Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

DateTime3
Numeric10
Categorical2

Dataset

Description선박 SSAS 경보 발신 및 수신 현황에 대한 데이터. 경보 발신시간, 수신시간, 위도, 경도, Heading, Speed 값에 대한 정보가 포함되어있음.
Author해양수산부
URLhttps://www.data.go.kr/data/15071894/fileData.do

Alerts

경도_방위 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
위도_방위 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
위도 is highly overall correlated with 위도_도 and 8 other fieldsHigh correlation
위도_도 is highly overall correlated with 위도 and 8 other fieldsHigh correlation
위도_분 is highly overall correlated with 위도 and 9 other fieldsHigh correlation
위도_초 is highly overall correlated with 위도 and 9 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 9 other fieldsHigh correlation
경도_도 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
경도_분 is highly overall correlated with 위도 and 7 other fieldsHigh correlation
경도_초 is highly overall correlated with 위도 and 7 other fieldsHigh correlation
헤딩값 is highly overall correlated with 위도_분 and 4 other fieldsHigh correlation
속도 is highly overall correlated with 위도_방위 and 1 other fieldsHigh correlation
위도 has 2939 (29.4%) zerosZeros
위도_도 has 3025 (30.2%) zerosZeros
위도_분 has 3158 (31.6%) zerosZeros
위도_초 has 3080 (30.8%) zerosZeros
경도 has 2946 (29.5%) zerosZeros
경도_도 has 2996 (30.0%) zerosZeros
경도_분 has 3184 (31.8%) zerosZeros
경도_초 has 3115 (31.1%) zerosZeros
헤딩값 has 3428 (34.3%) zerosZeros
속도 has 6858 (68.6%) zerosZeros

Reproduction

Analysis started2023-12-12 13:44:58.184915
Analysis finished2023-12-12 13:45:13.315036
Duration15.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct9919
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2010-04-14 06:41:00
Maximum2022-09-16 10:00:46
2023-12-12T22:45:13.389106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:13.572146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct9969
Distinct (%)99.7%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
Minimum1980-01-04 05:01:41
Maximum2022-09-16 10:00:39
2023-12-12T22:45:13.744312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:13.887262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct9994
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2010-04-14 15:45:15
Maximum2022-09-16 10:00:46
2023-12-12T22:45:14.070431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:14.253346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5593
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.356735
Minimum-42.845
Maximum63.625833
Zeros2939
Zeros (%)29.4%
Negative483
Negative (%)4.8%
Memory size166.0 KiB
2023-12-12T22:45:14.405648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-42.845
5-th percentile0
Q10
median24.501167
Q335.077167
95-th percentile38
Maximum63.625833
Range106.47083
Interquartile range (IQR)35.077167

Descriptive statistics

Standard deviation18.199468
Coefficient of variation (CV)0.99143278
Kurtosis-0.91788478
Mean18.356735
Median Absolute Deviation (MAD)12.773035
Skewness-0.42197573
Sum183567.35
Variance331.22065
MonotonicityNot monotonic
2023-12-12T22:45:14.549627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2939
29.4%
35.0855 41
 
0.4%
35.1035 25
 
0.2%
33.639576 22
 
0.2%
38.685167 21
 
0.2%
38.0 18
 
0.2%
20.193833 15
 
0.1%
35.091667 14
 
0.1%
34.732833 14
 
0.1%
34.732333 13
 
0.1%
Other values (5583) 6878
68.8%
ValueCountFrequency (%)
-42.845 1
< 0.1%
-40.4895 1
< 0.1%
-39.779167 1
< 0.1%
-38.756 1
< 0.1%
-38.639167 1
< 0.1%
-38.632833 1
< 0.1%
-38.094167 1
< 0.1%
-37.795176 1
< 0.1%
-37.661333 1
< 0.1%
-37.6595 1
< 0.1%
ValueCountFrequency (%)
63.625833 1
< 0.1%
57.041 1
< 0.1%
56.698833 1
< 0.1%
55.671667 1
< 0.1%
55.046 1
< 0.1%
55.008667 1
< 0.1%
54.782 1
< 0.1%
54.771 1
< 0.1%
54.278333 1
< 0.1%
54.133667 1
< 0.1%

위도_도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.0897
Minimum-42
Maximum63
Zeros3025
Zeros (%)30.2%
Negative461
Negative (%)4.6%
Memory size166.0 KiB
2023-12-12T22:45:14.733679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-42
5-th percentile0
Q10
median24
Q335
95-th percentile38
Maximum63
Range105
Interquartile range (IQR)35

