Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)0.1%
Total size in memory1.2 MiB
Average record size in memory131.0 B

Variable types

Categorical3
Numeric11

Dataset

Description대전교통공사 AFC 발권기 현금 회수 현황입니다.2006년 3월부터 2023년 9월까지의 데이터를 조회할 수 있습니다.각 지폐와 동전 회수 현황과 총 회수 금액 총계에 대한 데이터입니다.
Author대전교통공사
URLhttps://www.data.go.kr/data/15122855/fileData.do

Alerts

영업월 has constant value ""Constant
Dataset has 7 (0.1%) duplicate rowsDuplicates
회수보충금액 is highly overall correlated with 동전금액 and 6 other fieldsHigh correlation
동전금액 is highly overall correlated with 회수보충금액 and 4 other fieldsHigh correlation
10원 is highly overall correlated with 동전금액 and 3 other fieldsHigh correlation
50원 is highly overall correlated with 동전금액 and 3 other fieldsHigh correlation
100원 is highly overall correlated with 회수보충금액 and 4 other fieldsHigh correlation
500원 is highly overall correlated with 동전금액 and 3 other fieldsHigh correlation
1000원 is highly overall correlated with 회수보충금액 and 3 other fieldsHigh correlation
5000원 is highly overall correlated with 회수보충금액 and 3 other fieldsHigh correlation
10000원 is highly overall correlated with 회수보충금액 and 3 other fieldsHigh correlation
지폐금액 is highly overall correlated with 회수보충금액 and 3 other fieldsHigh correlation
장비보관금 is highly overall correlated with 회수보충금액High correlation
동전금액 is highly skewed (γ1 = 31.8217236)Skewed
10원 is highly skewed (γ1 = 57.67845213)Skewed
50원 is highly skewed (γ1 = 56.63884453)Skewed
500원 is highly skewed (γ1 = 40.24663553)Skewed
장비보관금 is highly skewed (γ1 = 44.30169998)Skewed
동전금액 has 6306 (63.1%) zerosZeros
10원 has 9197 (92.0%) zerosZeros
50원 has 7470 (74.7%) zerosZeros
100원 has 6340 (63.4%) zerosZeros
500원 has 7700 (77.0%) zerosZeros
1000원 has 7695 (77.0%) zerosZeros
5000원 has 8326 (83.3%) zerosZeros
10000원 has 8326 (83.3%) zerosZeros
지폐금액 has 7695 (77.0%) zerosZeros
장비보관금 has 214 (2.1%) zerosZeros

Reproduction

Analysis started2023-12-12 17:58:47.563442
Analysis finished2023-12-12 17:59:08.454902
Duration20.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영업월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2006-03
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2006-03
2nd row2006-03
3rd row2006-03
4th row2006-03
5th row2006-03

Common Values

ValueCountFrequency (%)
2006-03 10000
100.0%

Length

2023-12-13T02:59:08.532210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:59:08.636354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2006-03 10000
100.0%

역이름
Categorical

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대전역
 
516
탄방역
 
508
서대전네거리역
 
505
정부청사역
 
485
중앙로역
 
483
Other values (17)
7503 

Length

Max length7
Median length3
Mean length3.6981
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용문역
2nd row현충원역
3rd row판암역
4th row서대전네거리역
5th row월드컵경기장역

Common Values

ValueCountFrequency (%)
대전역 516
 
5.2%
탄방역 508
 
5.1%
서대전네거리역 505
 
5.1%
정부청사역 485
 
4.9%
중앙로역 483
 
4.8%
시청역 476
 
4.8%
용문역 471
 
4.7%
오룡역 465
 
4.7%
대동역 465
 
4.7%
노은역 456
 
4.6%
Other values (12) 5170
51.7%

Length

2023-12-13T02:59:08.755027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대전역 516
 
5.2%
탄방역 508
 
5.1%
서대전네거리역 505
 
5.1%
정부청사역 485
 
4.9%
중앙로역 483
 
4.8%
시청역 476
 
4.8%
용문역 471
 
4.7%
오룡역 465
 
4.7%
대동역 465
 
4.7%
노은역 456
 
4.6%
Other values (12) 5170
51.7%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지폐회수
2305 
동전회수
2200 
<NA>
1632 
지폐보충
1620 
동전보충
1426 

