19) 31
16.0%
Common
ValueCountFrequency (%)
16359
48.7%
1 3748
 
11.2%
2 2730
 
8.1%
- 1876
 
5.6%
3 1769
 
5.3%
6 1166
 
3.5%
4 1086
 
3.2%
5 1070
 
3.2%
7 1056
 
3.1%
8 912
 
2.7%
Other values (8) 1823
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60712
64.2%
ASCII 33789
35.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16359
48.4%
1 3748
 
11.1%
2 2730
 
8.1%
- 1876
 
5.6%
3 1769
 
5.2%
6 1166
 
3.5%
4 1086
 
3.2%
5 1070
 
3.2%
7 1056
 
3.1%
8 912
 
2.7%
Other values (37) 2017
 
6.0%
Hangul
ValueCountFrequency (%)
전 5936
 
9.8%
동 5816
 
9.6%
가 3813
 
6.3%
도 3508
 
5.8%
주 3045
 
5.0%
북 3031
 
5.0%
구 2854
 
4.7%
시 2834
 
4.7%
라 2797
 
4.6%
산 2138
 
3.5%
Other values (450) 24940
41.1%

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2041
Missing (%)73.7%
Memory size21.8 KiB

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2197
Missing (%)79.4%
Memory size21.8 KiB

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2196
Missing (%)79.3%
Memory size21.8 KiB

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2460
Missing (%)88.9%
Memory size21.8 KiB

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2285
Missing (%)82.6%
Memory size21.8 KiB

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2675
Missing (%)96.6%
Memory size21.8 KiB

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2749
Missing (%)99.3%
Memory size21.8 KiB

Unnamed: 11
Unsupported

REJECTED  UNSUPPORTED 

Missing13
Missing (%)0.5%
Memory size21.8 KiB

Unnamed: 12
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.8 KiB
남
1649 
여
1111 
<NA>
 
7
성별
 
1

Length

Max length4
Median length1
Mean length1.007948
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row성별
3rd row<NA>
4th row여
5th row남

Common Values

ValueCountFrequency (%)
남 1649
59.6%
여 1111
40.1%
<NA> 7
 
0.3%
성별 1
 
< 0.1%

Length

2023-12-12T08:55:54.306646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:55:54.445408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 1649
59.6%
여 1111
40.1%
na 7
 
0.3%
성별 1
 
< 0.1%

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2242
Missing (%)81.0%
Memory size21.8 KiB

Unnamed: 14
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1546
Missing (%)55.9%
Memory size21.8 KiB

Unnamed: 15
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1756
Missing (%)63.4%
Memory size21.8 KiB

Unnamed: 16
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2764
Missing (%)99.9%
Memory size21.8 KiB

Unnamed: 17
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2760
Missing (%)99.7%
Memory size21.8 KiB