Descriptive statistics

Standard deviation17.98937
Coefficient of variation (CV)0.99445372
Kurtosis-0.9373348
Mean18.0897
Median Absolute Deviation (MAD)13
Skewness-0.40913201
Sum180897
Variance323.61742
MonotonicityNot monotonic
2023-12-12T22:45:14.878672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3025
30.2%
35 1730
17.3%
34 902
 
9.0%
36 372
 
3.7%
37 363
 
3.6%
31 239
 
2.4%
33 226
 
2.3%
38 210
 
2.1%
32 177
 
1.8%
5 171
 
1.7%
Other values (90) 2585
25.9%
ValueCountFrequency (%)
-42 1
 
< 0.1%
-40 1
 
< 0.1%
-39 1
 
< 0.1%
-38 4
 
< 0.1%
-37 3
 
< 0.1%
-36 1
 
< 0.1%
-35 11
0.1%
-34 24
0.2%
-33 15
0.1%
-32 14
0.1%
ValueCountFrequency (%)
63 1
 
< 0.1%
57 1
 
< 0.1%
56 1
 
< 0.1%
55 3
 
< 0.1%
54 5
0.1%
53 9
0.1%
52 1
 
< 0.1%
51 11
0.1%
50 2
 
< 0.1%
49 6
0.1%

위도_분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct119
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5005
Minimum-59
Maximum59
Zeros3158
Zeros (%)31.6%
Negative475
Negative (%)4.8%
Memory size166.0 KiB
2023-12-12T22:45:15.015074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-59
5-th percentile0
Q10
median6
Q332
95-th percentile55
Maximum59
Range118
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.119144
Coefficient of variation (CV)1.4269955
Kurtosis0.04902044
Mean15.5005
Median Absolute Deviation (MAD)6
Skewness0.22047693
Sum155005
Variance489.25653
MonotonicityNot monotonic
2023-12-12T22:45:15.178910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3158
31.6%
5 369
 
3.7%
3 366
 
3.7%
4 304
 
3.0%
6 282
 
2.8%
54 181
 
1.8%
7 173
 
1.7%
1 150
 
1.5%
58 149
 
1.5%
46 147
 
1.5%
Other values (109) 4721
47.2%
ValueCountFrequency (%)
-59 5
 
0.1%
-58 16
0.2%
-57 5
 
0.1%
-56 9
0.1%
-55 10
0.1%
-54 10
0.1%
-53 8
0.1%
-52 8
0.1%
-51 5
 
0.1%
-50 6
 
0.1%
ValueCountFrequency (%)
59 124
1.2%
58 149
1.5%
57 95
0.9%
56 89
0.9%
55 114
1.1%
54 181
1.8%
53 129
1.3%
52 74
0.7%
51 81
0.8%
50 78
0.8%

위도_초
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct119
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.8513
Minimum-59
Maximum59
Zeros3080
Zeros (%)30.8%
Negative472
Negative (%)4.7%
Memory size166.0 KiB
2023-12-12T22:45:15.374630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-59
5-th percentile0
Q10
median14
Q336
95-th percentile55
Maximum59
Range118
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.460041
Coefficient of variation (CV)1.258174
Kurtosis0.059565608
Mean17.8513
Median Absolute Deviation (MAD)14
Skewness-0.1229142
Sum178513
Variance504.45343
MonotonicityNot monotonic
2023-12-12T22:45:15.538891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3080
30.8%
22 254
 
2.5%
46 184
 
1.8%
49 167
 
1.7%
7 161
 
1.6%
37 144
 
1.4%
58 137
 
1.4%
28 134
 
1.3%
21 133
 
1.3%
4 133
 
1.3%
Other values (109) 5473
54.7%
ValueCountFrequency (%)
-59 8
0.1%
-58 9
0.1%
-57 7
0.1%
-56 5
0.1%
-55 12
0.1%
-54 8
0.1%
-53 7
0.1%
-52 10
0.1%
-51 10
0.1%
-50 8
0.1%
ValueCountFrequency (%)
59 115
1.1%
58 137
1.4%
57 97
1.0%
56 98
1.0%
55 120
1.2%
54 102
1.0%
53 93
0.9%
52 97
1.0%
51 92
0.9%
50 100
1.0%

위도_방위
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
6578 
<NA>
2898 
S
 
484
 
40

Length

Max length4
Median length1
Mean length1.8694
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd row<NA>
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 6578
65.8%
<NA> 2898
29.0%
S 484
 