Length

Max length5
Median length4
Mean length4.0817
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지폐보충
2nd row지폐회수
3rd row동전보충
4th row동전회수
5th row동전회수

Common Values

ValueCountFrequency (%)
지폐회수 2305
23.1%
동전회수 2200
22.0%
<NA> 1632
16.3%
지폐보충 1620
16.2%
동전보충 1426
14.3%
동전클리어 817
 
8.2%

Length

2023-12-13T02:59:08.883564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:59:09.011766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지폐회수 2305
23.1%
동전회수 2200
22.0%
na 1632
16.3%
지폐보충 1620
16.2%
동전보충 1426
14.3%
동전클리어 817
 
8.2%

회수보충금액
Real number (ℝ)

HIGH CORRELATION 

Distinct8025
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8377608.2
Minimum0
Maximum1.17864 × 108
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:09.182941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21095
Q1158275
median2061250
Q39435500
95-th percentile41017100
Maximum1.17864 × 108
Range1.17864 × 108
Interquartile range (IQR)9277225

Descriptive statistics

Standard deviation14614284
Coefficient of variation (CV)1.7444459
Kurtosis9.1373109
Mean8377608.2
Median Absolute Deviation (MAD)2021250
Skewness2.8253489
Sum8.3776082 × 1010
Variance2.1357731 × 1014
MonotonicityNot monotonic
2023-12-13T02:59:09.323796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 93
 
0.9%
10000 71
 
0.7%
15000 59
 
0.6%
30000 53
 
0.5%
25000 35
 
0.4%
40000 32
 
0.3%
60000 27
 
0.3%
5000 23
 
0.2%
50000 22
 
0.2%
45000 21
 
0.2%
Other values (8015) 9564
95.6%
ValueCountFrequency (%)
0 15
0.1%
200 1
 
< 0.1%
500 1
 
< 0.1%
600 1
 
< 0.1%
700 1
 
< 0.1%
1000 2
 
< 0.1%
2000 2
 
< 0.1%
2200 1
 
< 0.1%
2500 4
 
< 0.1%
3000 3
 
< 0.1%
ValueCountFrequency (%)
117864000 1
< 0.1%
114658000 1
< 0.1%
111987000 1
< 0.1%
110163000 1
< 0.1%
107798000 1
< 0.1%
104667000 1
< 0.1%
104285000 1
< 0.1%
102814000 1
< 0.1%
102602000 1
< 0.1%
100029000 1
< 0.1%

동전금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2619
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209588.63
Minimum0
Maximum46863850
Zeros6306
Zeros (%)63.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:09.503926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q360150
95-th percentile1351245
Maximum46863850
Range46863850
Interquartile range (IQR)60150

Descriptive statistics

Standard deviation856471.66
Coefficient of variation (CV)4.0864414
Kurtosis1635.0811
Mean209588.63
Median Absolute Deviation (MAD)0
Skewness31.821724
Sum2.0958863 × 109
Variance7.335437 × 1011
MonotonicityNot monotonic
2023-12-13T02:59:09.644572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6306
63.1%
20000 93
 
0.9%
10000 73
 
0.7%
15000 58
 
0.6%
30000 52
 
0.5%
25000 35
 
0.4%
40000 33
 
0.3%
5000 27
 
0.3%
60000 27
 
0.3%
2000 23
 
0.2%
Other values (2609) 3273
32.7%
ValueCountFrequency (%)
0 6306
63.1%
200 1
 
< 0.1%
500 1
 
< 0.1%
600 1
 
< 0.1%
700 1
 
< 0.1%
1000 2
 
< 0.1%
1800 1
 
< 0.1%
2000 23
 
0.2%
2100 1
 
< 0.1%
2200 3
 
< 0.1%
ValueCountFrequency (%)
46863850 1
< 0.1%
45003000 1
< 0.1%
12848980 1
< 0.1%
5008000 1
< 0.1%
5003800 1
< 0.1%
4686200 1
< 0.1%
4594050 1
< 0.1%
4591200 1
< 0.1%
4585850 1
< 0.1%
4541800 1
< 0.1%

