Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.3 KiB
Average record size in memory68.3 B

Variable types

Text3
Numeric4
Categorical1

Dataset

Description샘플 데이터
Author서울시(스마트카드사)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=13

Alerts

승차일시(GETON_DATETIME) has unique valuesUnique
이용거리(DISTANCE) has 36 (7.2%) zerosZeros

Reproduction

Analysis started2024-01-14 06:49:42.669577
Analysis finished2024-01-14 06:49:46.295606
Duration3.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

카드번호(CARD_ID)
Text

Distinct487
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-01-14T15:49:46.435468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters8000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique474 ?
Unique (%)94.8%

Sample

1st rowD*0*3*6*4*B*E*A*
2nd rowD*0*3*9*0*8*6*4*
3rd rowD*0*3*6*8*0*8*6*
4th rowD*0*3*9*0*8*8*B*
5th rowD*0*3*9*0*8*4*9*
ValueCountFrequency (%)
d*0*3*9*0*8*9*5 2
 
0.4%
d*0*3*9*0*c*8*4 2
 
0.4%
d*0*3*7*1*9*7*5 2
 
0.4%
d*0*3*9*1*c*2*5 2
 
0.4%
d*0*3*6*6*a*a*7 2
 
0.4%
d*0*3*9*1*1*a*6 2
 
0.4%
d*0*3*9*1*4*5*a 2
 
0.4%
d*0*3*9*1*0*8*7 2
 
0.4%
d*0*3*9*0*9*7*7 2
 
0.4%
d*0*3*9*0*8*6*4 2
 
0.4%
Other values (477) 480
96.0%
2024-01-14T15:49:46.789143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 4000
50.0%
0 661
 
8.3%
3 640
 
8.0%
D 623
 
7.8%
6 388
 
4.9%
9 233
 
2.9%
8 194
 
2.4%
4 181
 
2.3%
1 167
 
2.1%
7 161
 
2.0%
Other values (7) 752
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 4000
50.0%
Decimal Number 2907
36.3%
Uppercase Letter 1093
 
13.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 661
22.7%
3 640
22.0%
6 388
13.3%
9 233
 
8.0%
8 194
 
6.7%
4 181
 
6.2%
1 167
 
5.7%
7 161
 
5.5%
5 160
 
5.5%
2 122
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
D 623
57.0%
C 129
 
11.8%
B 103
 
9.4%
A 99
 
9.1%
E 88
 
8.1%
F 51
 
4.7%
Other Punctuation
ValueCountFrequency (%)
* 4000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6907
86.3%
Latin 1093
 
13.7%

Most frequent character per script

Common
ValueCountFrequency (%)
* 4000
57.9%
0 661
 
9.6%
3 640
 
9.3%
6 388
 
5.6%
9 233
 
3.4%
8 194
 
2.8%
4 181
 
2.6%
1 167
 
2.4%
7 161
 
2.3%
5 160
 
2.3%
Latin
ValueCountFrequency (%)
D 623
57.0%
C 129
 
11.8%
B 103
 
9.4%
A 99
 
9.1%
E 88
 
8.1%
F 51
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 4000
50.0%
0 661
 
8.3%
3 640
 
8.0%
D 623
 
7.8%
6 388
 
4.9%
9 233
 
2.9%
8 194
 
2.4%
4 181
 
2.3%
1 167
 
2.1%
7 161
 
2.0%
Other values (7) 752
 
9.4%

사용자구분코드(USER_TYPE)
Real number (ℝ)

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.986
Minimum11
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:49:46.917942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median21
Q321
95-th percentile23
Maximum42
Range31
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1257533
Coefficient of variation (CV)0.36063542
Kurtosis1.5381887
Mean16.986
Median Absolute Deviation (MAD)9
Skewness0.85589022
Sum8493
Variance37.524854
MonotonicityNot monotonic
2024-01-14T15:49:47.031915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
11 181
36.2%
21 151
30.2%
23 88
17.6%
12 62
 
12.4%
41 7
 
1.4%
24 7
 
1.4%
22 3
 
0.6%
42 1
 
0.2%
ValueCountFrequency (%)
11 181
36.2%
12 62
 
12.4%
21 151
30.2%
22 3
 
0.6%
23 88
17.6%
24 7
 
1.4%
41 7
 
1.4%
42 1
 
0.2%
ValueCountFrequency (%)
42 1
 
0.2%
41 7
 
1.4%
24 7
 
1.4%
23 88
17.6%
22 3
 
0.6%
21 151
30.2%
12 62
 
12.4%
11 181
36.2%

발생사ID(COMPANY_ID)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
C900008
302 
C900001
198 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC900008
2nd rowC900008
3rd rowC900008
4th rowC900008
5th rowC900001

