Overview

Dataset statistics

Number of variables12
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.4 KiB
Average record size in memory107.3 B

Variable types

Numeric11
Text1

Dataset

Description샘플 데이터
Author신한은행
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=320

Reproduction

Analysis started2023-12-10 14:57:03.766287
Analysis finished2023-12-10 14:57:26.734124
Duration22.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월(BASE_YYMM)
Real number (ℝ)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201906.88
Minimum201901
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:26.844388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201901
5-th percentile201901
Q1201904
median201907
Q3201910
95-th percentile201912
Maximum201912
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5820752
Coefficient of variation (CV)1.7741224 × 10-5
Kurtosis-1.284381
Mean201906.88
Median Absolute Deviation (MAD)3
Skewness-0.1118095
Sum1.0095344 × 108
Variance12.831263
MonotonicityNot monotonic
2023-12-10T23:57:27.070748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
201912 58
11.6%
201911 56
11.2%
201908 46
9.2%
201906 41
8.2%
201903 40
8.0%
201909 39
7.8%
201901 39
7.8%
201902 38
7.6%
201905 38
7.6%
201910 38
7.6%
Other values (2) 67
13.4%
ValueCountFrequency (%)
201901 39
7.8%
201902 38
7.6%
201903 40
8.0%
201904 37
7.4%
201905 38
7.6%
201906 41
8.2%
201907 30
6.0%
201908 46
9.2%
201909 39
7.8%
201910 38
7.6%
ValueCountFrequency (%)
201912 58
11.6%
201911 56
11.2%
201910 38
7.6%
201909 39
7.8%
201908 46
9.2%
201907 30
6.0%
201906 41
8.2%
201905 38
7.6%
201904 37
7.4%
201903 40
8.0%
Distinct220
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:57:27.702558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters12
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

Unique74 ?
Unique (%)14.8%

Sample

1st rowG*0*2*6*5*
2nd rowG*0*2*1*1*
3rd rowG*0*0*0*1*
4th rowG*0*2*7*8*
5th rowG*0*1*6*4*
ValueCountFrequency (%)
g*0*0*7*9 7
 
1.4%
g*0*0*6*4 6
 
1.2%
g*0*0*7*6 6
 
1.2%
g*0*1*8*2 6
 
1.2%
g*0*1*6*0 6
 
1.2%
g*0*0*6*2 6
 
1.2%
g*0*0*5*2 5
 
1.0%
g*0*1*2*7 5
 
1.0%
g*0*1*9*5 5
 
1.0%
g*0*1*6*3 5
 
1.0%
Other values (210) 443
88.6%
2023-12-10T23:57:28.597252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 2500
50.0%
0 811
 
16.2%
G 500
 
10.0%
1 327
 
6.5%
2 167
 
3.3%
7 114
 
2.3%
6 113
 
2.3%
3 99
 
2.0%
4 98
 
2.0%
9 94
 
1.9%
Other values (2) 177
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 2500
50.0%
Decimal Number 2000
40.0%
Uppercase Letter 500
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 811
40.6%
1 327
16.4%
2 167
 
8.3%
7 114
 
5.7%
6 113
 
5.7%
3 99
 
5.0%
4 98
 
4.9%
9 94
 
4.7%
8 90
 
4.5%
5 87
 
4.3%
Other Punctuation
ValueCountFrequency (%)
* 2500
100.0%
Uppercase Letter
ValueCountFrequency (%)
G 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4500
90.0%
Latin 500
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 2500
55.6%
0 811
 
18.0%
1 327
 
7.3%
2 167
 
3.7%
7 114
 
2.5%
6 113
 
2.5%
3 99
 
2.2%
4 98
 
2.2%
9 94
 
2.1%
8 90
 
2.0%
Latin
ValueCountFrequency (%)
G 500
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 2500
50.0%
0 811
 