10원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct66
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.1116
Minimum0
Maximum65635
Zeros9197
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:09.782576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile40
Maximum65635
Range65635
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1136.1489
Coefficient of variation (CV)45.243985
Kurtosis3326.9772
Mean25.1116
Median Absolute Deviation (MAD)0
Skewness57.678452
Sum251116
Variance1290834.2
MonotonicityNot monotonic
2023-12-13T02:59:09.917948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9197
92.0%
20 204
 
2.0%
40 125
 
1.2%
60 72
 
0.7%
80 48
 
0.5%
100 38
 
0.4%
45 31
 
0.3%
25 22
 
0.2%
120 22
 
0.2%
85 18
 
0.2%
Other values (56) 223
 
2.2%
ValueCountFrequency (%)
0 9197
92.0%
1 2
 
< 0.1%
2 1
 
< 0.1%
5 2
 
< 0.1%
10 2
 
< 0.1%
20 204
 
2.0%
25 22
 
0.2%
30 6
 
0.1%
35 4
 
< 0.1%
40 125
 
1.2%
ValueCountFrequency (%)
65635 1
< 0.1%
65608 1
< 0.1%
65535 1
< 0.1%
360 1
< 0.1%
350 1
< 0.1%
335 1
< 0.1%
320 1
< 0.1%
315 1
< 0.1%
290 1
< 0.1%
285 1
< 0.1%

50원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct468
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.9497
Minimum0
Maximum65841
Zeros7470
Zeros (%)74.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:10.054899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile240
Maximum65841
Range65841
Interquartile range (IQR)20

Descriptive statistics

Standard deviation1143.9529
Coefficient of variation (CV)18.768802
Kurtosis3246.201
Mean60.9497
Median Absolute Deviation (MAD)0
Skewness56.638845
Sum609497
Variance1308628.2
MonotonicityNot monotonic
2023-12-13T02:59:10.197290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7470
74.7%
20 231
 
2.3%
100 181
 
1.8%
40 119
 
1.2%
200 84
 
0.8%
60 72
 
0.7%
50 61
 
0.6%
80 41
 
0.4%
120 37
 
0.4%
21 37
 
0.4%
Other values (458) 1667
 
16.7%
ValueCountFrequency (%)
0 7470
74.7%
2 4
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
65841 1
< 0.1%
65646 1
< 0.1%
65536 1
< 0.1%
4600 1
< 0.1%
2588 1
< 0.1%
1946 1
< 0.1%
1876 1
< 0.1%
1810 1
< 0.1%
1763 1
< 0.1%
1760 1
< 0.1%

100원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2103
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean869.8608
Minimum0
Maximum74626
Zeros6340
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:10.339143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3377
95-th percentile5574.05
Maximum74626
Range74626
Interquartile range (IQR)377

Descriptive statistics

Standard deviation2547.3704
Coefficient of variation (CV)2.9284805
Kurtosis167.30457
Mean869.8608
Median Absolute Deviation (MAD)0
Skewness8.5486615
Sum8698608
Variance6489096
MonotonicityNot monotonic
2023-12-13T02:59:10.482252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6340
63.4%
200 171
 
1.7%
100 146
 
1.5%
400 79
 
0.8%
300 66
 
0.7%
600 42
 
0.4%
20 40
 
0.4%
500 31
 
0.3%
50 26
 
0.3%
800 25
 
0.2%
Other values (2093) 3034
30.3%
ValueCountFrequency (%)
0 6340
63.4%
2 1
 
< 0.1%
6 1
 
< 0.1%
18 1
 
< 0.1%
20 40
 
0.4%
21 1
 
< 0.1%
22 2
 
< 0.1%
23 2
 
< 0.1%
24 1
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
74626 1
< 0.1%
66236 1
< 0.1%
66117 1
< 0.1%
22757 1
< 0.1%
22491 1
< 0.1%
21393 1
< 0.1%
20654 1
< 0.1%
20650 1
< 0.1%
20581 1
< 0.1%
20416 1
< 0.1%

