Overview

Dataset statistics

Number of variables8
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory72.8 B

Variable types

DateTime1
Numeric7

Dataset

Description교통 안전 데이터로서 어린이 등하교 및 초등학교 주변 사각지대, 위험요소 데이터 제공합니다. (출처: 공공데이터포털, https://www.data.go.kr/data/15076627/fileData.do)
Author백수빈
URLhttps://www.jejudatahub.net/data/view/data/875

Alerts

음주운전 is highly overall correlated with 무면허 and 1 other fieldsHigh correlation
무면허 is highly overall correlated with 음주운전 and 1 other fieldsHigh correlation
신호위반 is highly overall correlated with 중앙선침범 and 1 other fieldsHigh correlation
안전띠 is highly overall correlated with 음주운전 and 1 other fieldsHigh correlation
중앙선침범 is highly overall correlated with 신호위반 and 1 other fieldsHigh correlation
기타 is highly overall correlated with 신호위반 and 1 other fieldsHigh correlation
연도별 has unique valuesUnique
신호위반 has unique valuesUnique
과속 has unique valuesUnique
안전띠 has 1 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-11 20:04:57.369001
Analysis finished2023-12-11 20:05:03.651799
Duration6.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도별
Date

UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
Minimum2015-01-01 00:00:00
Maximum2020-12-01 00:00:00
2023-12-12T05:05:03.736398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:03.916040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

음주운전
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.375
Minimum129
Maximum699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T05:05:04.432531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile149.1
Q1220.25
median323.5
Q3440.25
95-th percentile592.05
Maximum699
Range570
Interquartile range (IQR)220

Descriptive statistics

Standard deviation142.41285
Coefficient of variation (CV)0.42718516
Kurtosis-0.33674205
Mean333.375
Median Absolute Deviation (MAD)110
Skewness0.62862185
Sum24003
Variance20281.421
MonotonicityNot monotonic
2023-12-12T05:05:04.632870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171 2
 
2.8%
347 2
 
2.8%
187 2
 
2.8%
457 2
 
2.8%
257 2
 
2.8%
240 2
 
2.8%
444 2
 
2.8%
179 1
 
1.4%
150 1
 
1.4%
142 1
 
1.4%
Other values (55) 55
76.4%
ValueCountFrequency (%)
129 1
1.4%
142 1
1.4%
147 1
1.4%
148 1
1.4%
150 1
1.4%
164 1
1.4%
165 1
1.4%
171 2
2.8%
178 1
1.4%
179 1
1.4%
ValueCountFrequency (%)
699 1
1.4%
662 1
1.4%
658 1
1.4%
619 1
1.4%
570 1
1.4%
555 1
1.4%
546 1
1.4%
537 1
1.4%
535 1
1.4%
483 1
1.4%

무면허
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.29167
Minimum41
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T05:05:04.814555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile49.65
Q166.5
median94
Q3144.25
95-th percentile197
Maximum265
Range224
Interquartile range (IQR)77.75

Descriptive statistics

Standard deviation51.361584
Coefficient of variation (CV)0.47428935
Kurtosis0.25782912
Mean108.29167
Median Absolute Deviation (MAD)34.5
Skewness0.89606743
Sum7797
Variance2638.0123
MonotonicityNot monotonic
2023-12-12T05:05:04.973295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 3
 
4.2%
76 3
 
4.2%
46 2
 
2.8%
60 2
 
2.8%
63 2
 
2.8%
83 2
 
2.8%
197 2
 
2.8%
80 2
 
2.8%
97 2
 
2.8%
57 2
 
2.8%
Other values (43) 50
69.4%
ValueCountFrequency (%)
41 1
1.4%
46 2
2.8%
48 1
1.4%
51 1
1.4%
52 1
1.4%
54 1
1.4%
55 1
1.4%
57 2
2.8%
58 1
1.4%
59 1
1.4%
ValueCountFrequency (%)
265 1
1.4%
242 1
1.4%
216 1
1.4%
197 2
2.8%
186 2
2.8%
179 1
1.4%
177 1
1.4%
175 1
1.4%
171 1
1.4%
158 1
1.4%

신호위반
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2167.6389
Minimum913
Maximum4543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T05:05:05.157137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum913
5-th percentile1013.2
Q11583.5
median2088.5
Q32670.25
95-th percentile3734.65
Maximum4543
Range3630
Interquartile range (IQR)1086.75

Descriptive statistics

Standard deviation821.95043
Coefficient of variation (CV)0.37919159
Kurtosis0.5741287
Mean2167.6389
Median Absolute Deviation (MAD)541
Skewness0.82792637
Sum156070
Variance675602.52
MonotonicityNot monotonic
2023-12-12T05:05:05.356809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2702 1
 
