Overview

Dataset statistics

Number of variables21
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.0 KiB
Average record size in memory182.3 B

Variable types

Text5
Numeric10
Categorical6

Dataset

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

Alerts

교통카드사용자구분코드3(BILL_USER_GBN3) has constant value ""Constant
승객수2(PASSN_CNT2) has constant value ""Constant
승객수3(PASSN_CNT3) has constant value ""Constant
교통카드사용자구분코드2(BILL_USER_GBN2) is highly imbalanced (97.9%)Imbalance
승객수1(PASSN_CNT1) is highly imbalanced (94.7%)Imbalance
카드번호(CARD_ID) has unique valuesUnique
승차일시(GETON_DATETIME) has unique valuesUnique
하차일시(GETOFF_DATETIME) has unique valuesUnique
이용거리(MOV_DIST) has 15 (3.0%) zerosZeros

Reproduction

Analysis started2024-04-17 19:24:49.172070
Analysis finished2024-04-17 19:24:49.379621
Duration0.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

카드번호(CARD_ID)
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T04:24:49.516142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length44
Mean length43.92
Min length24

Characters and Unicode

Total characters21960
Distinct characters66
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowi*/*L*h*t*4*v*0*c*x*L*A*O*+*I*V*I*u*8*I*Z*0*
2nd row9*D*N*I*Y*A*8*9*c*5*E*u*E*T*C*U*+*G*q*9*J*I*
3rd rowV*C*B*4*X*v*M*S*+*K*Z*w*+*N*5*0*h*0*i*F*E*M*
4th rowi*2*i*q*+*O*A*/*u*e*E*c*i*Q*R*5*O*2*u*T*M*c*
5th rowa*D*v*7*7*M*m*Y*3*I*f*3*G*U*a*/*7*T*b*z*a*k*
ValueCountFrequency (%)
i*/*l*h*t*4*v*0*c*x*l*a*o*+*i*v*i*u*8*i*z*0 1
 
0.2%
p*e*w*j*s*l*s*w*a*j*p*x*j*c*n*3*p*h*6*j*a*8 1
 
0.2%
y*+*m*o*2*e*h*3*w*b*y*q*k*i*u*v*5*g*j*5*a*q 1
 
0.2%
j*j*x*y*l*u*n*b*n*h*d*9*/*p*n*e*a*k*u*0*t*0 1
 
0.2%
y*r*g*j*r*t*3*x*x*k*p*y*8*o*y*r*g*x*8*d*g*i 1
 
0.2%
p*w*m*9*x*k*a*x*e*c*f*w*p*f*s*a*z*y*t*n*u*y 1
 
0.2%
g*v*m*y*i*d*p*4*6*j*1*a*1*a*d*p*e*e*m*r*c*y 1
 
0.2%
h*v*c*y*x*r*q*s*3*d*f*u*k*g*x*2*q*4*w*t*k*m 1
 
0.2%
e*6*g*9*a*d*z*6*v*t*t*3*t*0*i*x*l*d*b*u*x*a 1
 
0.2%
0*k*4*b*t*a*a*j*i*x*j*d*i*l*4*o*y*n*m*e*k*0 1
 
0.2%
Other values (490) 490
98.0%
2024-04-18T04:24:49.811852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 10980
50.0%
8 227
 
1.0%
c 217
 
1.0%
k 207
 
0.9%
A 207
 
0.9%
Q 202
 
0.9%
I 200
 
0.9%
M 194
 
0.9%
E 194
 
0.9%
w 194
 
0.9%
Other values (56) 9138
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 11121
50.6%
Uppercase Letter 4472
20.4%
Lowercase Letter 4448
 
20.3%
Decimal Number 1764
 
8.0%
Math Symbol 155
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 217
 
4.9%
k 207
 
4.7%
w 194
 
4.4%
g 194
 
4.4%
x 190
 
4.3%
q 182
 
4.1%
a 179
 
4.0%
t 178
 
4.0%
m 174
 
3.9%
l 174
 
3.9%
Other values (16) 2559
57.5%
Uppercase Letter
ValueCountFrequency (%)
A 207
 
4.6%
Q 202
 
4.5%
I 200
 
4.5%
M 194
 
4.3%
E 194
 
4.3%
Y 189
 
4.2%
U 187
 
4.2%
N 183
 
4.1%
T 182
 
4.1%
B 173
 
3.9%
Other values (16) 2561
57.3%
Decimal Number
ValueCountFrequency (%)
8 227
12.9%
1 189
10.7%
4 188
10.7%
6 184
10.4%
0 181
10.3%
2 176
10.0%
9 166
9.4%
7 164
9.3%
3 145
8.2%
5 144
8.2%
Other Punctuation
ValueCountFrequency (%)
* 10980
98.7%
/ 141
 