500원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1100
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.6079
Minimum0
Maximum72627
Zeros7700
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:10.677700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1561.05
Maximum72627
Range72627
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1199.2507
Coefficient of variation (CV)5.026031
Kurtosis2300.4718
Mean238.6079
Median Absolute Deviation (MAD)0
Skewness40.246636
Sum2386079
Variance1438202.3
MonotonicityNot monotonic
2023-12-13T02:59:10.872401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7700
77.0%
10 49
 
0.5%
20 44
 
0.4%
90 40
 
0.4%
30 35
 
0.4%
100 32
 
0.3%
40 30
 
0.3%
80 30
 
0.3%
50 30
 
0.3%
120 28
 
0.3%
Other values (1090) 1982
 
19.8%
ValueCountFrequency (%)
0 7700
77.0%
1 1
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
10 49
 
0.5%
11 4
 
< 0.1%
12 1
 
< 0.1%
16 1
 
< 0.1%
20 44
 
0.4%
ValueCountFrequency (%)
72627 1
< 0.1%
67186 1
< 0.1%
8963 1
< 0.1%
8065 1
< 0.1%
7586 1
< 0.1%
7377 1
< 0.1%
7167 1
< 0.1%
6947 1
< 0.1%
6150 1
< 0.1%
6133 1
< 0.1%

1000원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2152
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2059.6052
Minimum0
Maximum51609
Zeros7695
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:11.054585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13332.35
Maximum51609
Range51609
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5296.2314
Coefficient of variation (CV)2.5714789
Kurtosis14.627548
Mean2059.6052
Median Absolute Deviation (MAD)0
Skewness3.5165084
Sum20596052
Variance28050067
MonotonicityNot monotonic
2023-12-13T02:59:11.224284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7695
77.0%
114 4
 
< 0.1%
1281 3
 
< 0.1%
3495 3
 
< 0.1%
3827 3
 
< 0.1%
2936 3
 
< 0.1%
8144 3
 
< 0.1%
1398 3
 
< 0.1%
1750 3
 
< 0.1%
2604 3
 
< 0.1%
Other values (2142) 2277
 
22.8%
ValueCountFrequency (%)
0 7695
77.0%
114 4
 
< 0.1%
171 1
 
< 0.1%
228 1
 
< 0.1%
256 1
 
< 0.1%
385 2
 
< 0.1%
406 1
 
< 0.1%
407 1
 
< 0.1%
411 1
 
< 0.1%
412 1
 
< 0.1%
ValueCountFrequency (%)
51609 1
< 0.1%
48487 1
< 0.1%
42346 1
< 0.1%
41112 1
< 0.1%
41008 1
< 0.1%
40364 1
< 0.1%
40135 1
< 0.1%
39388 1
< 0.1%
39171 1
< 0.1%
38599 1
< 0.1%

5000원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct967
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0515
Minimum0
Maximum2538
Zeros8326
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:11.372689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile777.1
Maximum2538
Range2538
Interquartile range (IQR)0

Descriptive statistics

Standard deviation281.29043
Coefficient of variation (CV)2.8114564
Kurtosis12.261092
Mean100.0515
Median Absolute Deviation (MAD)0
Skewness3.3664292
Sum1000515
Variance79124.308
MonotonicityNot monotonic
2023-12-13T02:59:11.549194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8326
83.3%
257 7
 
0.1%
201 7
 
0.1%
338 6
 
0.1%
241 6
 
0.1%
214 6
 
0.1%
228 6
 
0.1%
167 6
 
0.1%
251 6
 
0.1%
999 6
 
0.1%
Other values (957) 1618
 
16.2%
ValueCountFrequency (%)
0 8326
83.3%
6 1
 
< 0.1%
30 1
 
< 0.1%
37 1
 
< 0.1%
41 3
 
< 0.1%
43 2
 
< 0.1%
44 1
 
< 0.1%
45 1
 
< 0.1%
46 2
 
< 0.1%
47 2
 
< 0.1%
ValueCountFrequency (%)
2538 1
< 0.1%
2447 1
< 0.1%
2344 1
< 0.1%
2199 1
< 0.1%
2196 1
< 0.1%
2189 1
< 0.1%
2112 1
< 0.1%
2034 1
< 0.1%
2012 1
< 0.1%
1978 1
< 0.1%

