Overview

Dataset statistics

Number of variables14
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory127.7 B

Variable types

Categorical2
Numeric12

Dataset

Description- 2019년(1월~12월)월별, 시간대별 음주운전 교통사고 현황(사고건수, 사망자수, 부상자수)에 대한 데이터
Author도로교통공단
URLhttps://www.data.go.kr/data/15087979/fileData.do

Alerts

1 is highly overall correlated with 2 and 10 other fieldsHigh correlation
2 is highly overall correlated with 1 and 11 other fieldsHigh correlation
3 is highly overall correlated with 1 and 10 other fieldsHigh correlation
4 is highly overall correlated with 1 and 11 other fieldsHigh correlation
5 is highly overall correlated with 1 and 11 other fieldsHigh correlation
6 is highly overall correlated with 1 and 11 other fieldsHigh correlation
7 is highly overall correlated with 1 and 11 other fieldsHigh correlation
8 is highly overall correlated with 1 and 11 other fieldsHigh correlation
9 is highly overall correlated with 1 and 11 other fieldsHigh correlation
10 is highly overall correlated with 1 and 11 other fieldsHigh correlation
11 is highly overall correlated with 1 and 11 other fieldsHigh correlation
12 is highly overall correlated with 1 and 11 other fieldsHigh correlation
발생월 is highly overall correlated with 2 and 9 other fieldsHigh correlation
1 has 3 (8.3%) zerosZeros
2 has 3 (8.3%) zerosZeros
3 has 4 (11.1%) zerosZeros
4 has 4 (11.1%) zerosZeros
5 has 1 (2.8%) zerosZeros
6 has 4 (11.1%) zerosZeros
7 has 3 (8.3%) zerosZeros
8 has 3 (8.3%) zerosZeros
9 has 2 (5.6%) zerosZeros
10 has 1 (2.8%) zerosZeros
11 has 1 (2.8%) zerosZeros
12 has 3 (8.3%) zerosZeros

Reproduction

Analysis started2023-12-12 18:18:23.892579
Analysis finished2023-12-12 18:18:38.193623
Duration14.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발생시간대
Categorical

Distinct12
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
00시-02시
02시-04시
04시-06시
06시-08시
08시-10시
Other values (7)
21 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00시-02시
2nd row00시-02시
3rd row00시-02시
4th row02시-04시
5th row02시-04시

Common Values

ValueCountFrequency (%)
00시-02시 3
8.3%
02시-04시 3
8.3%
04시-06시 3
8.3%
06시-08시 3
8.3%
08시-10시 3
8.3%
10시-12시 3
8.3%
12시-14시 3
8.3%
14시-16시 3
8.3%
16시-18시 3
8.3%
18시-20시 3
8.3%
Other values (2) 6
16.7%

Length

2023-12-13T03:18:38.291955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00시-02시 3
8.3%
02시-04시 3
8.3%
04시-06시 3
8.3%
06시-08시 3
8.3%
08시-10시 3
8.3%
10시-12시 3
8.3%
12시-14시 3
8.3%
14시-16시 3
8.3%
16시-18시 3
8.3%
18시-20시 3
8.3%
Other values (2) 6
16.7%

발생월
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
사고건수
12 
사망자수
12 
부상자수
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사고건수
2nd row사망자수
3rd row부상자수
4th row사고건수
5th row사망자수

Common Values

ValueCountFrequency (%)
사고건수 12
33.3%
사망자수 12
33.3%
부상자수 12
33.3%

Length

2023-12-13T03:18:38.440505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:18:38.590635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사고건수 12
33.3%
사망자수 12
33.3%
부상자수 12
33.3%

1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.166667
Minimum0
Maximum376
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:39.095564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.75
median53.5
Q3109.5
95-th percentile253.25
Maximum376
Range376
Interquartile range (IQR)106.75

Descriptive statistics

Standard deviation93.544031
Coefficient of variation (CV)1.1668694
Kurtosis2.4055754
Mean80.166667
Median Absolute Deviation (MAD)52
Skewness1.5820304
Sum2886
Variance8750.4857
MonotonicityNot monotonic
2023-12-13T03:18:39.288121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 5
 