16.2%
G 500
 
10.0%
1 327
 
6.5%
2 167
 
3.3%
7 114
 
2.3%
6 113
 
2.3%
3 99
 
2.0%
4 98
 
2.0%
9 94
 
1.9%
Other values (2) 177
 
3.5%
Distinct169
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.524
Minimum8
Maximum507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:28.926759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12
Q131
median57
Q390
95-th percentile185.05
Maximum507
Range499
Interquartile range (IQR)59

Descriptive statistics

Standard deviation65.39151
Coefficient of variation (CV)0.88938999
Kurtosis10.911595
Mean73.524
Median Absolute Deviation (MAD)28
Skewness2.7390326
Sum36762
Variance4276.0495
MonotonicityNot monotonic
2023-12-10T23:57:29.267263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 12
 
2.4%
11 11
 
2.2%
12 10
 
2.0%
69 9
 
1.8%
50 8
 
1.6%
13 8
 
1.6%
54 7
 
1.4%
71 7
 
1.4%
14 7
 
1.4%
20 7
 
1.4%
Other values (159) 414
82.8%
ValueCountFrequency (%)
8 1
 
0.2%
9 2
 
0.4%
10 4
 
0.8%
11 11
2.2%
12 10
2.0%
13 8
1.6%
14 7
1.4%
15 5
1.0%
16 5
1.0%
17 4
 
0.8%
ValueCountFrequency (%)
507 1
0.2%
461 1
0.2%
446 1
0.2%
371 1
0.2%
352 1
0.2%
341 1
0.2%
336 1
0.2%
333 1
0.2%
314 1
0.2%
296 1
0.2%
Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3512371 × 108
Minimum423491.87
Maximum3.21753 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:29.592680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum423491.87
5-th percentile15562443
Q198661033
median2.0461594 × 108
Q33.9724026 × 108
95-th percentile1.1125473 × 109
Maximum3.21753 × 109
Range3.2171065 × 109
Interquartile range (IQR)2.9857923 × 108

Descriptive statistics

Standard deviation4.2910024 × 108
Coefficient of variation (CV)1.2804234
Kurtosis13.208623
Mean3.3512371 × 108
Median Absolute Deviation (MAD)1.3262942 × 108
Skewness3.2535744
Sum1.6756186 × 1011
Variance1.8412702 × 1017
MonotonicityNot monotonic
2023-12-10T23:57:29.897907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12134284.8696 1
 
0.2%
390965620.059 1
 
0.2%
85825970.058 1
 
0.2%
166344077.606 1
 
0.2%
344012081.902 1
 
0.2%
46352582.72 1
 
0.2%
162095412.04 1
 
0.2%
2406586901.158 1
 
0.2%
522425357.859 1
 
0.2%
573425848.944 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
423491.86998 1
0.2%
3283774.11 1
0.2%
3312003.3008 1
0.2%
3366440.07 1
0.2%
3990675.2295 1
0.2%
4367695.4805 1
0.2%
4420352.0598 1
0.2%
4551278.7008 1
0.2%
4599366.0603 1
0.2%
6496487.79 1
0.2%
ValueCountFrequency (%)
3217529972.25 1
0.2%
2823043370.789 1
0.2%
2764818451.008 1
0.2%
2536002562.64 1
0.2%
2406586901.158 1
0.2%
2346889157.97 1
0.2%
2167476846.0 1
0.2%
2106131939.58 1
0.2%
2052703818.111 1
0.2%
2042367658.972 1
0.2%
Distinct43
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.678
Minimum0
Maximum67
Zeros175
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:30.225067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q315
95-th percentile34
Maximum67
Range67
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.912772
Coefficient of variation (CV)1.115637
Kurtosis3.5780515
Mean10.678
Median Absolute Deviation (MAD)8
Skewness1.6377449
Sum5339
Variance141.91414
MonotonicityNot monotonic
2023-12-10T23:57:30.519094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 175
35.0%
6 31
 