10000원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1367
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.0518
Minimum0
Maximum6946
Zeros8326
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:12.004008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2487.15
Maximum6946
Range6946
Interquartile range (IQR)0

Descriptive statistics

Standard deviation893.04241
Coefficient of variation (CV)2.7473849
Kurtosis11.14872
Mean325.0518
Median Absolute Deviation (MAD)0
Skewness3.2477855
Sum3250518
Variance797524.74
MonotonicityNot monotonic
2023-12-13T02:59:12.170596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8326
83.3%
1324 6
 
0.1%
1890 4
 
< 0.1%
717 4
 
< 0.1%
1466 4
 
< 0.1%
457 4
 
< 0.1%
2237 4
 
< 0.1%
1476 3
 
< 0.1%
611 3
 
< 0.1%
1885 3
 
< 0.1%
Other values (1357) 1639
 
16.4%
ValueCountFrequency (%)
0 8326
83.3%
14 1
 
< 0.1%
141 1
 
< 0.1%
171 1
 
< 0.1%
187 1
 
< 0.1%
200 1
 
< 0.1%
203 1
 
< 0.1%
213 1
 
< 0.1%
223 1
 
< 0.1%
224 1
 
< 0.1%
ValueCountFrequency (%)
6946 1
< 0.1%
6814 1
< 0.1%
6810 1
< 0.1%
6767 1
< 0.1%
6700 1
< 0.1%
6606 1
< 0.1%
6425 1
< 0.1%
6412 1
< 0.1%
6391 1
< 0.1%
6362 1
< 0.1%

지폐금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2249
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6253520.7
Minimum0
Maximum1.17864 × 108
Zeros7695
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:12.369191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile40920050
Maximum1.17864 × 108
Range1.17864 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14992416
Coefficient of variation (CV)2.3974361
Kurtosis9.5330101
Mean6253520.7
Median Absolute Deviation (MAD)0
Skewness2.9744917
Sum6.2535207 × 1010
Variance2.2477254 × 1014
MonotonicityNot monotonic
2023-12-13T02:59:12.589561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7695
77.0%
114000 4
 
< 0.1%
14193000 2
 
< 0.1%
34426000 2
 
< 0.1%
35146000 2
 
< 0.1%
26272000 2
 
< 0.1%
18080000 2
 
< 0.1%
21193000 2
 
< 0.1%
26359000 2
 
< 0.1%
36521000 2
 
< 0.1%
Other values (2239) 2285
 
22.9%
ValueCountFrequency (%)
0 7695
77.0%
114000 4
 
< 0.1%
171000 1
 
< 0.1%
228000 1
 
< 0.1%
256000 1
 
< 0.1%
915000 1
 
< 0.1%
988000 1
 
< 0.1%
1000000 1
 
< 0.1%
1086000 1
 
< 0.1%
1087000 1
 
< 0.1%
ValueCountFrequency (%)
117864000 1
< 0.1%
114658000 1
< 0.1%
111987000 1
< 0.1%
110163000 1
< 0.1%
107798000 1
< 0.1%
104667000 1
< 0.1%
104285000 1
< 0.1%
102814000 1
< 0.1%
102602000 1
< 0.1%
100029000 1
< 0.1%

장비보관금
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct9355
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17016128
Minimum0
Maximum2.0651464 × 109
Zeros214
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:59:12.787261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35972.5
Q13807825
median13995700
Q324391962
95-th percentile46993160
Maximum2.0651464 × 109
Range2.0651464 × 109
Interquartile range (IQR)20584138

Descriptive statistics

Standard deviation27857286
Coefficient of variation (CV)1.6371108
Kurtosis3067.4726
Mean17016128
Median Absolute Deviation (MAD)10267975
Skewness44.3017
Sum1.7016128 × 1011
Variance7.7602838 × 1014
MonotonicityNot monotonic
2023-12-13T02:59:12.953641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 214
 