13.9%
0 3
 
8.3%
3 2
 
5.6%
33 2
 
5.6%
174 1
 
2.8%
54 1
 
2.8%
376 1
 
2.8%
230 1
 
2.8%
229 1
 
2.8%
2 1
 
2.8%
Other values (18) 18
50.0%
ValueCountFrequency (%)
0 3
8.3%
1 5
13.9%
2 1
 
2.8%
3 2
 
5.6%
7 1
 
2.8%
23 1
 
2.8%
33 2
 
5.6%
35 1
 
2.8%
45 1
 
2.8%
53 1
 
2.8%
ValueCountFrequency (%)
376 1
2.8%
323 1
2.8%
230 1
2.8%
229 1
2.8%
182 1
2.8%
174 1
2.8%
144 1
2.8%
126 1
2.8%
117 1
2.8%
107 1
2.8%

2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.138889
Minimum0
Maximum309
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:39.459930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.75
median50.5
Q3105
95-th percentile259.25
Maximum309
Range309
Interquartile range (IQR)102.25

Descriptive statistics

Standard deviation84.788865
Coefficient of variation (CV)1.1284285
Kurtosis1.2362354
Mean75.138889
Median Absolute Deviation (MAD)48.5
Skewness1.3388202
Sum2705
Variance7189.1516
MonotonicityNot monotonic
2023-12-13T03:18:39.654472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2 4
 
11.1%
0 3
 
8.3%
1 2
 
5.6%
188 1
 
2.8%
93 1
 
2.8%
39 1
 
2.8%
74 1
 
2.8%
49 1
 
2.8%
94 1
 
2.8%
117 1
 
2.8%
Other values (20) 20
55.6%
ValueCountFrequency (%)
0 3
8.3%
1 2
5.6%
2 4
11.1%
3 1
 
2.8%
5 1
 
2.8%
6 1
 
2.8%
21 1
 
2.8%
29 1
 
2.8%
30 1
 
2.8%
34 1
 
2.8%
ValueCountFrequency (%)
309 1
2.8%
293 1
2.8%
248 1
2.8%
188 1
2.8%
167 1
2.8%
164 1
2.8%
163 1
2.8%
133 1
2.8%
117 1
2.8%
101 1
2.8%

3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.138889
Minimum0
Maximum421
Zeros4
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:39.839454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.75
median54.5
Q3148
95-th percentile313.25
Maximum421
Range421
Interquartile range (IQR)143.25

Descriptive statistics

Standard deviation106.96626
Coefficient of variation (CV)1.13626
Kurtosis1.4018042
Mean94.138889
Median Absolute Deviation (MAD)53
Skewness1.3295412
Sum3389
Variance11441.78
MonotonicityNot monotonic
2023-12-13T03:18:40.004916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 4
 
11.1%
2 3
 
8.3%
43 2
 
5.6%
7 2
 
5.6%
32 1
 
2.8%
421 1
 
2.8%
244 1
 
2.8%
312 1
 
2.8%
5 1
 
2.8%
174 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
0 4
11.1%
1 1
 
2.8%
2 3
8.3%
4 1
 
2.8%
5 1
 
2.8%
7 2
5.6%
20 1
 
2.8%
27 1
 
2.8%
32 1
 
2.8%
42 1
 
2.8%
ValueCountFrequency (%)
421 1
2.8%
317 1
2.8%
312 1
2.8%
244 1
2.8%
210 1
2.8%
199 1
2.8%
197 1
2.8%
174 1
2.8%
154 1
2.8%
146 1
2.8%

4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.44444
Minimum0
Maximum464
Zeros4
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:40.139222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median67
Q3142
95-th percentile337.25
Maximum464
Range464
Interquartile range (IQR)139

Descriptive statistics

Standard deviation117.02099
Coefficient of variation (CV)1.1535475
Kurtosis1.8778725
Mean101.44444
Median Absolute Deviation (MAD)65
Skewness1.4762728
Sum3652
Variance13693.911
MonotonicityNot monotonic
2023-12-13T03:18:40.284593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 4
 
11.1%
3 3
 
8.3%
1 3
 
8.3%
5 2
 
5.6%
60 2
 
5.6%
222 1
 
2.8%
35 1
 
2.8%
464 1
 
2.8%
274 1
 
2.8%
323 1
 
2.8%
Other values (17) 17
47.2%
ValueCountFrequency (%)
0 4
11.1%
1 3
8.3%
3 3
8.3%
5 2
5.6%
35 1
 
