Overview

Dataset statistics

Number of variables3
Number of observations132
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory27.0 B

Variable types

Text1
Numeric2

Dataset

Description통계기준일,사고 건수(공사장,교통,화재),사망자수 (사망자+부상자)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21078/S/1/datasetView.do

Alerts

사고 건수(공사장,교통,화재) is highly overall correlated with 사망자수 (사망자+부상자)High correlation
사망자수 (사망자+부상자) is highly overall correlated with 사고 건수(공사장,교통,화재)High correlation
통계기준일 has unique valuesUnique

Reproduction

Analysis started2024-05-10 23:33:55.100160
Analysis finished2024-05-10 23:33:57.061285
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계기준일
Text

UNIQUE 

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T23:33:57.423438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters2244
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)100.0%

Sample

1st row20240311~20240317
2nd row20240304~20240310
3rd row20240226~20240303
4th row20240219~20240225
5th row20240212~20240218
ValueCountFrequency (%)
20240311~20240317 1
 
0.8%
20220711~20220717 1
 
0.8%
20220321~20220327 1
 
0.8%
20220328~20220403 1
 
0.8%
20220404~20220410 1
 
0.8%
20220411~20220417 1
 
0.8%
20220418~20220424 1
 
0.8%
20220425~20220501 1
 
0.8%
20220502~20220508 1
 
0.8%
20220509~20220515 1
 
0.8%
Other values (122) 122
92.4%
2024-05-10T23:33:58.583557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 781
34.8%
0 586
26.1%
1 292
 
13.0%
3 157
 
7.0%
~ 132
 
5.9%
4 64
 
2.9%
8 52
 
2.3%
9 48
 
2.1%
7 47
 
2.1%
6 44
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2112
94.1%
Math Symbol 132
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 781
37.0%
0 586
27.7%
1 292
 
13.8%
3 157
 
7.4%
4 64
 
3.0%
8 52
 
2.5%
9 48
 
2.3%
7 47
 
2.2%
6 44
 
2.1%
5 41
 
1.9%
Math Symbol
ValueCountFrequency (%)
~ 132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 781
34.8%
0 586
26.1%
1 292
 
13.0%
3 157
 
7.0%
~ 132
 
5.9%
4 64
 
2.9%
8 52
 
2.3%
9 48
 
2.1%
7 47
 
2.1%
6 44
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 781
34.8%
0 586
26.1%
1 292
 
13.0%
3 157
 
7.0%
~ 132
 
5.9%
4 64
 
2.9%
8 52
 
2.3%
9 48
 
2.1%
7 47
 
2.1%
6 44
 
2.0%

사고 건수(공사장,교통,화재)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean477.88636
Minimum18
Maximum671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T23:33:59.202166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile364.65
Q1433
median490.5
Q3520
95-th percentile573.35
Maximum671
Range653
Interquartile range (IQR)87

Descriptive statistics

Standard deviation76.565558
Coefficient of variation (CV)0.16021708
Kurtosis9.1995783
Mean477.88636
Median Absolute Deviation (MAD)40.5
Skewness-1.5418779
Sum63081
Variance5862.2847
MonotonicityNot monotonic
2024-05-10T23:34:00.099604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 4
 
3.0%
525 3
 
2.3%
488 3
 
2.3%
480 3
 
2.3%
520 3
 
2.3%
514 2
 
1.5%
433 2
 
1.5%
447 2
 
1.5%
407 2
 
1.5%
511 2
 
1.5%
Other values (87) 106
80.3%
ValueCountFrequency (%)
18 1
0.8%
311 1
0.8%
338 1
0.8%
339 1
0.8%
353 1
0.8%
359 1
0.8%
363 1
0.8%
366 1
0.8%
374 1
0.8%
380 1
0.8%
ValueCountFrequency (%)
671 1
0.8%
658 1
0.8%
645 1
0.8%
625 1
0.8%
620 1
0.8%
601 1
0.8%
575 1
0.8%
572 1
0.8%
570 1
0.8%
566 1
0.8%

사망자수 (사망자+부상자)
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean602.9697
Minimum43
Maximum892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T23:34:00.552268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile471.1
Q1545.75
median615
Q3653.25
95-th percentile727.35
Maximum892
Range849
Interquartile range (IQR)107.5

Descriptive statistics

Standard deviation94.341506
Coefficient of variation (CV)0.15646144
Kurtosis8.8470388
Mean602.9697
Median Absolute Deviation (MAD)49.5
Skewness-1.4089541
Sum79592
Variance8900.3197
MonotonicityNot monotonic
2024-05-10T23:34:00.967244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
644 3
 
2.3%
593 3
 
2.3%
498 2
 
1.5%
629 2
 
1.5%
601 2
 
1.5%
619 2
 
1.5%
543 2
 
1.5%
676 2
 
1.5%
626 2
 
1.5%
704 2
 
1.5%
Other values (96) 110
83.3%
ValueCountFrequency (%)
43 1
0.8%
422 1
0.8%
431 1
0.8%
440 1
0.8%
458 2
1.5%
459 1
0.8%
481 1
0.8%
483 1
0.8%
488 1
0.8%
489 2
1.5%
ValueCountFrequency (%)
892 1
0.8%
807 1
0.8%
804 1
0.8%
766 1
0.8%
750 1
0.8%
737 1
0.8%
729 1
0.8%
726 1
0.8%
709 1
0.8%
708 1
0.8%

Interactions

2024-05-10T23:33:55.952399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:33:55.262252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:33:56.389750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:33:55.546466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:34:01.271942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고 건수(공사장,교통,화재)사망자수 (사망자+부상자)
사고 건수(공사장,교통,화재)1.0000.952
사망자수 (사망자+부상자)0.9521.000
2024-05-10T23:34:01.561057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사고 건수(공사장,교통,화재)사망자수 (사망자+부상자)
사고 건수(공사장,교통,화재)1.0000.970
사망자수 (사망자+부상자)0.9701.000

Missing values

2024-05-10T23:33:56.693333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:33:56.955553image/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

통계기준일사고 건수(공사장,교통,화재)사망자수 (사망자+부상자)
020240311~20240317388498
120240304~20240310409489
220240226~20240303339459
320240219~20240225398494
420240212~20240218338422
520240205~20240211388491
620240129~20240204421524
720240122~20240128493610
820240115~20240121429532
920240108~20240114374458
통계기준일사고 건수(공사장,교통,화재)사망자수 (사망자+부상자)
12220210927~20211003537704
12320210920~20210926311511
12420210913~20210919658804
12520210906~20210912570709
12620210830~20210905503619
12720210823~20210829522638
12820210816~20210822473585
12920210809~20210815529674
13020210802~20210808518644
13120210726~20210801549662