6.2%
7 30
 
6.0%
8 25
 
5.0%
12 21
 
4.2%
14 19
 
3.8%
10 19
 
3.8%
11 19
 
3.8%
13 17
 
3.4%
17 11
 
2.2%
Other values (33) 133
26.6%
ValueCountFrequency (%)
0 175
35.0%
6 31
 
6.2%
7 30
 
6.0%
8 25
 
5.0%
9 11
 
2.2%
10 19
 
3.8%
11 19
 
3.8%
12 21
 
4.2%
13 17
 
3.4%
14 19
 
3.8%
ValueCountFrequency (%)
67 2
0.4%
61 1
 
0.2%
58 1
 
0.2%
57 1
 
0.2%
56 1
 
0.2%
54 1
 
0.2%
52 2
0.4%
41 2
0.4%
40 3
0.6%
39 1
 
0.2%
Distinct316
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31851641
Minimum0
Maximum5.52026 × 108
Zeros185
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:30.837576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17849788
Q336659000
95-th percentile1.3490302 × 108
Maximum5.52026 × 108
Range5.52026 × 108
Interquartile range (IQR)36659000

Descriptive statistics

Standard deviation53820430
Coefficient of variation (CV)1.6897224
Kurtosis32.553918
Mean31851641
Median Absolute Deviation (MAD)17849788
Skewness4.5702094
Sum1.592582 × 1010
Variance2.8966387 × 1015
MonotonicityNot monotonic
2023-12-10T23:57:31.112651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 185
37.0%
183953644.98 1
 
0.2%
148175089.002 1
 
0.2%
18616558.0 1
 
0.2%
31459353.996 1
 
0.2%
32426542.995 1
 
0.2%
68689631.003 1
 
0.2%
23793729.0 1
 
0.2%
29152089.996 1
 
0.2%
88909692.994 1
 
0.2%
Other values (306) 306
61.2%
ValueCountFrequency (%)
0.0 185
37.0%
4378386.0 1
 
0.2%
6382377.0 1
 
0.2%
6632930.9997 1
 
0.2%
7820643.0 1
 
0.2%
8170111.0 1
 
0.2%
9047865.0 1
 
0.2%
9079009.998 1
 
0.2%
9238800.0 1
 
0.2%
9450897.0 1
 
0.2%
ValueCountFrequency (%)
552026000.008 1
0.2%
518589555.0 1
0.2%
282491955.97 1
0.2%
240772891.008 1
0.2%
235399396.024 1
0.2%
221791750.005 1
0.2%
216286039.008 1
0.2%
203237314.014 1
0.2%
198288803.0 1
0.2%
190289312.012 1
0.2%
Distinct45
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.102
Minimum0
Maximum103
Zeros177
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:31.868547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q314
95-th percentile32.05
Maximum103
Range103
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.888746
Coefficient of variation (CV)1.1768705
Kurtosis9.3807449
Mean10.102
Median Absolute Deviation (MAD)8
Skewness2.2729796
Sum5051
Variance141.34228
MonotonicityNot monotonic
2023-12-10T23:57:32.160947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 177
35.4%
8 36
 
7.2%
6 30
 
6.0%
7 27
 
5.4%
10 27
 
5.4%
9 22
 
4.4%
12 19
 
3.8%
11 15
 
3.0%
13 14
 
2.8%
14 12
 
2.4%
Other values (35) 121
24.2%
ValueCountFrequency (%)
0 177
35.4%
6 30
 
6.0%
7 27
 
5.4%
8 36
 
7.2%
9 22
 
4.4%
10 27
 
5.4%
11 15
 
3.0%
12 19
 
3.8%
13 14
 
2.8%
14 12
 
2.4%
ValueCountFrequency (%)
103 1
0.2%
62 1
0.2%
61 1
0.2%
54 1
0.2%
52 1
0.2%
50 1
0.2%
49 1
0.2%
48 2
0.4%
46 1
0.2%
44 1
0.2%
Distinct339
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27335191
Minimum0
Maximum5.1337571 × 108
Zeros162
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:32.442488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17971898
Q333877459
95-th percentile93613342
Maximum5.1337571 × 108
Range5.1337571 × 108
Interquartile range (IQR)33877459