2.8%
37 1
 
2.8%
48 1
 
2.8%
60 2
5.6%
61 1
 
2.8%
73 1
 
2.8%
ValueCountFrequency (%)
464 1
2.8%
380 1
2.8%
323 1
2.8%
274 1
2.8%
228 1
2.8%
222 1
2.8%
211 1
2.8%
179 1
2.8%
148 1
2.8%
140 1
2.8%

5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.722222
Minimum0
Maximum383
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:40.426530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median79.5
Q3155.25
95-th percentile298.25
Maximum383
Range383
Interquartile range (IQR)153.25

Descriptive statistics

Standard deviation102.18795
Coefficient of variation (CV)1.024726
Kurtosis0.44374275
Mean99.722222
Median Absolute Deviation (MAD)77.5
Skewness1.0165796
Sum3590
Variance10442.378
MonotonicityNot monotonic
2023-12-13T03:18:40.592967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 5
 
13.9%
2 4
 
11.1%
196 1
 
2.8%
48 1
 
2.8%
383 1
 
2.8%
235 1
 
2.8%
302 1
 
2.8%
175 1
 
2.8%
191 1
 
2.8%
123 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
0 1
 
2.8%
1 5
13.9%
2 4
11.1%
5 1
 
2.8%
7 1
 
2.8%
31 1
 
2.8%
45 1
 
2.8%
48 1
 
2.8%
60 1
 
2.8%
68 1
 
2.8%
ValueCountFrequency (%)
383 1
2.8%
302 1
2.8%
297 1
2.8%
259 1
2.8%
235 1
2.8%
196 1
2.8%
191 1
2.8%
175 1
2.8%
159 1
2.8%
154 1
2.8%

6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.777778
Minimum0
Maximum345
Zeros4
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:40.748586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median75
Q3145.5
95-th percentile289.25
Maximum345
Range345
Interquartile range (IQR)141.5

Descriptive statistics

Standard deviation93.025069
Coefficient of variation (CV)1.0135903
Kurtosis0.57851002
Mean91.777778
Median Absolute Deviation (MAD)71
Skewness1.0517378
Sum3304
Variance8653.6635
MonotonicityNot monotonic
2023-12-13T03:18:40.875809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 4
 
11.1%
3 3
 
8.3%
4 3
 
8.3%
187 1
 
2.8%
73 1
 
2.8%
345 1
 
2.8%
209 1
 
2.8%
288 1
 
2.8%
176 1
 
2.8%
169 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
0 4
11.1%
1 1
 
2.8%
3 3
8.3%
4 3
8.3%
5 1
 
2.8%
39 1
 
2.8%
45 1
 
2.8%
55 1
 
2.8%
58 1
 
2.8%
72 1
 
2.8%
ValueCountFrequency (%)
345 1
2.8%
293 1
2.8%
288 1
2.8%
209 1
2.8%
192 1
2.8%
187 1
2.8%
176 1
2.8%
169 1
2.8%
159 1
2.8%
141 1
2.8%

7
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.333333
Minimum0
Maximum302
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:41.013830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.75
median58
Q3117
95-th percentile226
Maximum302
Range302
Interquartile range (IQR)114.25

Descriptive statistics

Standard deviation80.634271
Coefficient of variation (CV)1.0293737
Kurtosis0.93496305
Mean78.333333
Median Absolute Deviation (MAD)55.5
Skewness1.1302592
Sum2820
Variance6501.8857
MonotonicityNot monotonic
2023-12-13T03:18:41.132630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 4
 
11.1%
0 3
 
8.3%
1 2
 
5.6%
3 2
 
5.6%
170 1
 
2.8%
55 1
 
2.8%
302 1
 
2.8%
171 1
 
2.8%
207 1
 
2.8%
129 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
0 3
8.3%
1 2
5.6%
2 4
11.1%
3 2
5.6%
4 1
 
2.8%
33 1
 
2.8%
35 1
 
2.8%
51 1
 
2.8%
52 1
 
2.8%
53 1
 
2.8%
ValueCountFrequency (%)
302 1
2.8%
283 1
2.8%
207 1
2.8%
179 1
2.8%
171 1
2.8%
170 1
2.8%
148 1
2.8%
135 1
2.8%
129 1
2.8%
113 1
2.8%