1.3%
Math Symbol
ValueCountFrequency (%)
+ 153
98.7%
= 2
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 13040
59.4%
Latin 8920
40.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 217
 
2.4%
k 207
 
2.3%
A 207
 
2.3%
Q 202
 
2.3%
I 200
 
2.2%
M 194
 
2.2%
E 194
 
2.2%
w 194
 
2.2%
g 194
 
2.2%
x 190
 
2.1%
Other values (42) 6921
77.6%
Common
ValueCountFrequency (%)
* 10980
84.2%
8 227
 
1.7%
1 189
 
1.4%
4 188
 
1.4%
6 184
 
1.4%
0 181
 
1.4%
2 176
 
1.3%
9 166
 
1.3%
7 164
 
1.3%
+ 153
 
1.2%
Other values (4) 432
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 10980
50.0%
8 227
 
1.0%
c 217
 
1.0%
k 207
 
0.9%
A 207
 
0.9%
Q 202
 
0.9%
I 200
 
0.9%
M 194
 
0.9%
E 194
 
0.9%
w 194
 
0.9%
Other values (56) 9138
41.6%

운행출발일시(START_DATETIME)
Text

Distinct299
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T04:24:49.962034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length8.748
Min length1

Characters and Unicode

Total characters4374
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

Unique298 ?
Unique (%)59.6%

Sample

1st row20180224104506
2nd row20170311161320
3rd row~
4th row~
5th row20170311035835
ValueCountFrequency (%)
202
40.4%
20190315084204 1
 
0.2%
20190220110641 1
 
0.2%
20200304112839 1
 
0.2%
20180503092114 1
 
0.2%
20191114103548 1
 
0.2%
20210130052932 1
 
0.2%
20201010075620 1
 
0.2%
20210313195954 1
 
0.2%
20210408040002 1
 
0.2%
Other values (289) 289
57.8%
2024-04-18T04:24:50.206822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1061
24.3%
1 843
19.3%
2 778
17.8%
5 248
 
5.7%
3 243
 
5.6%
4 242
 
5.5%
9 228
 
5.2%
~ 202
 
4.6%
7 201
 
4.6%
8 170
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4172
95.4%
Math Symbol 202
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1061
25.4%
1 843
20.2%
2 778
18.6%
5 248
 
5.9%
3 243
 
5.8%
4 242
 
5.8%
9 228
 
5.5%
7 201
 
4.8%
8 170
 
4.1%
6 158
 
3.8%
Math Symbol
ValueCountFrequency (%)
~ 202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4374
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1061
24.3%
1 843
19.3%
2 778
17.8%
5 248
 
5.7%
3 243
 
5.6%
4 242
 
5.5%
9 228
 
5.2%
~ 202
 
4.6%
7 201
 
4.6%
8 170
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1061
24.3%
1 843
19.3%
2 778
17.8%
5 248
 
5.7%
3 243
 
5.6%
4 242
 
5.5%
9 228
 
5.2%
~ 202
 
4.6%
7 201
 
4.6%
8 170
 
3.9%

트랜잭션ID(TRANSACTION_ID)
Real number (ℝ)

Distinct135
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.718
Minimum1
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:50.319411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q125
median52
Q383
95-th percentile151
Maximum196
Range195
Interquartile range (IQR)58

Descriptive statistics

Standard deviation42.760395
Coefficient of variation (CV)0.72823316
Kurtosis1.4056075
Mean58.718
Median Absolute Deviation (MAD)28
Skewness1.1281174
Sum29359
Variance1828.4514
MonotonicityNot monotonic
2024-04-18T04:24:50.427259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 13
 
2.6%
2 11
 
2.2%
28 11
 
2.2%
22 10
 
2.0%
72 9
 
1.8%
56 8
 
1.6%
24 8
 
1.6%
52 8
 
1.6%
70 8
 
1.6%
79 8
 
1.6%
Other values (125) 406
81.2%
ValueCountFrequency (%)
1 7
1.4%
2 11
2.2%
3 4
 
0.8%
4 6
1.2%
5 5
1.0%
6 2
 
0.4%
7 4
 
0.8%
8 2
 
0.4%
9 5
1.0%
10 2
 
0.4%
ValueCountFrequency (%)
196 2
0.4%
195 1
 
0.2%
194 1
 
0.2%
193 1
 
0.2%
190 4
0.8%
188 1
 
0.2%
184 2
0.4%
182 2
0.4%
181 1
 
0.2%
179 1
 
0.2%

교통수단CD(SUDAN_CD)
Real number (ℝ)