Descriptive statistics

Standard deviation41440931
Coefficient of variation (CV)1.5160286
Kurtosis45.615612
Mean27335191
Median Absolute Deviation (MAD)17971898
Skewness5.1546405
Sum1.3667595 × 1010
Variance1.7173508 × 1015
MonotonicityNot monotonic
2023-12-10T23:57:32.771428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 162
32.4%
136698313.0 1
 
0.2%
37408948.995 1
 
0.2%
9580096.998 1
 
0.2%
14891734.998 1
 
0.2%
15430057.998 1
 
0.2%
15420158.999 1
 
0.2%
40508563.004 1
 
0.2%
12166980.0 1
 
0.2%
13794453.0 1
 
0.2%
Other values (329) 329
65.8%
ValueCountFrequency (%)
0.0 162
32.4%
4998040.0002 1
 
0.2%
6722686.998 1
 
0.2%
7169460.998 1
 
0.2%
7323547.002 1
 
0.2%
7454851.998 1
 
0.2%
7773262.0 1
 
0.2%
7906699.998 1
 
0.2%
7976175.998 1
 
0.2%
7993937.0 1
 
0.2%
ValueCountFrequency (%)
513375711.0 1
0.2%
337161916.014 1
0.2%
216570287.029 1
0.2%
214608507.02 1
0.2%
204556672.008 1
0.2%
164288455.018 1
0.2%
142238619.009 1
0.2%
141882918.012 1
0.2%
136698313.0 1
0.2%
136293114.986 1
0.2%
Distinct88
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.466
Minimum0
Maximum236
Zeros47
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:33.049111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median24
Q341
95-th percentile82.05
Maximum236
Range236
Interquartile range (IQR)29

Descriptive statistics

Standard deviation29.916263
Coefficient of variation (CV)0.95074885
Kurtosis9.8789214
Mean31.466
Median Absolute Deviation (MAD)14
Skewness2.5119713
Sum15733
Variance894.98281
MonotonicityNot monotonic
2023-12-10T23:57:33.328768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
 
9.4%
10 17
 
3.4%
11 15
 
3.0%
12 15
 
3.0%
16 13
 
2.6%
18 13
 
2.6%
24 12
 
2.4%
27 12
 
2.4%
7 12
 
2.4%
15 11
 
2.2%
Other values (78) 333
66.6%
ValueCountFrequency (%)
0 47
9.4%
6 10
 
2.0%
7 12
 
2.4%
8 9
 
1.8%
9 11
 
2.2%
10 17
 
3.4%
11 15
 
3.0%
12 15
 
3.0%
13 6
 
1.2%
14 7
 
1.4%
ValueCountFrequency (%)
236 1
0.2%
203 1
0.2%
188 1
0.2%
178 1
0.2%
151 2
0.4%
136 1
0.2%
135 1
0.2%
131 1
0.2%
125 1
0.2%
123 1
0.2%
Distinct459
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57201017
Minimum0
Maximum4.5228078 × 108
Zeros42
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:33.612233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117968328
median39187857
Q378244113
95-th percentile1.7202179 × 108
Maximum4.5228078 × 108
Range4.5228078 × 108
Interquartile range (IQR)60275785

Descriptive statistics

Standard deviation59277513
Coefficient of variation (CV)1.0363017
Kurtosis7.2805513
Mean57201017
Median Absolute Deviation (MAD)25007034
Skewness2.247139
Sum2.8600509 × 1010
Variance3.5138235 × 1015
MonotonicityNot monotonic
2023-12-10T23:57:33.916502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 42
 