2.1%
25000 23
 
0.2%
1000 15
 
0.1%
3000 14
 
0.1%
5000 12
 
0.1%
2000 11
 
0.1%
15000 11
 
0.1%
4000 11
 
0.1%
50000 11
 
0.1%
6000 10
 
0.1%
Other values (9345) 9668
96.7%
ValueCountFrequency (%)
0 214
2.1%
1000 15
 
0.1%
2000 11
 
0.1%
2300 1
 
< 0.1%
3000 14
 
0.1%
4000 11
 
0.1%
5000 12
 
0.1%
6000 10
 
0.1%
7000 8
 
0.1%
7250 1
 
< 0.1%
ValueCountFrequency (%)
2065146350 1
< 0.1%
984011000 1
< 0.1%
166526960 1
< 0.1%
157636800 1
< 0.1%
156244000 1
< 0.1%
149837100 1
< 0.1%
147552200 1
< 0.1%
141720000 1
< 0.1%
137338400 1
< 0.1%
136063200 1
< 0.1%

Interactions

2023-12-13T02:59:06.726097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:51.676697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:53.040048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:54.369438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.717152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:57.031910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:58.893358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:00.424762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:01.881960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:03.250857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:04.801229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:06.838884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:51.795514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:53.165473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:54.485339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.836679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:57.168220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:59.030581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:00.553109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:02.001004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:03.386246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:04.935998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:06.956085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:51.910295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:53.274904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:54.602597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.935321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:57.294427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:59.156250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:00.672741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:02.112039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:03.511111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:05.059637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:07.089748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:52.039597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:53.405353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:54.747487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:56.047483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:57.422435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:59.279031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:00.810315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:02.229962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:03.651851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:05.216185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:07.213065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:52.170217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:53.536544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:54.875403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:56.153966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:57.547995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:59.420069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:00.931169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:02.363639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:03.797383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:05.354948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:07.342328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:52.280625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:53.680363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.001034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:56.282270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:58.019379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:59.578481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:01.079817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:02.478280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:03.940021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:05.495549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:07.484836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:52.409367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:53.795563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.115509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:56.400089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:58.169601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:59.713862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:01.228947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:02.600676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:04.080530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:05.634656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:07.612904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:52.538164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:53.897163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.242270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:56.530511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:58.310040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:59.846841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:01.329700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:02.736418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:04.219586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:05.784537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:07.720808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:52.665009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:54.014352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.357194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:56.648220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:58.452878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:59.975934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:01.461711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:02.866671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:04.367996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:05.933583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:07.838458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:52.797655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:54.140379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.473400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:56.785135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:58.598484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:00.145181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:01.609988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:03.009519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:04.510254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:06.081073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:07.939584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:52.919971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:54.264748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:55.589868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:56.907442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:58:58.745323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:00.291853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:01.753779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:03.130875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:04.662749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:06.596009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:59:13.084881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역이름동전지폐회수구분회수보충금액동전금액10원50원100원500원1000원5000원10000원지폐금액장비보관금
역이름1.0000.0880.2600.0000.0000.0000.2620.0970.3600.2450.2540.2470.000
동전지폐회수구분0.0881.0000.7230.0110.0190.0190.2350.0310.6950.6650.6980.7530.021
회수보충금액0.2600.7231.0000.0760.0880.0880.0860.0940.7810.9530.9661.0000.104
동전금액0.0000.0110.0761.0001.0001.0000.8080.6760.0000.0000.0000.0000.500
10원0.0000.0190.0881.0001.0000.9661.0000.5480.0000.0000.0000.0000.363
50원0.0000.0190.0881.0000.9661.0001.0000.5480.0000.0000.0000.0000.363
100원0.2620.2350.0860.8081.0001.0001.0000.8940.0210.0000.0220.0390.941
500원0.0970.0310.0940.6760.5480.5480.8941.0000.0000.0000.0000.0000.827
1000원0.3600.6950.7810.0000.0000.0000.0210.0001.0000.7380.7090.7920.000
5000원0.2450.6650.9530.0000.0000.0000.0000.0000.7381.0000.9410.9570.000
10000원0.2540.6980.9660.0000.0000.0000.0220.0000.7090.9411.0000.9700.000
지폐금액0.2470.7531.0000.0000.0000.0000.0390.0000.7920.9570.9701.0000.000
장비보관금0.0000.0210.1040.5000.3630.3630.9410.8270.0000.0000.0000.0001.000
2023-12-13T02:59:13.257715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역이름동전지폐회수구분
역이름1.0000.043
동전지폐회수구분0.0431.000
2023-12-13T02:59:13.392160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회수보충금액동전금액10원50원100원500원1000원5000원10000원지폐금액장비보관금역이름동전지폐회수구분
회수보충금액1.000-0.551-0.053-0.402-0.550-0.2800.6750.6070.6080.6860.5600.0990.383
동전금액-0.5511.0000.5260.8000.9920.826-0.400-0.330-0.330-0.4000.0920.0000.009
10원-0.0530.5261.0000.5070.5190.617-0.160-0.132-0.132-0.1600.1890.0000.023
50원-0.4020.8000.5071.0000.8090.617-0.311-0.256-0.256-0.3110.0410.0000.023
100원-0.5500.9920.5190.8091.0000.791-0.397-0.327-0.327-0.3970.0760.1230.161
500원-0.2800.8260.6170.6170.7911.000-0.292-0.241-0.241-0.2920.3610.0500.023
1000원0.675-0.400-0.160-0.311-0.397-0.2921.0000.7860.7860.9910.2050.1410.359
5000원0.607-0.330-0.132-0.256-0.327-0.2410.7861.0001.0000.8570.3090.0930.336
10000원0.608-0.330-0.132-0.256-0.327-0.2410.7861.0001.0000.8570.3090.0960.363
지폐금액0.686-0.400-0.160-0.311-0.397-0.2920.9910.8570.8571.0000.2330.0940.410
장비보관금0.5600.0920.1890.0410.0760.3610.2050.3090.3090.2331.0000.0000.016
역이름0.0990.0000.0000.0000.1230.0500.1410.0930.0960.0940.0001.0000.043
동전지폐회수구분0.3830.0090.0230.0230.1610.0230.3590.3360.3630.4100.0160.0431.000