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.952
Minimum105
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:50.521488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile105
Q1115
median120
Q3201
95-th percentile203
Maximum205
Range100
Interquartile range (IQR)86

Descriptive statistics

Standard deviation43.405642
Coefficient of variation (CV)0.28378604
Kurtosis-1.9111735
Mean152.952
Median Absolute Deviation (MAD)15
Skewness0.23042666
Sum76476
Variance1884.0498
MonotonicityNot monotonic
2024-04-18T04:24:50.596778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
201 121
24.2%
115 110
22.0%
120 109
21.8%
203 82
16.4%
105 58
11.6%
205 15
 
3.0%
121 4
 
0.8%
130 1
 
0.2%
ValueCountFrequency (%)
105 58
11.6%
115 110
22.0%
120 109
21.8%
121 4
 
0.8%
130 1
 
0.2%
201 121
24.2%
203 82
16.4%
205 15
 
3.0%
ValueCountFrequency (%)
205 15
 
3.0%
203 82
16.4%
201 121
24.2%
130 1
 
0.2%
121 4
 
0.8%
120 109
21.8%
115 110
22.0%
105 58
11.6%

환승횟수(TRANSFER_CNT)
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
357 
1
120 
2
 
18
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 357
71.4%
1 120
 
24.0%
2 18
 
3.6%
3 5
 
1.0%

Length

2024-04-18T04:24:50.681784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:24:50.753609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 357
71.4%
1 120
 
24.0%
2 18
 
3.6%
3 5
 
1.0%

버스노선ID(LINE_ID)
Text

Distinct195
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T04:24:50.971494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length4.752
Min length1

Characters and Unicode

Total characters2376
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

Unique139 ?
Unique (%)27.8%

Sample

1st row11110018
2nd row11110002
3rd row11110220
4th row11110242
5th row~
ValueCountFrequency (%)
232
46.4%
41110053 6
 
1.2%
11110270 4
 
0.8%
11110027 4
 
0.8%
11110028 3
 
0.6%
11410001 3
 
0.6%
11110224 3
 
0.6%
11110418 3
 
0.6%
11110063 3
 
0.6%
11110265 3
 
0.6%
Other values (185) 236
47.2%
2024-04-18T04:24:51.313040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1114
46.9%
0 441
 
18.6%
~ 232
 
9.8%
4 99
 
4.2%
2 95
 
4.0%
5 76
 
3.2%
3 72
 
3.0%
6 71
 
3.0%
9 65
 
2.7%
7 60
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2144
90.2%
Math Symbol 232
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1114
52.0%
0 441
 
20.6%
4 99
 
4.6%
2 95
 
4.4%
5 76
 
3.5%
3 72
 
3.4%
6 71
 
3.3%
9 65
 
3.0%
7 60
 
2.8%
8 51
 
2.4%
Math Symbol
ValueCountFrequency (%)
~ 232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1114
46.9%
0 441
 
18.6%
~ 232
 
9.8%
4 99
 
4.2%
2 95
 
4.0%
5 76
 
3.2%
3 72
 
3.0%
6 71
 
3.0%
9 65
 
2.7%
7 60
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1114
46.9%
0 441
 
18.6%
~ 232
 
9.8%
4 99
 
4.2%
2 95
 
4.0%
5 76
 
3.2%
3 72
 
3.0%
6 71
 
3.0%
9 65
 
2.7%
7 60
 
2.5%

교통사업자ID(COMPANY_ID)
Real number (ℝ)

Distinct110
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5527683 × 108
Minimum1.110002 × 108
Maximum2.412 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:51.433986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.110002 × 108
5-th percentile1.110012 × 108
Q11.1100533 × 108
median1.1151057 × 108
Q32.11 × 108
95-th percentile2.12 × 108
Maximum2.412 × 108
Range1.301998 × 108
Interquartile range (IQR)99994674

Descriptive statistics

Standard deviation50176811
Coefficient of variation (CV)0.32314423
Kurtosis-1.9089616
Mean1.5527683 × 108
Median Absolute Deviation (MAD)508621
Skewness0.26582792
Sum7.7638414 × 1010
Variance2.5177123 × 1015
MonotonicityNot monotonic
2024-04-18T04:24:51.553091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211000000 192
38.4%
212000000 15
 