8.4%
18702470.996 1
 
0.2%
37722722.0 1
 
0.2%
50808736.008 1
 
0.2%
137862588.994 1
 
0.2%
90453186.0 1
 
0.2%
40984283.987 1
 
0.2%
71207166.008 1
 
0.2%
27491994.0 1
 
0.2%
39431013.993 1
 
0.2%
Other values (449) 449
89.8%
ValueCountFrequency (%)
0.0 42
8.4%
1960124.0001 1
 
0.2%
3293367.0 1
 
0.2%
3546241.0 1
 
0.2%
4053311.0001 1
 
0.2%
4129858.0001 1
 
0.2%
5175422.0001 1
 
0.2%
5744548.9997 1
 
0.2%
6311196.0002 1
 
0.2%
6502465.002 1
 
0.2%
ValueCountFrequency (%)
452280779.92 1
0.2%
338261239.89 1
0.2%
315689127.966 1
0.2%
303637219.971 1
0.2%
303401682.983 1
0.2%
298857741.0 1
0.2%
286911337.986 1
0.2%
256866493.968 1
0.2%
242722971.051 1
0.2%
241443743.002 1
0.2%
Distinct86
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.428
Minimum0
Maximum229
Zeros45
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:34.198967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median23
Q340.25
95-th percentile79.1
Maximum229
Range229
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation30.839113
Coefficient of variation (CV)1.013511
Kurtosis12.058056
Mean30.428
Median Absolute Deviation (MAD)14
Skewness2.8967411
Sum15214
Variance951.05092
MonotonicityNot monotonic
2023-12-10T23:57:34.499204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45
 
9.0%
8 20
 
4.0%
6 16
 
3.2%
19 15
 
3.0%
7 15
 
3.0%
17 14
 
2.8%
31 14
 
2.8%
22 13
 
2.6%
20 13
 
2.6%
9 13
 
2.6%
Other values (76) 322
64.4%
ValueCountFrequency (%)
0 45
9.0%
6 16
 
3.2%
7 15
 
3.0%
8 20
4.0%
9 13
 
2.6%
10 7
 
1.4%
11 12
 
2.4%
12 13
 
2.6%
13 12
 
2.4%
14 6
 
1.2%
ValueCountFrequency (%)
229 1
0.2%
225 1
0.2%
190 1
0.2%
189 1
0.2%
177 1
0.2%
176 1
0.2%
156 2
0.4%
155 1
0.2%
152 1
0.2%
129 1
0.2%
Distinct441
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34058121
Minimum0
Maximum4.5390562 × 108
Zeros60
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:57:34.855953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111730737
median23650820
Q342630071
95-th percentile1.056337 × 108
Maximum4.5390562 × 108
Range4.5390562 × 108
Interquartile range (IQR)30899334

Descriptive statistics

Standard deviation39853408
Coefficient of variation (CV)1.1701587
Kurtosis29.510449
Mean34058121
Median Absolute Deviation (MAD)14105843
Skewness4.0609652
Sum1.7029061 × 1010
Variance1.5882941 × 1015
MonotonicityNot monotonic
2023-12-10T23:57:35.151269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 60
 
12.0%
69600365.002 1
 
0.2%
6884942.0002 1
 
0.2%
15175012.0 1
 
0.2%
35291093.994 1
 
0.2%
8061405.0003 1
 
0.2%
107276735.985 1
 
0.2%
28036074.0 1
 
0.2%
62174578.0 1
 
0.2%
38942368.0 1
 
0.2%
Other values (431) 431
86.2%
ValueCountFrequency (%)
0.0 60
12.0%
1977658.0002 1
 