4.8%
40
 
0.4%

Length

2023-12-12T22:45:15.691428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:45:15.798271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 6578
66.0%
na 2898
29.1%
s 484
 
4.9%

경도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5569
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.154262
Minimum-177.38667
Maximum179.92333
Zeros2946
Zeros (%)29.5%
Negative259
Negative (%)2.6%
Memory size166.0 KiB
2023-12-12T22:45:15.903127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-177.38667
5-th percentile0
Q10
median115.942
Q3128.59083
95-th percentile130.40609
Maximum179.92333
Range357.31
Interquartile range (IQR)128.59083

Descriptive statistics

Standard deviation61.141687
Coefficient of variation (CV)0.80286626
Kurtosis-0.89779904
Mean76.154262
Median Absolute Deviation (MAD)14.464102
Skewness-0.63446877
Sum761542.62
Variance3738.3059
MonotonicityNot monotonic
2023-12-12T22:45:16.054021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2946
29.5%
129.034833 41
 
0.4%
130.406092 34
 
0.3%
128.994167 22
 
0.2%
125.0475 21
 
0.2%
130.406113 21
 
0.2%
128.0 18
 
0.2%
129.066833 18
 
0.2%
128.995808 16
 
0.2%
127.756833 16
 
0.2%
Other values (5559) 6847
68.5%
ValueCountFrequency (%)
-177.386667 1
< 0.1%
-176.787167 1
< 0.1%
-176.613 1
< 0.1%
-176.084333 1
< 0.1%
-174.712 1
< 0.1%
-169.806167 1
< 0.1%
-169.379833 1
< 0.1%
-164.732778 1
< 0.1%
-157.874725 1
< 0.1%
-157.857667 1
< 0.1%
ValueCountFrequency (%)
179.923333 1
< 0.1%
179.17653 1
< 0.1%
178.1505 1
< 0.1%
177.130833 1
< 0.1%
176.181167 1
< 0.1%
176.137667 1
< 0.1%
175.687833 1
< 0.1%
175.598667 1
< 0.1%
174.09 1
< 0.1%
172.959855 1
< 0.1%

경도_도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct249
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.3081
Minimum-176
Maximum179
Zeros2996
Zeros (%)30.0%
Negative322
Negative (%)3.2%
Memory size166.0 KiB
2023-12-12T22:45:16.242553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-176
5-th percentile0
Q10
median34
Q3119
95-th percentile129
Maximum179
Range355
Interquartile range (IQR)119

Descriptive statistics

Standard deviation54.181614
Coefficient of variation (CV)1.0769958
Kurtosis-1.0866078
Mean50.3081
Median Absolute Deviation (MAD)34
Skewness0.37782957
Sum503081
Variance2935.6473
MonotonicityNot monotonic
2023-12-12T22:45:16.427224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2996
30.0%
129 857
 
8.6%
35 721
 
7.2%
34 446
 
4.5%
126 333
 
3.3%
128 304
 
3.0%
127 265
 
2.6%
31 180
 
1.8%
36 169
 
1.7%
38 168
 
1.7%
Other values (239) 3561
35.6%
ValueCountFrequency (%)
-176 1
< 0.1%
-169 1
< 0.1%
-155 2
< 0.1%
-151 1
< 0.1%
-146 1
< 0.1%
-137 1
< 0.1%
-133 1
< 0.1%
-130 1
< 0.1%
-125 2
< 0.1%
-123 2
< 0.1%
ValueCountFrequency (%)
179 2
< 0.1%
178 1
< 0.1%
177 1
< 0.1%
176 2
< 0.1%
172 1
< 0.1%
171 2
< 0.1%
170 1
< 0.1%
168 2
< 0.1%
167 1
< 0.1%
166 1
< 0.1%

경도_분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct119
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.3935
Minimum-59
Maximum59
Zeros3184
Zeros (%)31.8%
Negative324
Negative (%)3.2%
Memory size166.0 KiB
2023-12-12T22:45:16.582769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-59
5-th percentile0
Q10
median8
Q336
95-th percentile55
Maximum59
Range118
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.531605
Coefficient of variation (CV)1.237911
Kurtosis-0.40209446
Mean17.3935
Median Absolute Deviation (MAD)8
Skewness0.25249921
Sum173935
Variance463.61002
MonotonicityNot monotonic
2023-12-12T22:45:17.095963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3184
31.8%
3 337
 