3.0%
111004500 13
 
2.6%
111002900 10
 
2.0%
111002000 10
 
2.0%
111007100 9
 
1.8%
111002400 8
 
1.6%
111009020 8
 
1.6%
111008800 7
 
1.4%
111007800 7
 
1.4%
Other values (100) 221
44.2%
ValueCountFrequency (%)
111000201 1
 
0.2%
111000400 2
 
0.4%
111000500 4
0.8%
111000600 3
0.6%
111000700 5
1.0%
111000800 2
 
0.4%
111001001 3
0.6%
111001100 4
0.8%
111001200 4
0.8%
111001400 1
 
0.2%
ValueCountFrequency (%)
241200000 6
 
1.2%
212500000 6
 
1.2%
212000000 15
 
3.0%
211000000 192
38.4%
111554691 2
 
0.4%
111520190 2
 
0.4%
111520141 3
 
0.6%
111520046 1
 
0.2%
111520020 1
 
0.2%
111520001 2
 
0.4%

차량ID(VEHICLE_ID)
Text

Distinct287
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T04:24:51.759822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length5.672
Min length1

Characters and Unicode

Total characters2836
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

Unique280 ?
Unique (%)56.0%

Sample

1st row111753132
2nd row111758104
3rd row~
4th row~
5th row111741806
ValueCountFrequency (%)
208
41.6%
111706681 2
 
0.4%
111753209 2
 
0.4%
111741234 2
 
0.4%
111741823 2
 
0.4%
111744226 2
 
0.4%
111746214 2
 
0.4%
111748785 1
 
0.2%
111743385 1
 
0.2%
111758773 1
 
0.2%
Other values (277) 277
55.4%
2024-04-18T04:24:52.081185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1040
36.7%
7 397
 
14.0%
4 252
 
8.9%
~ 208
 
7.3%
5 168
 
5.9%
2 159
 
5.6%
3 136
 
4.8%
0 134
 
4.7%
9 122
 
4.3%
8 120
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2628
92.7%
Math Symbol 208
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1040
39.6%
7 397
 
15.1%
4 252
 
9.6%
5 168
 
6.4%
2 159
 
6.1%
3 136
 
5.2%
0 134
 
5.1%
9 122
 
4.6%
8 120
 
4.6%
6 100
 
3.8%
Math Symbol
ValueCountFrequency (%)
~ 208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2836
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1040
36.7%
7 397
 
14.0%
4 252
 
8.9%
~ 208
 
7.3%
5 168
 
5.9%
2 159
 
5.6%
3 136
 
4.8%
0 134
 
4.7%
9 122
 
4.3%
8 120
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1040
36.7%
7 397
 
14.0%
4 252
 
8.9%
~ 208
 
7.3%
5 168
 
5.9%
2 159
 
5.6%
3 136
 
4.8%
0 134
 
4.7%
9 122
 
4.3%
8 120
 
4.2%

교통카드발행사ID(CARD_COMP_ID)
Real number (ℝ)

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2357300.7
Minimum2000100
Maximum3104201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:52.181359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000100
5-th percentile2000100
Q12000600
median2000800
Q33101000
95-th percentile3101916
Maximum3104201
Range1104101
Interquartile range (IQR)1100400

Descriptive statistics

Standard deviation515794.45
Coefficient of variation (CV)0.21880724
Kurtosis-1.4366272
Mean2357300.7
Median Absolute Deviation (MAD)700
Skewness0.75440524
Sum1.1786504 × 109
Variance2.6604392 × 1011
MonotonicityNot monotonic
2024-04-18T04:24:52.271405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3101000 98
19.6%
2000100 94
18.8%
2000800 78
15.6%
2000700 59
11.8%
2000600 30
 
6.0%
2000200 23
 
4.6%
2001400 18
 
3.6%
3101915 17
 
3.4%
2001000 17
 
3.4%
2000900 15
 
3.0%
Other values (10) 51
10.2%
ValueCountFrequency (%)
2000100 94
18.8%
2000200 23
 