1.4%
1520 1
 
1.4%
2007 1
 
1.4%
1587 1
 
1.4%
1691 1
 
1.4%
1209 1
 
1.4%
936 1
 
1.4%
913 1
 
1.4%
1286 1
 
1.4%
1423 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
913 1
1.4%
936 1
1.4%
961 1
1.4%
1000 1
1.4%
1024 1
1.4%
1093 1
1.4%
1209 1
1.4%
1269 1
1.4%
1286 1
1.4%
1302 1
1.4%
ValueCountFrequency (%)
4543 1
1.4%
4492 1
1.4%
4009 1
1.4%
3744 1
1.4%
3727 1
1.4%
3464 1
1.4%
3297 1
1.4%
3186 1
1.4%
3144 1
1.4%
3122 1
1.4%

과속
Real number (ℝ)

UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9249.6528
Minimum3124
Maximum25244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T05:05:05.577194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3124
5-th percentile4607.7
Q16035.75
median7527.5
Q310362
95-th percentile21095.4
Maximum25244
Range22120
Interquartile range (IQR)4326.25

Descriptive statistics

Standard deviation5215.1222
Coefficient of variation (CV)0.56381816
Kurtosis1.9190555
Mean9249.6528
Median Absolute Deviation (MAD)1720
Skewness1.6444763
Sum665975
Variance27197500
MonotonicityNot monotonic
2023-12-12T05:05:05.766968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8372 1
 
1.4%
4850 1
 
1.4%
8794 1
 
1.4%
8238 1
 
1.4%
7005 1
 
1.4%
6615 1
 
1.4%
6096 1
 
1.4%
5542 1
 
1.4%
6059 1
 
1.4%
8100 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
3124 1
1.4%
3516 1
1.4%
3613 1
1.4%
4446 1
1.4%
4740 1
1.4%
4850 1
1.4%
4853 1
1.4%
4893 1
1.4%
5027 1
1.4%
5186 1
1.4%
ValueCountFrequency (%)
25244 1
1.4%
24341 1
1.4%
23192 1
1.4%
21542 1
1.4%
20730 1
1.4%
19802 1
1.4%
19370 1
1.4%
19246 1
1.4%
16345 1
1.4%
16268 1
1.4%

안전띠
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410.73611
Minimum0
Maximum3428
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T05:05:05.955941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q121.75
median158.5
Q3361.5
95-th percentile2104.65
Maximum3428
Range3428
Interquartile range (IQR)339.75

Descriptive statistics

Standard deviation670.79366
Coefficient of variation (CV)1.63315
Kurtosis6.5837655
Mean410.73611
Median Absolute Deviation (MAD)150.5
Skewness2.514557
Sum29573
Variance449964.14
MonotonicityNot monotonic
2023-12-12T05:05:06.197303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3
 
4.2%
2 3
 
4.2%
11 2
 
2.8%
175 2
 
2.8%
1 2
 
2.8%
30 2
 
2.8%
303 1
 
1.4%
199 1
 
1.4%
162 1
 
1.4%
232 1
 
1.4%
Other values (54) 54
75.0%
ValueCountFrequency (%)
0 1
 
1.4%
1 2
2.8%
2 3
4.2%
3 1
 
1.4%
4 1
 
1.4%
5 3
4.2%
6 1
 
1.4%
7 1
 
1.4%
9 1
 
1.4%
11 2
2.8%
ValueCountFrequency (%)
3428 1
1.4%
2295 1
1.4%
2233 1
1.4%
2180 1
1.4%
2043 1
1.4%
1579 1
1.4%
1459 1
1.4%
1224 1
1.4%
1195 1
1.4%
1032 1
1.4%

중앙선침범
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.68056
Minimum150
Maximum790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T05:05:06.415008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile194.5
Q1252.5
median293
Q3375.75
95-th percentile654.65
Maximum790
Range640
Interquartile range (IQR)123.25

Descriptive statistics

Standard deviation144.88051
Coefficient of variation (CV)0.4227859
Kurtosis2.0427571
Mean342.68056
Median Absolute Deviation (MAD)53.5
Skewness1.5380058
Sum24673
Variance20990.361
MonotonicityNot monotonic
2023-12-12T05:05:06.564862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
335 3
 