8
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.555556
Minimum0
Maximum396
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:41.269551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median74.5
Q3143
95-th percentile253
Maximum396
Range396
Interquartile range (IQR)139.25

Descriptive statistics

Standard deviation97.176847
Coefficient of variation (CV)1.0277222
Kurtosis1.3520792
Mean94.555556
Median Absolute Deviation (MAD)70
Skewness1.170727
Sum3404
Variance9443.3397
MonotonicityNot monotonic
2023-12-13T03:18:41.406678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3 3
 
8.3%
0 3
 
8.3%
108 2
 
5.6%
143 2
 
5.6%
5 2
 
5.6%
1 2
 
5.6%
201 1
 
2.8%
61 1
 
2.8%
396 1
 
2.8%
2 1
 
2.8%
Other values (18) 18
50.0%
ValueCountFrequency (%)
0 3
8.3%
1 2
5.6%
2 1
 
2.8%
3 3
8.3%
4 1
 
2.8%
5 2
5.6%
37 1
 
2.8%
38 1
 
2.8%
51 1
 
2.8%
53 1
 
2.8%
ValueCountFrequency (%)
396 1
2.8%
304 1
2.8%
236 1
2.8%
234 1
2.8%
201 1
2.8%
185 1
2.8%
182 1
2.8%
170 1
2.8%
143 2
5.6%
141 1
2.8%

9
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.63889
Minimum0
Maximum415
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:41.550403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q12
median90
Q3161.25
95-th percentile353.75
Maximum415
Range415
Interquartile range (IQR)159.25

Descriptive statistics

Standard deviation114.25239
Coefficient of variation (CV)1.0614416
Kurtosis0.63120058
Mean107.63889
Median Absolute Deviation (MAD)87.5
Skewness1.1136309
Sum3875
Variance13053.609
MonotonicityNot monotonic
2023-12-13T03:18:41.698548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 4
 
11.1%
1 4
 
11.1%
125 2
 
5.6%
0 2
 
5.6%
222 1
 
2.8%
92 1
 
2.8%
415 1
 
2.8%
262 1
 
2.8%
365 1
 
2.8%
198 1
 
2.8%
Other values (18) 18
50.0%
ValueCountFrequency (%)
0 2
5.6%
1 4
11.1%
2 4
11.1%
3 1
 
2.8%
4 1
 
2.8%
34 1
 
2.8%
44 1
 
2.8%
49 1
 
2.8%
58 1
 
2.8%
64 1
 
2.8%
ValueCountFrequency (%)
415 1
2.8%
365 1
2.8%
350 1
2.8%
262 1
2.8%
241 1
2.8%
222 1
2.8%
214 1
2.8%
198 1
2.8%
171 1
2.8%
158 1
2.8%

10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.05556
Minimum0
Maximum495
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:41.820514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14.75
median81.5
Q3165.75
95-th percentile321
Maximum495
Range495
Interquartile range (IQR)161

Descriptive statistics

Standard deviation120.23998
Coefficient of variation (CV)1.073039
Kurtosis1.8129732
Mean112.05556
Median Absolute Deviation (MAD)79.5
Skewness1.3411042
Sum4034
Variance14457.654
MonotonicityNot monotonic
2023-12-13T03:18:41.954551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 4
 
11.1%
2 3
 
8.3%
6 2
 
5.6%
162 2
 
5.6%
226 1
 
2.8%
105 1
 
2.8%
92 1
 
2.8%
58 1
 
2.8%
77 1
 
2.8%
193 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
0 1
 
2.8%
1 4
11.1%
2 3
8.3%
4 1
 
2.8%
5 1
 
2.8%
6 2
5.6%
50 1
 
2.8%
51 1
 
2.8%
57 1
 
2.8%
58 1
 
2.8%
ValueCountFrequency (%)
495 1
2.8%
381 1
2.8%
301 1
2.8%
272 1
2.8%
267 1
2.8%
226 1
2.8%
211 1
2.8%
193 1
2.8%
168 1
2.8%
165 1
2.8%

11
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.66667
Minimum0
Maximum467
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:42.104686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median86
Q3167.75
95-th percentile350.75
Maximum467
Range467
Interquartile range (IQR)165.75