4.6%
2000300 4
 
0.8%
2000600 30
 
6.0%
2000700 59
11.8%
2000800 78
15.6%
2000900 15
 
3.0%
2001000 17
 
3.4%
2001400 18
 
3.6%
3101000 98
19.6%
ValueCountFrequency (%)
3104201 8
1.6%
3104016 2
 
0.4%
3104012 7
1.4%
3104011 3
 
0.6%
3104000 3
 
0.6%
3103101 1
 
0.2%
3101916 7
1.4%
3101915 17
3.4%
3101914 1
 
0.2%
3101001 15
3.0%

교통카드사용자구분코드(CARD_USER_GBN)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.714
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:52.345363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6834107
Coefficient of variation (CV)0.98215329
Kurtosis3.2011806
Mean1.714
Median Absolute Deviation (MAD)0
Skewness2.1720847
Sum857
Variance2.8338717
MonotonicityNot monotonic
2024-04-18T04:24:52.422069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 413
82.6%
6 38
 
7.6%
4 28
 
5.6%
7 10
 
2.0%
2 9
 
1.8%
8 2
 
0.4%
ValueCountFrequency (%)
1 413
82.6%
2 9
 
1.8%
4 28
 
5.6%
6 38
 
7.6%
7 10
 
2.0%
8 2
 
0.4%
ValueCountFrequency (%)
8 2
 
0.4%
7 10
 
2.0%
6 38
 
7.6%
4 28
 
5.6%
2 9
 
1.8%
1 413
82.6%

교통카드사용자구분코드1(BILL_USER_GBN1)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.538
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:52.716772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5156353
Coefficient of variation (CV)0.98545857
Kurtosis5.2257962
Mean1.538
Median Absolute Deviation (MAD)0
Skewness2.6162003
Sum769
Variance2.2971503
MonotonicityNot monotonic
2024-04-18T04:24:52.792639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 440
88.0%
6 37
 
7.4%
4 15
 
3.0%
7 5
 
1.0%
2 2
 
0.4%
8 1
 
0.2%
ValueCountFrequency (%)
1 440
88.0%
2 2
 
0.4%
4 15
 
3.0%
6 37
 
7.4%
7 5
 
1.0%
8 1
 
0.2%
ValueCountFrequency (%)
8 1
 
0.2%
7 5
 
1.0%
6 37
 
7.4%
4 15
 
3.0%
2 2
 
0.4%
1 440
88.0%

교통카드사용자구분코드2(BILL_USER_GBN2)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
~
499 
02
 
1

Length

Max length2
Median length1
Mean length1.002
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row~
2nd row~
3rd row~
4th row~
5th row~

Common Values

ValueCountFrequency (%)
~ 499
99.8%
02 1
 
0.2%

Length

2024-04-18T04:24:52.884665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:24:52.954311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
499
99.8%
02 1
 
0.2%

교통카드사용자구분코드3(BILL_USER_GBN3)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
~
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row~
2nd row~
3rd row~
4th row~
5th row~

Common Values

ValueCountFrequency (%)
~ 500
100.0%

Length

2024-04-18T04:24:53.027203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:24:53.092728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500
100.0%

승차일시(GETON_DATETIME)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190392 × 1013
Minimum2.0170105 × 1013
Maximum2.0211031 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:53.172905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0170105 × 1013
5-th percentile2.0170408 × 1013
Q12.0180525 × 1013
median2.0190526 × 1013
Q32.0200924 × 1013
95-th percentile2.0210805 × 1013
Maximum2.0211031 × 1013
Range4.0926033 × 1010
Interquartile range (IQR)2.0398553 × 1010

Descriptive statistics

Standard deviation1.3590509 × 1010
Coefficient of variation (CV)0.00067311762
Kurtosis-1.2079948
Mean2.0190392 × 1013
Median Absolute Deviation (MAD)1.019751 × 1010
Skewness0.0089018859
Sum1.0095196 × 1016
Variance1.8470193 × 1020
MonotonicityNot monotonic
2024-04-18T04:24:53.279794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180224171619 1
 
0.2%
20180510091937 1
 
0.2%
20180503181342 1
 
0.2%
20191114160924 1
 
0.2%
20210130165242 1
 
0.2%
20201010085653 1
 
0.2%
20210313110535 1
 
0.2%
20201025200331 1
 
0.2%
20170119232524 1
 
0.2%
20210408220704 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
20170105111029 1
0.2%
20170112202720 1
0.2%
20170114082455 1
0.2%
20170115084504 1
0.2%
20170117065450 1
0.2%
20170119232524 1
0.2%
20170129082509 1
0.2%
20170202211115 1
0.2%
20170203130311 1
0.2%
20170220130637 1
0.2%
ValueCountFrequency (%)
20211031144354 1
0.2%
20211025103513 1
0.2%
20211023070954 1
0.2%
20211021200923 1
0.2%
20211018072154 1
0.2%
20211017170505 1
0.2%
20211016195413 1
0.2%
20211014061401 1
0.2%
20211012180129 1
0.2%
20211011184507 1
0.2%