0.2%
3128488.0002 1
 
0.2%
3550455.0004 1
 
0.2%
3874958.9998 1
 
0.2%
3991284.0 1
 
0.2%
4212876.9998 1
 
0.2%
4356534.0 1
 
0.2%
4612205.0002 1
 
0.2%
4659126.0001 1
 
0.2%
ValueCountFrequency (%)
453905623.008 1
0.2%
282849738.054 1
0.2%
216440062.02 1
0.2%
207520278.0 1
0.2%
188077067.01 1
0.2%
181769388.012 1
0.2%
180526375.04 1
0.2%
149728230.97 1
0.2%
146090295.948 1
0.2%
144213412.042 1
0.2%

Interactions

2023-12-10T23:57:23.889396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:04.680678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:06.661403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:08.944649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:10.680129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:12.498214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:14.192010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:16.003009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:17.865883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:20.206687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:22.001853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:24.097399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:04.861727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:06.820217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:09.132174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:10.853744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:12.666008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:14.355307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:16.165625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:18.053706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:20.353820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:22.160325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:24.288538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:05.027850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:06.986799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:09.323771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:11.017796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:12.827587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:14.503312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:16.319463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:18.227141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:20.528119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:22.309003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:24.542696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:05.204748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:07.166209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:09.478514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:11.176251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:12.983075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:14.667372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:16.504148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:18.396610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:20.707703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:22.501177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:24.742541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:05.372906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:07.342753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:09.642241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:11.354453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:13.145458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:14.843666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:16.691623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:19.007547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:20.880919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:22.680699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:24.921693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:05.555801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:07.531733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:09.793448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:11.553442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:13.294339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:15.012607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:16.881261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:19.194606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:21.043770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:22.833196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.107007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:05.756649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:07.715594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:09.952079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:11.717156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:13.450257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:15.173054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:17.037259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:19.388673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:21.225312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:23.007118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.299360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:05.945182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:07.874447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:10.099748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:11.861260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:13.626171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:15.374635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:17.193886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:19.546936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:21.386854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:23.197925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.469597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:06.126672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:08.413841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:10.254425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:12.030980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:13.764104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:15.528826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:17.380273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:19.708512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:21.536063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:23.370547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.694653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:06.326554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:08.604597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:10.413128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:12.201388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:13.899731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:15.688134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:17.551586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:19.867861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:21.694864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:23.536390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.897288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:06.496959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:08.767395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:10.526973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:12.347546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:14.030147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:15.846713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:17.717009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:20.033783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:21.843164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:23.721157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:57:35.393519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(BASE_YYMM)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
기준년월(BASE_YYMM)1.0000.1260.1000.0000.0000.1340.0000.0000.0870.2410.094
총수신평잔_건수(DEP_TOT_AVJN_N)0.1261.0000.2770.0000.0000.0000.0000.4030.0000.4990.066
총수신평잔_총액(DEP_TOT_AVJN_TOT)0.1000.2771.0000.0000.1060.0000.0000.4510.2520.0000.000
유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)0.0000.0000.0001.0000.0660.0000.0000.0000.0000.0000.000
유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)0.0000.0000.1060.0661.0000.0000.0000.0000.0840.0000.000
유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)0.1340.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)0.0000.4030.4510.0000.0000.0000.0001.0000.0000.0000.076
신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)0.0870.0000.2520.0000.0840.0000.0000.0001.0000.0000.634
신용카드_체크카드_건수(C_CARDSUM_AMT_N)0.2410.4990.0000.0000.0000.0000.0000.0000.0001.0000.000
신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)0.0940.0660.0000.0000.0000.0000.0000.0760.6340.0001.000
2023-12-10T23:57:35.734938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(BASE_YYMM)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
기준년월(BASE_YYMM)1.000-0.014-0.062-0.070-0.046-0.035-0.0430.0250.047-0.036-0.017
총수신평잔_건수(DEP_TOT_AVJN_N)-0.0141.0000.055-0.0710.003-0.0090.0120.040-0.049-0.0470.070
총수신평잔_총액(DEP_TOT_AVJN_TOT)-0.0620.0551.0000.003-0.031-0.013-0.0320.037-0.053-0.0670.032
유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)-0.070-0.0710.0031.0000.025-0.007-0.057-0.075-0.0090.0220.081
유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)-0.0460.003-0.0310.0251.000-0.0380.0800.019-0.043-0.0230.033
유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)-0.035-0.009-0.013-0.007-0.0381.000-0.0330.068-0.0300.022-0.005
유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)-0.0430.012-0.032-0.0570.080-0.0331.0000.0200.0400.027-0.077
신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)0.0250.0400.037-0.0750.0190.0680.0201.0000.043-0.1040.052
신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)0.047-0.049-0.053-0.009-0.043-0.0300.0400.0431.000-0.0120.076
신용카드_체크카드_건수(C_CARDSUM_AMT_N)-0.036-0.047-0.0670.022-0.0230.0220.027-0.104-0.0121.000-0.050
신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)-0.0170.0700.0320.0810.033-0.005-0.0770.0520.076-0.0501.000