Common Values

ValueCountFrequency (%)
C900008 302
60.4%
C900001 198
39.6%

Length

2024-01-14T15:49:47.152044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:49:47.244766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c900008 302
60.4%
c900001 198
39.6%

승차일시(GETON_DATETIME)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.019577 × 1013
Minimum2.0180201 × 1013
Maximum2.021103 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:49:47.353586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0180201 × 1013
5-th percentile2.0180424 × 1013
Q12.0190122 × 1013
median2.020011 × 1013
Q32.0201204 × 1013
95-th percentile2.0210912 × 1013
Maximum2.021103 × 1013
Range3.0828994 × 1010
Interquartile range (IQR)1.1081973 × 1010

Descriptive statistics

Standard deviation1.0814714 × 1010
Coefficient of variation (CV)0.000535494
Kurtosis-1.2842648
Mean2.019577 × 1013
Median Absolute Deviation (MAD)9.8920494 × 109
Skewness-0.018395945
Sum1.0097885 × 1016
Variance1.1695803 × 1020
MonotonicityNot monotonic
2024-01-14T15:49:47.503201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200807161516 1
 
0.2%
20180624221801 1
 
0.2%
20180228142746 1
 
0.2%
20200805143030 1
 
0.2%
20210114181410 1
 
0.2%
20210916152813 1
 
0.2%
20180813072550 1
 
0.2%
20190524143630 1
 
0.2%
20180616200215 1
 
0.2%
20190811172024 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
20180201090438 1
0.2%
20180202151740 1
0.2%
20180203134825 1
0.2%
20180221180812 1
0.2%
20180223123033 1
0.2%
20180226121526 1
0.2%
20180228142746 1
0.2%
20180306204829 1
0.2%
20180311160855 1
0.2%
20180311195955 1
0.2%
ValueCountFrequency (%)
20211030084417 1
0.2%
20211028151954 1
0.2%
20211026135817 1
0.2%
20211025135757 1
0.2%
20211025114622 1
0.2%
20211023110942 1
0.2%
20211022173118 1
0.2%
20211022095935 1
0.2%
20211019044911 1
0.2%
20211016221041 1
0.2%

승차역ID(GETON_STATION_ID)
Real number (ℝ)

Distinct227
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1537.228
Minimum150
Maximum4711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:49:47.655965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile156
Q1232
median422.5
Q32717
95-th percentile4125
Maximum4711
Range4561
Interquartile range (IQR)2485

Descriptive statistics

Standard deviation1436.7031
Coefficient of variation (CV)0.93460636
Kurtosis-1.2080195
Mean1537.228
Median Absolute Deviation (MAD)268.5
Skewness0.4974467
Sum768614
Variance2064115.7
MonotonicityNot monotonic
2024-01-14T15:49:47.830286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 12
 
2.4%
329 9
 
1.8%
214 9
 
1.8%
331 9
 
1.8%
220 8
 
1.6%
339 8
 
1.6%
216 7
 
1.4%
425 6
 
1.2%
4125 6
 
1.2%
226 6
 
1.2%
Other values (217) 420
84.0%
ValueCountFrequency (%)
150 12
2.4%
151 1
 
0.2%
152 4
 
0.8%
153 1
 
0.2%
154 3
 
0.6%
155 3
 
0.6%
156 2
 
0.4%
157 3
 
0.6%
158 3
 
0.6%
201 1
 
0.2%
ValueCountFrequency (%)
4711 1
0.2%
4709 2
0.4%
4708 1
0.2%
4707 1
0.2%
4705 2
0.4%
4704 1
0.2%
4703 1
0.2%
4138 1
0.2%
4133 1
0.2%
4130 1
0.2%

하차일시(GETOFF_DATETIME)
Text

Distinct465
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-01-14T15:49:48.006354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.064
Min length1

Characters and Unicode

Total characters6532
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

Unique464 ?
Unique (%)92.8%

Sample

1st row20200807182143
2nd row20190118094257
3rd row20211030165302
4th row20200223091835
5th row20210818170710
ValueCountFrequency (%)
36
 