승차정류장ID(GETON_STATION_ID)
Real number (ℝ)

Distinct426
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2824575.6
Minimum150
Maximum9614006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:53.400908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile215.95
Q12530.25
median9597
Q38077521.8
95-th percentile9033596.1
Maximum9614006
Range9613856
Interquartile range (IQR)8074991.5

Descriptive statistics

Standard deviation4074094.4
Coefficient of variation (CV)1.442374
Kurtosis-1.3920499
Mean2824575.6
Median Absolute Deviation (MAD)9376.5
Skewness0.77083954
Sum1.4122878 × 109
Variance1.6598245 × 1013
MonotonicityNot monotonic
2024-04-18T04:24:53.515208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 5
 
1.0%
414 4
 
0.8%
234 4
 
0.8%
2549 4
 
0.8%
318 3
 
0.6%
219 3
 
0.6%
2623 3
 
0.6%
212 3
 
0.6%
2519 3
 
0.6%
2527 3
 
0.6%
Other values (416) 465
93.0%
ValueCountFrequency (%)
150 1
0.2%
151 1
0.2%
153 2
0.4%
154 1
0.2%
155 1
0.2%
201 2
0.4%
202 2
0.4%
203 1
0.2%
204 2
0.4%
207 1
0.2%
ValueCountFrequency (%)
9614006 1
0.2%
9107213 1
0.2%
9107113 1
0.2%
9105348 1
0.2%
9036973 1
0.2%
9036732 1
0.2%
9036640 1
0.2%
9036626 1
0.2%
9036128 1
0.2%
9036053 2
0.4%

하차일시(GETOFF_DATETIME)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190392 × 1013
Minimum2.0170105 × 1013
Maximum2.0211031 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:53.625571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0170105 × 1013
5-th percentile2.0170408 × 1013
Q12.0180525 × 1013
median2.0190526 × 1013
Q32.0200924 × 1013
95-th percentile2.0210805 × 1013
Maximum2.0211031 × 1013
Range4.0925939 × 1010
Interquartile range (IQR)2.0398504 × 1010

Descriptive statistics

Standard deviation1.3590506 × 1010
Coefficient of variation (CV)0.00067311748
Kurtosis-1.2079944
Mean2.0190392 × 1013
Median Absolute Deviation (MAD)1.0197475 × 1010
Skewness0.0089018095
Sum1.0095196 × 1016
Variance1.8470186 × 1020
MonotonicityNot monotonic
2024-04-18T04:24:53.732343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180224091658 1
 
0.2%
20180510074708 1
 
0.2%
20180503100201 1
 
0.2%
20191114183123 1
 
0.2%
20210130202416 1
 
0.2%
20201010165556 1
 
0.2%
20210313065347 1
 
0.2%
20201025081828 1
 
0.2%
20170119082223 1
 
0.2%
20210408062858 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
20170105141401 1
0.2%
20170112183907 1
0.2%
20170114190628 1
0.2%
20170115075010 1
0.2%
20170117174245 1
0.2%
20170119082223 1
0.2%
20170129225820 1
0.2%
20170202092735 1
0.2%
20170203185927 1
0.2%
20170220222654 1
0.2%
ValueCountFrequency (%)
20211031080121 1
0.2%
20211025230913 1
0.2%
20211023191801 1
0.2%
20211021170443 1
0.2%
20211018180936 1
0.2%
20211017183145 1
0.2%
20211016155912 1
0.2%
20211014150720 1
0.2%
20211012184241 1
0.2%
20211011070649 1
0.2%

하차정류장ID(GETOFF_STATION_ID)
Text

Distinct395
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-18T04:24:53.996486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.572
Min length1

Characters and Unicode

Total characters2786
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

Unique328 ?
Unique (%)65.6%

Sample

1st row4123
2nd row0205
3rd row0074931
4th row8000471
5th row0319
ValueCountFrequency (%)
6
 
1.2%
0234 6
 
1.2%
2527 5
 
1.0%
0226 5
 
1.0%
0230 5
 
1.0%
2727 5
 
1.0%
2733 4
 
0.8%
0222 4
 
0.8%
0009687 3
 
0.6%
0221 3
 
0.6%
Other values (385) 454
90.8%
2024-04-18T04:24:54.366014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 780
28.0%
2 381
13.7%
1 323
11.6%
9 228
 