Descriptive statistics

Standard deviation123.21549
Coefficient of variation (CV)1.0840072
Kurtosis0.85702431
Mean113.66667
Median Absolute Deviation (MAD)84
Skewness1.1906042
Sum4092
Variance15182.057
MonotonicityNot monotonic
2023-12-13T03:18:42.239653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 6
 
16.7%
2 3
 
8.3%
86 2
 
5.6%
228 1
 
2.8%
45 1
 
2.8%
467 1
 
2.8%
279 1
 
2.8%
377 1
 
2.8%
217 1
 
2.8%
322 1
 
2.8%
Other values (18) 18
50.0%
ValueCountFrequency (%)
0 1
 
2.8%
1 6
16.7%
2 3
8.3%
7 1
 
2.8%
8 1
 
2.8%
29 1
 
2.8%
45 1
 
2.8%
54 1
 
2.8%
57 1
 
2.8%
69 1
 
2.8%
ValueCountFrequency (%)
467 1
2.8%
377 1
2.8%
342 1
2.8%
322 1
2.8%
279 1
2.8%
228 1
2.8%
217 1
2.8%
204 1
2.8%
179 1
2.8%
164 1
2.8%

12
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.02778
Minimum0
Maximum558
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T03:18:42.369527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median74.5
Q3161.75
95-th percentile365.75
Maximum558
Range558
Interquartile range (IQR)158.75

Descriptive statistics

Standard deviation138.88813
Coefficient of variation (CV)1.1867963
Kurtosis2.6012229
Mean117.02778
Median Absolute Deviation (MAD)71.5
Skewness1.6271603
Sum4213
Variance19289.913
MonotonicityNot monotonic
2023-12-13T03:18:42.508398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3 3
 
8.3%
0 3
 
8.3%
1 3
 
8.3%
125 2
 
5.6%
295 1
 
2.8%
138 1
 
2.8%
101 1
 
2.8%
86 1
 
2.8%
143 1
 
2.8%
241 1
 
2.8%
Other values (19) 19
52.8%
ValueCountFrequency (%)
0 3
8.3%
1 3
8.3%
2 1
 
2.8%
3 3
8.3%
4 1
 
2.8%
5 1
 
2.8%
32 1
 
2.8%
39 1
 
2.8%
55 1
 
2.8%
56 1
 
2.8%
ValueCountFrequency (%)
558 1
2.8%
491 1
2.8%
324 1
2.8%
317 1
2.8%
295 1
2.8%
241 1
2.8%
225 1
2.8%
187 1
2.8%
176 1
2.8%
157 1
2.8%