7.2%
20181028152400 1
 
0.2%
20200611141529 1
 
0.2%
20210916094449 1
 
0.2%
20180813193520 1
 
0.2%
20190524180816 1
 
0.2%
20180616185652 1
 
0.2%
20190811063007 1
 
0.2%
20190303145050 1
 
0.2%
20200312153957 1
 
0.2%
Other values (455) 455
91.0%
2024-01-14T15:49:48.370574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1556
23.8%
1 1323
20.3%
2 1300
19.9%
5 391
 
6.0%
3 383
 
5.9%
4 368
 
5.6%
9 359
 
5.5%
8 356
 
5.5%
6 233
 
3.6%
7 227
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6496
99.4%
Math Symbol 36
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1556
24.0%
1 1323
20.4%
2 1300
20.0%
5 391
 
6.0%
3 383
 
5.9%
4 368
 
5.7%
9 359
 
5.5%
8 356
 
5.5%
6 233
 
3.6%
7 227
 
3.5%
Math Symbol
ValueCountFrequency (%)
~ 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6532
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1556
23.8%
1 1323
20.3%
2 1300
19.9%
5 391
 
6.0%
3 383
 
5.9%
4 368
 
5.6%
9 359
 
5.5%
8 356
 
5.5%
6 233
 
3.6%
7 227
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1556
23.8%
1 1323
20.3%
2 1300
19.9%
5 391
 
6.0%
3 383
 
5.9%
4 368
 
5.6%
9 359
 
5.5%
8 356
 
5.5%
6 233
 
3.6%
7 227
 
3.5%

하차역ID(GETOFF_STATION_ID)
Text

Distinct213
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-01-14T15:49:49.069247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.814
Min length1

Characters and Unicode

Total characters1907
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

Unique100 ?
Unique (%)20.0%

Sample

1st row0317
2nd row0229
3rd row0319
4th row4115
5th row0310
ValueCountFrequency (%)
31
 
6.2%
0329 14
 
2.8%
0216 11
 
2.2%
0331 11
 
2.2%
0150 10
 
2.0%
0221 8
 
1.6%
2549 8
 
1.6%
0222 7
 
1.4%
2543 7
 
1.4%
0223 7
 
1.4%
Other values (203) 386
77.2%
2024-01-14T15:49:49.751232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 449
23.5%
0 354
18.6%
1 236
12.4%
3 196
10.3%
4 187
9.8%
5 140
 
7.3%
7 106
 
5.6%
6 83
 
4.4%
9 63
 
3.3%
8 62
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1876
98.4%
Math Symbol 31
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 449
23.9%
0 354
18.9%
1 236
12.6%
3 196
10.4%
4 187
10.0%
5 140
 
7.5%
7 106
 
5.7%
6 83
 
4.4%
9 63
 
3.4%
8 62
 
3.3%
Math Symbol
ValueCountFrequency (%)
~ 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1907
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 449
23.5%
0 354
18.6%
1 236
12.4%
3 196
10.3%
4 187
9.8%
5 140
 
7.3%
7 106
 
5.6%
6 83
 
4.4%
9 63
 
3.3%
8 62
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1907
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 449
23.5%
0 354
18.6%
1 236
12.4%
3 196
10.3%
4 187
9.8%
5 140
 
7.3%
7 106
 
5.6%
6 83
 
4.4%
9 63
 
3.3%
8 62
 
3.3%

이용거리(DISTANCE)
Real number (ℝ)

ZEROS 

Distinct190
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8087.8
Minimum0
Maximum36600
Zeros36
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:49:49.917698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13200
median6550
Q311425
95-th percentile20830
Maximum36600
Range36600
Interquartile range (IQR)8225

Descriptive statistics

Standard deviation6529.4885
Coefficient of variation (CV)0.80732566
Kurtosis1.8624138
Mean8087.8
Median Absolute Deviation (MAD)4150
Skewness1.2130392
Sum4043900
Variance42634220
MonotonicityNot monotonic
2024-01-14T15:49:50.047749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
7.2%
2000 10
 
2.0%
6500 8
 
1.6%
4900 8
 
1.6%
1400 8
 
1.6%
3300 8
 
1.6%
11400 7
 
1.4%
3600 7
 
1.4%
7200 6
 
1.2%
9600 6
 
1.2%
Other values (180) 396
79.2%
ValueCountFrequency (%)
0 36
7.2%
700 1
 
0.2%
800 4
 
0.8%
900 4
 
0.8%
1000 4
 
0.8%
1100 1
 
0.2%
1200 2
 
0.4%
1300 5
 
1.0%
1400 8
 
1.6%
1500 1
 
0.2%
ValueCountFrequency (%)
36600 1
0.2%
36500 1
0.2%
34000 1
0.2%
33600 1
0.2%
28100 1
0.2%
27200 1
0.2%
26900 1
0.2%
26700 1
0.2%
25700 1
0.2%
24400 1
0.2%