Missing values

2023-12-10T23:57:26.158582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:57:26.560486image/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

기준년월(BASE_YYMM)그리드코드(GRID50_ID)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
0201912G*0*2*6*5*2112134284.8696670.000.02718702470.996160.0
1201911G*0*2*1*1*7456576140.7852730014217.0100.011104010044.0053911202268.0
2201902G*0*0*0*1*127162865897.27200.007169460.9984536962357.9942130883126.008
3201905G*0*2*7*8*6966161529.721423512008.0430.01090.0509449970.003
4201910G*0*1*6*4*5017234988.8481417449146.0140.070.03911994716.998
5201912G*0*1*9*1*7799664792.258020306823.999770243996.99218101601092.0190.0
6201906G*0*0*7*6*18317843241.5706025857288.0021056502.01550324674.03349383420.004
7201910G*0*0*1*3*87127917755.863027872352.998110.04410319349.996530.0
8201911G*0*0*5*4*20286413760.268100.0015555337.015124730728.9924318723873.0
9201908G*0*0*9*3*11145818083.058190.0160.056315689127.9662899174938.99
기준년월(BASE_YYMM)그리드코드(GRID50_ID)총수신평잔_건수(DEP_TOT_AVJN_N)총수신평잔_총액(DEP_TOT_AVJN_TOT)유동성급여_가맹점매출_유동성연금_입금_건수(I_TOT_AMT_N)유동성급여_가맹점매출_유동성연금_입금총액(I_TOT_AMT_TOT)유동성급여_유동성연금_입금_건수(I_PAY_PENS_AMT_N)유동성급여_유동성연금_입금_총액(I_PAY_PENS_AMT_TOT)신용카드_체크카드_현금소비_현금인출_건수(C_TOT_AMT_N)신용카드_체크카드_현금소비_현금인출_총액(C_TOT_AMT_TOT)신용카드_체크카드_건수(C_CARDSUM_AMT_N)신용카드_체크카드_총액(C_CARDSUM_AMT_TOT)
490201909G*0*1*7*1*51181532185.835260.0100.03021959804.9957012755376.0
491201901G*0*0*4*7*53441076196.851711612683.00211104581523.992012213401.01536150752.007
492201906G*0*1*7*4*51306958690.02670.0025542722.9973489781529.9864021843568.0008
493201905G*0*0*9*0*11101033027.244644754277.00800.06412669138.99941522705775.9999
494201903G*0*0*1*6*333522622192.0921434741039.995028298077.9982451485850.04611418387.003
495201907G*0*2*0*1*27682779737.464180.0100.0019075797.00092223548914.999
496201906G*0*1*3*8*287250347.871317052460.0061855956.00808223622.0022421474973.0008
497201912G*0*2*8*7*115145867075.5160.0200.01234521011.03368655920.025
498201905G*0*0*1*9*192296210280.384017221505.0010624500.02482753263.9926144213412.042
499201909G*0*0*5*4*79157287958.242740889384.9922514422951.0022733817377.993537287249.9998