Interactions

2023-12-13T03:18:36.618248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:24.543824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.826264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.899671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.147795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.173843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.191356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.198196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:32.379983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.895800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.958534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.819350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.687008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:24.661989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.930787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.975538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.249563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.260614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.284645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.284753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:32.480863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.986931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.035158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.888582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.754430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:24.755517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.030493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.059031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.338490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.357362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.364711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.364615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:32.591515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.073587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.100274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.962072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.820657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:24.848060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.108962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.384821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.421476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.445360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.443605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.444859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:32.686099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.159803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.166604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.024259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.895903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:24.991071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.180162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.450863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.494563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.529992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.518520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.527357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.081322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.242377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.234026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.083254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.992766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.100083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.280799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.545649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.582953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.618299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.605544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.645293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.180274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.355077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.318343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.155818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:37.080260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.230042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.372469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.633663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.670395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.701588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.690819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.805874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.292443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.481206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.398343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.222976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:37.203470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.342532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.459211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.725472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.750749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.783074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.764215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.902990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.390608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.590233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.468531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.292992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:37.305051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.456941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.558378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.819564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.839123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.875084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.855899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:32.007804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.494388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.682972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.550752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.365364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:37.408466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.550453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.630785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.896969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.927393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.949980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.932364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:32.097494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.591650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.755475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.610920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.425166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:37.545869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.639135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.720220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:27.977762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.010302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.038318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.024849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:32.200212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.696053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.829253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.677993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.490855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:37.641510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:25.727668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:26.820374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:28.051292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:29.093518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:30.110841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:31.105515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:32.288348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:33.803149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:34.893800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:35.747310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:36.550926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:18:42.630391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생시간대발생월123456789101112
발생시간대1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
발생월0.0001.0000.5990.8750.6450.8930.9260.9780.7870.7360.9150.9520.7600.680
10.0000.5991.0000.8660.9680.9270.9050.9150.9810.9750.8930.9350.9440.991
20.0000.8750.8661.0000.8280.9690.9660.9460.9130.8860.9480.9610.8750.893
30.0000.6450.9680.8281.0000.9440.8970.9390.9410.9510.9400.9040.9480.965
40.0000.8930.9270.9690.9441.0000.9680.9730.8950.9300.9880.9870.9590.923
50.0000.9260.9050.9660.8970.9681.0000.9730.9140.8810.9840.9620.8900.880
60.0000.9780.9150.9460.9390.9730.9731.0000.9250.9000.9850.9750.9420.871
70.0000.7870.9810.9130.9410.8950.9140.9251.0000.9720.8890.9340.9200.967
80.0000.7360.9750.8860.9510.9300.8810.9000.9721.0000.9110.9150.9330.990
90.0000.9150.8930.9480.9400.9880.9840.9850.8890.9111.0000.9750.9280.880
100.0000.9520.9350.9610.9040.9870.9620.9750.9340.9150.9751.0000.9650.909
110.0000.7600.9440.8750.9480.9590.8900.9420.9200.9330.9280.9651.0000.930
120.0000.6800.9910.8930.9650.9230.8800.8710.9670.9900.8800.9090.9301.000
2023-12-13T03:18:42.770763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생시간대발생월
발생시간대1.0000.000
발생월0.0001.000
2023-12-13T03:18:42.883464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
123456789101112발생시간대발생월
11.0000.9610.9710.9640.9780.9780.9710.9760.9570.9730.9510.9740.0000.425
20.9611.0000.9770.9650.9640.9820.9760.9670.9790.9500.9620.9750.0000.538
30.9710.9771.0000.9800.9700.9790.9760.9790.9630.9650.9520.9800.0000.474
40.9640.9650.9801.0000.9670.9760.9630.9780.9630.9690.9590.9760.0000.563
50.9780.9640.9700.9671.0000.9730.9600.9850.9460.9660.9510.9570.0000.615
60.9780.9820.9790.9760.9731.0000.9800.9840.9780.9620.9700.9800.0000.741
70.9710.9760.9760.9630.9600.9801.0000.9650.9650.9520.9570.9760.0000.647
80.9760.9670.9790.9780.9850.9840.9651.0000.9610.9740.9600.9640.0000.581
90.9570.9790.9630.9630.9460.9780.9650.9611.0000.9510.9640.9720.0000.596
100.9730.9500.9650.9690.9660.9620.9520.9740.9511.0000.9570.9650.0000.669
110.9510.9620.9520.9590.9510.9700.9570.9600.9640.9571.0000.9600.0000.559
120.9740.9750.9800.9760.9570.9800.9760.9640.9720.9650.9601.0000.0000.514
발생시간대0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
발생월0.4250.5380.4740.5630.6150.7410.6470.5810.5960.6690.5590.5140.0001.000

Missing values

2023-12-13T03:18:37.801201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:18:38.080458image/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

발생시간대발생월123456789101112
000시-02시사고건수174188199222196187170201222226228295
100시-02시사망자수167575451273
200시-02시부상자수323293317380297293283304350381342491
302시-04시사고건수104101125137154128110127149132132157
402시-04시사망자수314553251603
502시-04시부상자수182167197211259192179185241211204225
604시-06시사고건수86819376105958698107115104125
704시-06시사망자수722324132425
804시-06시부상자수11713314698159159135141158165149187
906시-08시사고건수65599288908872108931009185
발생시간대발생월123456789101112
2616시-18시부상자수8994109111120116111143171193164143
2718시-20시사고건수9693130140123105100121125162179138
2818시-20시사망자수022313033221
2918시-20시부상자수144163210228191169148182214267322241
3020시-22시사고건수126117174179175176129143198168217176
3120시-22시사망자수235024242111
3220시-22시부상자수229248312323302288207236365301377317
3322시-24시사고건수230164244274235209171234262272279324
3422시-24시사망자수327313322614
3522시-24시부상자수376309421464383345302396415495467558