8.2%
7 225
 
8.1%
3 200
 
7.2%
5 188
 
6.7%
4 175
 
6.3%
8 151
 
5.4%
6 129
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2780
99.8%
Math Symbol 6
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 780
28.1%
2 381
13.7%
1 323
11.6%
9 228
 
8.2%
7 225
 
8.1%
3 200
 
7.2%
5 188
 
6.8%
4 175
 
6.3%
8 151
 
5.4%
6 129
 
4.6%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 780
28.0%
2 381
13.7%
1 323
11.6%
9 228
 
8.2%
7 225
 
8.1%
3 200
 
7.2%
5 188
 
6.7%
4 175
 
6.3%
8 151
 
5.4%
6 129
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 780
28.0%
2 381
13.7%
1 323
11.6%
9 228
 
8.2%
7 225
 
8.1%
3 200
 
7.2%
5 188
 
6.7%
4 175
 
6.3%
8 151
 
5.4%
6 129
 
4.6%

승객수1(PASSN_CNT1)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
497 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 497
99.4%
2 3
 
0.6%

Length

2024-04-18T04:24:54.476608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:24:54.556708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 497
99.4%
2 3
 
0.6%

승객수2(PASSN_CNT2)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 500
100.0%

Length

2024-04-18T04:24:54.628380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:24:54.692459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%

승객수3(PASSN_CNT3)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 500
100.0%

Length

2024-04-18T04:24:54.759145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:24:54.829393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%

이용거리(MOV_DIST)
Real number (ℝ)

ZEROS 

Distinct403
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5785.846
Minimum0
Maximum29600
Zeros15
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-18T04:24:54.905605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile446.9
Q11647.75
median3772.5
Q38400
95-th percentile17600
Maximum29600
Range29600
Interquartile range (IQR)6752.25

Descriptive statistics

Standard deviation5639.7967
Coefficient of variation (CV)0.97475749
Kurtosis1.8436774
Mean5785.846
Median Absolute Deviation (MAD)2566.5
Skewness1.4670567
Sum2892923
Variance31807307
MonotonicityNot monotonic
2024-04-18T04:24:55.027371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
3.0%
4400 6
 
1.2%
5800 5
 
1.0%
2900 5
 
1.0%
5000 4
 
0.8%
7900 4
 
0.8%
5200 4
 
0.8%
14200 4
 
0.8%
8400 4
 
0.8%
6000 3
 
0.6%
Other values (393) 446
89.2%
ValueCountFrequency (%)
0 15
3.0%
203 1
 
0.2%
220 1
 
0.2%
225 1
 
0.2%
235 1
 
0.2%
263 1
 
0.2%
319 1
 
0.2%
373 1
 
0.2%
425 1
 
0.2%
445 2
 
0.4%
ValueCountFrequency (%)
29600 1
0.2%
28300 1
0.2%
25400 1
0.2%
24900 1
0.2%
24700 1
0.2%
24100 1
0.2%
24000 1
0.2%
22400 1
0.2%
22100 1
0.2%
21800 1
0.2%