4.2%
290 2
 
2.8%
447 2
 
2.8%
337 2
 
2.8%
292 2
 
2.8%
287 2
 
2.8%
568 2
 
2.8%
318 1
 
1.4%
222 1
 
1.4%
225 1
 
1.4%
Other values (54) 54
75.0%
ValueCountFrequency (%)
150 1
1.4%
160 1
1.4%
171 1
1.4%
189 1
1.4%
199 1
1.4%
204 1
1.4%
212 1
1.4%
219 1
1.4%
221 1
1.4%
222 1
1.4%
ValueCountFrequency (%)
790 1
1.4%
777 1
1.4%
762 1
1.4%
697 1
1.4%
620 1
1.4%
604 1
1.4%
568 2
2.8%
524 1
1.4%
521 1
1.4%
510 1
1.4%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2339.5556
Minimum954
Maximum4043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T05:05:06.723320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum954
5-th percentile1357.4
Q11789.25
median2376
Q32692.5
95-th percentile3661.6
Maximum4043
Range3089
Interquartile range (IQR)903.25

Descriptive statistics

Standard deviation679.9746
Coefficient of variation (CV)0.29064264
Kurtosis0.13590592
Mean2339.5556
Median Absolute Deviation (MAD)387
Skewness0.42980662
Sum168448
Variance462365.46
MonotonicityNot monotonic
2023-12-12T05:05:06.864640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2443 2
 
2.8%
1254 1
 
1.4%
2282 1
 
1.4%
2297 1
 
1.4%
1760 1
 
1.4%
1610 1
 
1.4%
1466 1
 
1.4%
1517 1
 
1.4%
1916 1
 
1.4%
1594 1
 
1.4%
Other values (61) 61
84.7%
ValueCountFrequency (%)
954 1
1.4%
1068 1
1.4%
1216 1
1.4%
1254 1
1.4%
1442 1
1.4%
1466 1
1.4%
1517 1
1.4%
1537 1
1.4%
1594 1
1.4%
1610 1
1.4%
ValueCountFrequency (%)
4043 1
1.4%
3922 1
1.4%
3889 1
1.4%
3688 1
1.4%
3640 1
1.4%
3601 1
1.4%
3432 1
1.4%
3090 1
1.4%
3052 1
1.4%
3009 1
1.4%

Interactions

2023-12-12T05:05:02.507621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.659556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.452147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.191938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.980287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.878052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.720358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.616487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.755273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.567340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.297819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.089289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.998218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.834783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.717427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.862032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.659239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.410263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.189619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.117458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.939654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.856593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.979640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.801683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.551475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.311904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.239983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.073072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.974740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.083099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.894654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.659871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.470670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.337551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.172301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:03.099156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.231201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.984526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.757745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.616743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.466093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.293625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:03.229583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.331453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.082691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.863500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.758440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.594044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.403916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:05:06.972432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별음주운전무면허신호위반과속안전띠중앙선침범기타
연도별1.0001.0001.0001.0001.0001.0001.0001.000
음주운전1.0001.0000.9090.4990.4650.5060.6270.481
무면허1.0000.9091.0000.6710.5650.7550.8520.454
신호위반1.0000.4990.6711.0000.5740.4260.8060.654
과속1.0000.4650.5650.5741.0000.5110.6760.365
안전띠1.0000.5060.7550.4260.5111.0000.7500.203
중앙선침범1.0000.6270.8520.8060.6760.7501.0000.715
기타1.0000.4810.4540.6540.3650.2030.7151.000
2023-12-12T05:05:07.099045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
음주운전무면허신호위반과속안전띠중앙선침범기타
음주운전1.0000.8600.1190.2380.6540.2960.017
무면허0.8601.0000.2180.1220.7610.2990.142
신호위반0.1190.2181.0000.2150.3930.7060.639
과속0.2380.1220.2151.0000.1110.4630.126
안전띠0.6540.7610.3930.1111.0000.4240.217
중앙선침범0.2960.2990.7060.4630.4241.0000.612
기타0.0170.1420.6390.1260.2170.6121.000

Missing values

2023-12-12T05:05:03.394227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:05:03.584244image/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

연도별음주운전무면허신호위반과속안전띠중앙선침범기타
02015-01-0134194270283723932901068
12015-02-0127076248363176962791254
22015-03-013611342724679314593352452
32015-04-013521492555567122953052602
42015-05-013381403297502734282732724
52015-06-01242141288347404613352594
62015-07-0125112125574446762762735
72015-08-01421186247374692873122765
82015-09-01450144154182401142292727
92015-10-01483146175065413322752615
연도별음주운전무면허신호위반과속안전띠중앙선침범기타
622020-03-01171541602807612422348
632020-04-011474631441980222612443
642020-05-0116448400916345213612971
652020-06-011875244921559225213688
662020-07-012144145431071315103640
672020-08-01185573464596604833601
682020-09-0119080266431243354473922
692020-10-01240972922351615795684043
702020-11-01187582689105995045683889
712020-12-01129632250106181933873090