Missing values

2023-12-13T02:59:08.110063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:59:08.369369image/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

영업월역이름동전지폐회수구분회수보충금액동전금액10원50원100원500원1000원5000원10000원지폐금액장비보관금
188822006-03용문역지폐보충31470000000000008369600
31202006-03현충원역지폐회수53290000000053290053290002199750
191702006-03판암역동전보충31000310000031000000205750
1582006-03서대전네거리역동전회수2162300216230050017183980000028432350
188212006-03월드컵경기장역동전회수485004850002012570000019648300
44222006-03시청역동전회수25867002586700100280129172560000062577900
25412006-03오룡역지폐회수90710000000090710090710005243700
114732006-03지족역지폐보충1265000000000000028064150
85362006-03지족역<NA>40660000000000009688000
53822006-03노은역동전클리어12655018200008220000078200
영업월역이름동전지폐회수구분회수보충금액동전금액10원50원100원500원1000원5000원10000원지폐금액장비보관금
102792006-03대동역지폐보충1448400000000000066532400
185322006-03대전역<NA>379100000000000020044400
40302006-03중앙로역동전클리어137530000000000161000
5012006-03시청역동전보충27360027360007142379000002051700
12202006-03오룡역지폐회수114160000000011416001141600013428050
82772006-03월평역동전클리어60900000000000660000
175792006-03중앙로역동전보충3250032500070240100000447650
194882006-03노은역지폐보충36910000000000005831600
2222006-03정부청사역동전클리어8742900000000000
94692006-03유성온천역<NA>187200000000000015324400

Duplicate rows

Most frequently occurring

영업월역이름동전지폐회수구분회수보충금액동전금액10원50원100원500원1000원5000원10000원지폐금액장비보관금# duplicates
02006-03노은역동전보충1500015000010010000000250004
12006-03시청역동전보충3000030000020020000000500003
32006-03월평역동전보충1500015000010010000000250003
22006-03월드컵경기장역동전보충1500015000010010000000250002
42006-03유성온천역동전보충1500015000010010000000250002
52006-03중구청역동전보충1500015000010010000000250002
62006-03중앙로역동전보충2500025000010020000000450002