Sample

카드번호(CARD_ID)운행출발일시(START_DATETIME)트랜잭션ID(TRANSACTION_ID)교통수단CD(SUDAN_CD)환승횟수(TRANSFER_CNT)버스노선ID(LINE_ID)교통사업자ID(COMPANY_ID)차량ID(VEHICLE_ID)교통카드발행사ID(CARD_COMP_ID)교통카드사용자구분코드(CARD_USER_GBN)교통카드사용자구분코드1(BILL_USER_GBN1)교통카드사용자구분코드2(BILL_USER_GBN2)교통카드사용자구분코드3(BILL_USER_GBN3)승차일시(GETON_DATETIME)승차정류장ID(GETON_STATION_ID)하차일시(GETOFF_DATETIME)하차정류장ID(GETOFF_STATION_ID)승객수1(PASSN_CNT1)승객수2(PASSN_CNT2)승객수3(PASSN_CNT3)이용거리(MOV_DIST)
0i*/*L*h*t*4*v*0*c*x*L*A*O*+*I*V*I*u*8*I*Z*0*2018022410450656120011110018211000000111753132310100016~~2018022417161976512018022409165841231003541
19*D*N*I*Y*A*8*9*c*5*E*u*E*T*C*U*+*G*q*9*J*I*2017031116132059120211110002111005400111758104200060011~~20170311221446411320170311143121020510012000
2V*C*B*4*X*v*M*S*+*K*Z*w*+*N*5*0*h*0*i*F*E*M*~84105011110220211000000~310100011~~201803280550001140020180328221154007493110013500
3i*2*i*q*+*O*A*/*u*e*E*c*i*Q*R*5*O*2*u*T*M*c*~25203111110242211000000~310100011~~20191102171442755122019110221471880004711001179
4a*D*v*7*7*M*m*Y*3*I*f*3*G*U*a*/*7*T*b*z*a*k*20170311035835292030~111001001111741806200080011~~201703110757499406201703111509250319100562
5v*9*2*i*x*9*N*6*D*7*y*m*u*y*2*z*8*b*0*3*d*8*~861050~211000000111748396200010071~~20181023095422722472018102316274785012791003857
6i*s*J*x*Z*F*i*p*y*S*V*T*t*6*I*F*x*U*Y*b*K*Y*2017122219011189120111110249211000000~200070061~~2017122217350994322017122219455700714271002339
7+*4*f*S*X*O*6*x*O*w*J*t*x*x*c*8*d*B*p*D*6*c*20210531152617681150~111002100~200010011~~20210531151959900912320210531185027256310015600
8T*3*i*l*R*q*5*L*n*Q*Y*H*O*z*d*T*F*6*p*G*R*8*20190324140527281150~111006100111749528200080011~~20190324182359903605320190324083807001082710016900
9x*t*H*0*V*+*j*4*q*o*B*P*a*m*w*s*W*H*E*h*a*Y*2021081904444386120111110296111000400111711462200010011~~202108191159171000720210819110955001139810014001
카드번호(CARD_ID)운행출발일시(START_DATETIME)트랜잭션ID(TRANSACTION_ID)교통수단CD(SUDAN_CD)환승횟수(TRANSFER_CNT)버스노선ID(LINE_ID)교통사업자ID(COMPANY_ID)차량ID(VEHICLE_ID)교통카드발행사ID(CARD_COMP_ID)교통카드사용자구분코드(CARD_USER_GBN)교통카드사용자구분코드1(BILL_USER_GBN1)교통카드사용자구분코드2(BILL_USER_GBN2)교통카드사용자구분코드3(BILL_USER_GBN3)승차일시(GETON_DATETIME)승차정류장ID(GETON_STATION_ID)하차일시(GETOFF_DATETIME)하차정류장ID(GETOFF_STATION_ID)승객수1(PASSN_CNT1)승객수2(PASSN_CNT2)승객수3(PASSN_CNT3)이용거리(MOV_DIST)
490T*L*7*h*T*1*j*5*q*G*I*/*j*7*n*2*k*D*3*s*j*g*202004201919497121011110202111001001~200080011~~20200420130554720122020042016480502031002600
491l*A*Q*v*O*O*+*j*I*E*1*/*z*R*5*5*r*C*3*S*0*4*20180804062708114105111110178111009030111744199200060011~~20180804124305850120020180804181140023310018000
492L*u*U*w*V*b*c*W*+*Q*U*8*G*M*8*x*w*3*A*u*E*w*2017040605435479201211110063211000000~200070011~~201704062140054332017040615245302301002900
493I*m*R*C*P*4*P*j*K*2*l*J*/*M*K*+*4*i*0*I*x*U*20200423101517931201~211000000111751323310100016~~202004230737104302020042309103080021861001156
494M*F*G*X*c*f*W*L*d*z*B*5*Z*R*+*V*v*g*i*N*o*Q*~12201041110044211000000~200070026~~2021081408300640920210814181128007512610012100
495B*8*2*y*4*1*T*6*J*b*7*/*P*1*r*m*K*4*C*A*Z*k*201808241349281902010~111004100~310191611~~20180824135901107082018082406334525461004472
496C*G*I*8*M*L*K*t*u*W*O*x*/*X*K*m*1*J*n*R*4*0*~75203111110268111500220~200080011~~2019020815223280010152019020815014585014401003395
4977*V*v*S*m*v*8*Y*B*M*F*8*/*4*7*V*D*X*s*2*e*8*2019101914113569201011110555111006100~200070061~~2019101906082841122019101917192027271002000
4986*Y*i*9*f*4*k*v*3*W*c*2*A*f*G*1*4*I*0*W*c*A*~151200~111008800111741823200080011~~20170815142944255720170815132410007573610012739
499P*Z*L*t*x*g*X*E*m*j*Z*m*8*Q*2*G*Z*/*8*s*m*0*~32010~211000000~200140014~~2018072307283398732018072318423502211006300