3.4%
4 273
 
2.7%
5 263
 
2.6%
2 217
 
2.2%
24 209
 
2.1%
59 195
 
1.9%
35 159
 
1.6%
45 156
 
1.6%
1 147
 
1.5%
Other values (109) 4860
48.6%
ValueCountFrequency (%)
-59 1
 
< 0.1%
-58 5
0.1%
-57 2
 
< 0.1%
-56 8
0.1%
-55 7
0.1%
-54 4
< 0.1%
-53 4
< 0.1%
-52 2
 
< 0.1%
-51 4
< 0.1%
-50 5
0.1%
ValueCountFrequency (%)
59 195
1.9%
58 93
0.9%
57 95
0.9%
56 65
 
0.7%
55 99
1.0%
54 143
1.4%
53 95
0.9%
52 65
 
0.7%
51 79
0.8%
50 112
1.1%

경도_초
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct118
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5819
Minimum-59
Maximum59
Zeros3115
Zeros (%)31.1%
Negative320
Negative (%)3.2%
Memory size166.0 KiB
2023-12-12T22:45:17.244338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-59
5-th percentile0
Q10
median15
Q337
95-th percentile55
Maximum59
Range118
Interquartile range (IQR)37

Descriptive statistics

Standard deviation21.563102
Coefficient of variation (CV)1.1604358
Kurtosis-0.15935937
Mean18.5819
Median Absolute Deviation (MAD)15
Skewness0.013254007
Sum185819
Variance464.96739
MonotonicityNot monotonic
2023-12-12T22:45:17.465162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3115
31.1%
46 195
 
1.9%
4 162
 
1.6%
22 158
 
1.6%
21 156
 
1.6%
31 149
 
1.5%
19 148
 
1.5%
23 146
 
1.5%
25 145
 
1.5%
7 142
 
1.4%
Other values (108) 5484
54.8%
ValueCountFrequency (%)
-59 1
 
< 0.1%
-58 3
 
< 0.1%
-57 4
 
< 0.1%
-56 6
0.1%
-55 7
0.1%
-54 10
0.1%
-53 6
0.1%
-52 13
0.1%
-50 9
0.1%
-49 5
 
0.1%
ValueCountFrequency (%)
59 111
1.1%
58 110
1.1%
57 93
0.9%
56 74
0.7%
55 133
1.3%
54 110
1.1%
53 130
1.3%
52 138
1.4%
51 99
1.0%
50 96
1.0%

경도_방위
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
E
6578 
<NA>
2898 
W
 
484
 
40

Length

Max length4
Median length1
Mean length1.8694
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd row<NA>
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 6578
65.8%
<NA> 2898
29.0%
W 484
 
4.8%
40
 
0.4%

Length

2023-12-12T22:45:17.660053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:45:17.796313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 6578
66.0%
na 2898
29.1%
w 484
 
4.9%

헤딩값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct367
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.98474
Minimum0
Maximum359
Zeros3428
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:45:17.933085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94
Q3229
95-th percentile328
Maximum359
Range359
Interquartile range (IQR)229

Descriptive statistics

Standard deviation120.19274
Coefficient of variation (CV)0.99345373
Kurtosis-1.3007335
Mean120.98474
Median Absolute Deviation (MAD)94
Skewness0.43624967
Sum1209847.4
Variance14446.294
MonotonicityNot monotonic
2023-12-12T22:45:18.108272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3428
34.3%
45.0 70
 
0.7%
267.0 49
 
0.5%
220.0 43
 
0.4%
40.0 40
 
0.4%
180.0 38
 
0.4%
270.0 37
 
0.4%
266.0 37
 
0.4%
230.0 34
 
0.3%
268.0 33
 
0.3%
Other values (357) 6191
61.9%
ValueCountFrequency (%)
0.0 3428
34.3%
1.0 18
 
0.2%
2.0 30
 
0.3%
3.0 17
 
0.2%
4.0 28
 
0.3%
5.0 28
 
0.3%
6.0 20
 
0.2%
7.0 16
 
0.2%
8.0 16
 
0.2%
9.0 16
 
0.2%
ValueCountFrequency (%)
359.0 15
0.1%
358.0 18
0.2%
357.0 13
0.1%
356.0 10
0.1%
355.0 13
0.1%
354.0 14
0.1%
353.0 13
0.1%
352.0 8
 
0.1%
351.0 16
0.2%
350.0 21
0.2%

속도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct203
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6059932
Minimum0
Maximum127
Zeros6858
Zeros (%)68.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:45:18.291262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.725
95-th percentile15.7
Maximum127
Range127
Interquartile range (IQR)7.725