Interactions

2024-01-14T15:49:45.460698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:44.269376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:44.686184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:45.082047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:45.609124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:44.398468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:44.788191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:45.165744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:45.780089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:44.488265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:44.895638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:45.259351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:45.908827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:44.586253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:44.983831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:45.343107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:49:50.136070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용자구분코드(USER_TYPE)발생사ID(COMPANY_ID)승차일시(GETON_DATETIME)승차역ID(GETON_STATION_ID)이용거리(DISTANCE)
사용자구분코드(USER_TYPE)1.0000.0980.0000.0000.000
발생사ID(COMPANY_ID)0.0981.0000.0000.1550.033
승차일시(GETON_DATETIME)0.0000.0001.0000.0000.000
승차역ID(GETON_STATION_ID)0.0000.1550.0001.0000.000
이용거리(DISTANCE)0.0000.0330.0000.0001.000
2024-01-14T15:49:50.263185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용자구분코드(USER_TYPE)승차일시(GETON_DATETIME)승차역ID(GETON_STATION_ID)이용거리(DISTANCE)발생사ID(COMPANY_ID)
사용자구분코드(USER_TYPE)1.000-0.0100.0040.0010.065
승차일시(GETON_DATETIME)-0.0101.000-0.005-0.0740.000
승차역ID(GETON_STATION_ID)0.004-0.0051.0000.0250.097
이용거리(DISTANCE)0.001-0.0740.0251.0000.032
발생사ID(COMPANY_ID)0.0650.0000.0970.0321.000

Missing values

2024-01-14T15:49:46.078196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:49:46.235745image/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

카드번호(CARD_ID)사용자구분코드(USER_TYPE)발생사ID(COMPANY_ID)승차일시(GETON_DATETIME)승차역ID(GETON_STATION_ID)하차일시(GETOFF_DATETIME)하차역ID(GETOFF_STATION_ID)이용거리(DISTANCE)
0D*0*3*6*4*B*E*A*11C9000082020080716151641242020080718214303171900
1D*0*3*9*0*8*6*4*23C9000082019011814510528222019011809425702299300
2D*0*3*6*8*0*8*6*11C900008202110300844174092021103016530203191800
3D*0*3*9*0*8*8*B*11C900008202002231424424252020022309183541156500
4D*0*3*9*0*8*4*9*12C9000012021081810310126162021081817071003102000
5D*0*3*6*D*1*9*3*23C9000012020122313550727422020122316480104294900
6D*0*3*9*1*4*1*5*11C900008201910191435352162019101918502502220
7D*0*3*9*0*D*5*A*21C90000820210226101709411020210226170946033112200
8D*0*3*6*8*6*A*A*21C9000082021051609290841052021051606564402107600
9D*0*3*9*1*D*5*8*21C9000012020112613204525152020112616200603310
카드번호(CARD_ID)사용자구분코드(USER_TYPE)발생사ID(COMPANY_ID)승차일시(GETON_DATETIME)승차역ID(GETON_STATION_ID)하차일시(GETOFF_DATETIME)하차역ID(GETOFF_STATION_ID)이용거리(DISTANCE)
490D*0*3*7*C*B*B*2*23C9000012019052510320431820190525130510042319200
491D*0*3*6*D*7*A*D*11C900008202004301354522262020043007320202116400
492D*0*3*9*1*C*A*7*12C9000082021052011353021520210520125726271712600
493D*0*3*6*A*5*F*E*23C90000120190510143308262320190510092444264011400
494D*0*3*6*6*3*1*A*12C900001201803062048293292018030621102426281300
495D*0*3*6*4*2*7*0*12C9000012019100115123627582019100108060104269700
496D*0*3*6*7*1*E*0*23C9000012019032407345125582019032418074604205000
497D*0*3*6*6*D*A*1*23C900001201912111807231542019121123573101505300
498D*0*3*9*0*C*8*4*11C9000082018082809382023120180828073847251722900
499D*0*3*6*A*F*2*2*11C900008202006011600452727~04297200