Missing values

2023-12-12T08:55:52.553529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:55:52.789417image/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.
2023-12-12T08:55:53.106107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17
0<NA>공공데이터 자료 요청(전라북도 전주시 교통사고 현황)NaN<NA>NaNNaNNaNNaNNaNNaNNaNNaN<NA>NaNNaNNaN조사기간: 2019. 1.1. ~12. 31.NaN
1<NA>구분신고일시환자발생위치교통사고 사상자NaNNaNNaNNaNNaNNaN연령성별환자분류NaNNaNNaNNaN
2<NA>NaNNaN<NA>운전자동승자보행자자전거오토바이그밖의 탈 것미상NaN<NA>응급준응급잠재응급대상외사망(추정포함)
3<NA>12019-01-01 00:43:00전라북도 전주시완산구 서완산동1가 서완산동1가 52-9 바울교회사거리NaN1NaNNaNNaNNaNNaN43여NaN1NaNNaNNaN
4<NA>22019-01-01 05:26:00전라북도 전주시완산구 효자동3가 효자동3가 1627-2 경찰청 사거리1NaNNaNNaNNaNNaNNaN55남NaN1NaNNaNNaN
5<NA>32019-01-01 05:26:00전라북도 전주시완산구 효자동3가 효자동3가 1627-2 경찰청 사거리NaN1NaNNaNNaNNaNNaN59여NaN1NaNNaNNaN
6<NA>42019-01-01 07:44:00전라북도 전주시완산구 서서학동 서서학동 295 뚜래쥬루 서학점 앞 도로상1NaNNaNNaNNaNNaNNaN52여NaN1NaNNaNNaN
7<NA>52019-01-01 14:43:00전라북도 전주시덕진구 우아동2가 우아동2가 967NaNNaNNaNNaN1NaNNaN46남NaN1NaNNaNNaN
8<NA>62019-01-01 23:27:00전라북도 전주시완산구 효자동2가 효자동2가 734-1 효자4동성당 앞도로1NaNNaNNaNNaNNaNNaN19남NaNNaN1NaNNaN
9<NA>72019-01-02 00:49:00전라북도 전주시덕진구 송천동1가 송천동1가 송호아파트사거리NaNNaN1NaNNaNNaNNaN22남1NaNNaNNaNNaN
Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17
2758<NA>27562019-12-31 12:16:00전라북도 전주시완산구 효자동3가 효자동3가 849 한국농수산대학교 사거리NaN1NaNNaNNaNNaNNaN87남NaN1NaNNaNNaN
2759<NA>27572019-12-31 12:16:00전라북도 전주시완산구 효자동3가 효자동3가 849 농수산대학교 사거리1NaNNaNNaNNaNNaNNaN58여NaN1NaNNaNNaN
2760<NA>27582019-12-31 12:18:00전라북도 전주시완산구 효자동2가 효자동2가 753-34 솔모루,1NaNNaNNaNNaNNaNNaN61여NaN1NaNNaNNaN
2761<NA>27592019-12-31 12:18:00전라북도 전주시완산구 효자동2가 효자동2가 753-34 솔모루,NaN1NaNNaNNaNNaNNaN36여NaNNaN1NaNNaN
2762<NA>27602019-12-31 12:55:00전라북도 전주시완산구 효자동3가 효자동3가 1699-4NaNNaN1NaNNaNNaNNaN25여NaN1NaNNaNNaN
2763<NA>27612019-12-31 17:42:00전라북도 전주시완산구 효자동1가 효자동1가 369-11 탑마트 주차장NaNNaN1NaNNaNNaNNaN60여NaNNaN1NaNNaN
2764<NA>27622019-12-31 17:54:00전라북도 전주시완산구 서신동 서신동 926-8NaNNaNNaNNaN1NaNNaN42남NaNNaN1NaNNaN
2765<NA>27632019-12-31 18:15:00전라북도 전주시완산구 평화동1가 평화동1가 751 뱅뱅매장 앞 도로상 시내버스NaN1NaNNaNNaNNaNNaN57남NaN1NaNNaNNaN
2766<NA>27642019-12-31 19:13:00전라북도 전주시완산구 중화산동2가 중화산동2가 676 종로약국사거리NaNNaNNaNNaN1NaNNaN45남NaN1NaNNaNNaN
2767<NA>27652019-12-31 21:07:00전라북도 전주시덕진구 진북동 진북동 417-35NaNNaNNaNNaN1NaNNaN56남1NaNNaNNaNNaN

Duplicate rows

Most frequently occurring

Unnamed: 3Unnamed: 12# duplicates
160전라북도 전주시완산구 중화산동2가 중화산동2가 676남12
193전라북도 전주시완산구 효자동2가 효자동2가 1267남12
104전라북도 전주시완산구 삼천동1가 삼천동1가 709여9
161전라북도 전주시완산구 중화산동2가 중화산동2가 676여9
18전라북도 전주시덕진구 도도동 도도동 14-65여8
27전라북도 전주시덕진구 산정동 산정동 896남7
71전라북도 전주시덕진구 진북동 진북동 1169여7
158전라북도 전주시완산구 중화산동2가 중화산동2가 560여7
194전라북도 전주시완산구 효자동2가 효자동2가 1267여7
103전라북도 전주시완산구 삼천동1가 삼천동1가 709남6