Descriptive statistics

Standard deviation8.3234496
Coefficient of variation (CV)2.3082267
Kurtosis113.85829
Mean3.6059932
Median Absolute Deviation (MAD)0
Skewness8.246099
Sum36059.932
Variance69.279814
MonotonicityNot monotonic
2023-12-12T22:45:18.479659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6858
68.6%
10.0 249
 
2.5%
11.0 242
 
2.4%
0.1 211
 
2.1%
9.0 199
 
2.0%
12.0 189
 
1.9%
13.0 115
 
1.1%
8.0 99
 
1.0%
7.0 93
 
0.9%
6.0 58
 
0.6%
Other values (193) 1687
 
16.9%
ValueCountFrequency (%)
0.0 6858
68.6%
0.1 211
 
2.1%
0.2 45
 
0.4%
0.3 24
 
0.2%
0.4 17
 
0.2%
0.5 9
 
0.1%
0.6 7
 
0.1%
0.7 2
 
< 0.1%
0.8 3
 
< 0.1%
0.9 1
 
< 0.1%
ValueCountFrequency (%)
127.0 24
0.2%
40.0 2
 
< 0.1%
39.0 1
 
< 0.1%
38.0 1
 
< 0.1%
37.8 1
 
< 0.1%
34.0 1
 
< 0.1%
25.4 1
 
< 0.1%
24.6 1
 
< 0.1%
24.0 2
 
< 0.1%
23.0 1
 
< 0.1%

Interactions

2023-12-12T22:45:11.575701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:01.931551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.989573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.109741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:05.140950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.475338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:07.577537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.614551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.482642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.493539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:11.671838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.043029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:03.095226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.206108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:05.233997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.575492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:07.717011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.699616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.573086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.597469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:12.071286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.130776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:03.218204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.298655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:05.632020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.662341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:07.837498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.781594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.669331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.690454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:12.160948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.221548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:03.320307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.410661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:05.760923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.745343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:07.944618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.867038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.764401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.799627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:12.251205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.313229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:03.442576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.509553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:05.860496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.839804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.040739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.954012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.870206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.927016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:12.345363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.422292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:03.557697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.605331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:05.952105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.924304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.137894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.039733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.952461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:11.079027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:12.459450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.533566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:03.680393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.699925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.038517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:07.014405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.226256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.126175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.041161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:11.186841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:12.568470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.647591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:03.793934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.825381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.133620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:07.124991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.317154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.206463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.129072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:11.277679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:12.664584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.761027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:03.902298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.934221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.218578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:07.254344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.420660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.289693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.239729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:11.368545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:12.786162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:02.890191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:04.005587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:05.044069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:06.356497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:07.411763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:08.521746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:09.385734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:10.378906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:45:11.469570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:45:18.616767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도위도_도위도_분위도_초위도_방위경도경도_도경도_분경도_초경도_방위헤딩값속도
위도1.0001.0000.8080.8350.8170.8540.8030.7170.7280.8170.6040.281
위도_도1.0001.0000.8000.8310.8080.8530.8020.7130.7250.8080.5980.277
위도_분0.8080.8001.0000.8070.8080.6910.6570.8980.6720.8080.4900.274
위도_초0.8350.8310.8071.0000.8100.7290.6980.6890.8960.8100.5520.201
위도_방위0.8170.8080.8080.8101.0000.6710.6250.6010.5971.0000.1450.596
경도0.8540.8530.6910.7290.6711.0000.9720.7440.7430.6710.6880.493
경도_도0.8030.8020.6570.6980.6250.9721.0000.7510.7470.6250.6510.359
경도_분0.7170.7130.8980.6890.6010.7440.7511.0000.8070.6010.5040.252
경도_초0.7280.7250.6720.8960.5970.7430.7470.8071.0000.5970.5490.214
경도_방위0.8170.8080.8080.8101.0000.6710.6250.6010.5971.0000.1450.596
헤딩값0.6040.5980.4900.5520.1450.6880.6510.5040.5490.1451.0000.345
속도0.2810.2770.2740.2010.5960.4930.3590.2520.2140.5960.3451.000
2023-12-12T22:45:18.768657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도_방위위도_방위
경도_방위1.0001.000
위도_방위1.0001.000
2023-12-12T22:45:18.872810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도위도_도위도_분위도_초경도경도_도경도_분경도_초헤딩값속도위도_방위경도_방위
위도1.0000.9950.6590.7010.7680.7190.5760.5980.4620.0100.7170.717
위도_도0.9951.0000.6360.7000.7700.7200.5630.5990.4610.0080.7050.705
위도_분0.6590.6361.0000.6820.5290.5870.7790.5910.5130.2750.6770.677
위도_초0.7010.7000.6821.0000.5960.6020.5930.7410.5110.1920.7170.717
경도0.7680.7700.5290.5961.0000.7880.5620.6110.5040.0650.5230.523
경도_도0.7190.7200.5870.6020.7881.0000.6610.6500.4920.1150.4710.471
경도_분0.5760.5630.7790.5930.5620.6611.0000.6790.5400.3040.4640.464
경도_초0.5980.5990.5910.7410.6110.6500.6791.0000.5480.2420.4830.483
헤딩값0.4620.4610.5130.5110.5040.4920.5400.5481.0000.4080.0860.086
속도0.0100.0080.2750.1920.0650.1150.3040.2420.4081.0000.5480.548
위도_방위0.7170.7050.6770.7170.5230.4710.4640.4830.0860.5481.0001.000
경도_방위0.7170.7050.6770.7170.5230.4710.4640.4830.0860.5481.0001.000

Missing values

2023-12-12T22:45:12.958473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:45:13.197898image/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

전송시간(UTC)전송시간(KST)수신시간(KST)위도위도_도위도_분위도_초위도_방위경도경도_도경도_분경도_초경도_방위헤딩값속도
435962015-06-20 01:57:002015-06-20 10:59:022015-06-20 10:59:2835.07733335438N128.79251284732E342.00.0
106032021-02-04 00:11:002021-02-04 09:12:542021-02-04 09:13:0235.6215353717N139.7596671394534E12.00.0
599382014-02-02 11:54:062014-02-02 11:54:032014-02-02 11:54:060.0000<NA>0.0000<NA>0.00.0
687552013-07-07 14:17:002013-07-07 23:17:392013-07-07 23:18:0435.06286335346N128.99580835346E103.00.0
572572014-03-05 00:12:212014-03-05 09:13:322014-03-05 09:13:5232.31816732195N122.58083332195E177.011.0
457202015-05-11 09:58:002015-05-11 10:01:142015-05-11 10:01:336.146823N92.66866792407E266.016.8
140522020-05-22 06:32:532020-05-22 06:32:272020-05-22 06:32:530.0000<NA>0.0000<NA>0.00.0
729792013-01-21 12:43:422013-01-21 12:43:132013-01-21 12:43:420.0000<NA>0.0000<NA>0.00.0
222742018-11-19 15:32:562018-11-19 15:32:362018-11-19 15:32:560.0000<NA>0.0000<NA>0.00.0
103012021-03-02 11:46:322021-03-02 18:23:572021-03-02 11:46:320.0000<NA>0.0000<NA>0.00.0
전송시간(UTC)전송시간(KST)수신시간(KST)위도위도_도위도_분위도_초위도_방위경도경도_도경도_분경도_초경도_방위헤딩값속도
715412013-04-03 15:33:312013-04-03 15:31:312013-04-03 15:33:310.0000<NA>0.0000<NA>0.00.0
607922014-01-27 23:44:532014-01-28 08:48:572014-01-28 08:49:2231.05716731325N122.13066731325E289.010.0
442042015-06-04 13:53:002015-06-04 22:57:432015-06-04 22:58:1534.660875343939N128.8495741285058E31.00.0
482102015-02-04 09:17:002015-02-04 18:19:102015-02-04 18:19:2431.189333311121N29.877295237E160.00.0
266012018-02-21 01:46:002018-02-21 10:46:502018-02-21 10:46:5835.08810835517N129.038659129219E0.00.0
611052014-01-25 23:42:592014-01-26 08:44:282014-01-26 08:44:5133.6905334125N127.903167334125E221.06.0
246312018-05-28 12:40:202018-05-28 21:41:492018-05-28 21:42:1813.182131055N100.8355100507E220.00.0
919822011-01-01 03:32:592011-01-01 12:34:412011-01-01 12:35:070.0988330555N109.0233330555E136.00.0
609852014-01-26 19:25:322014-01-27 04:26:442014-01-27 04:27:0831.824167314927N125.120167314927E250.010.0
49472022-02-26 18:52:002022-02-27 04:18:552022-02-27 04:19:112.52731223138N86.572185863419E118.011.0