Overview

Dataset statistics

Number of variables13
Number of observations9108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1014.1 KiB
Average record size in memory114.0 B

Variable types

DateTime1
Categorical2
Numeric10

Dataset

Description대전교통공사 이벤트수입 현황입니다.2009년 3월부터 2023년 6월까지의 데이터를 조회할 수 있습니다.월별로 각 역사에 따른 이벤트와 관련된 인원 수와 금액 데이터를 제공하고 있습니다.
Author대전교통공사
URLhttps://www.data.go.kr/data/15122689/fileData.do

Alerts

어른인원 is highly overall correlated with 어른운임 and 6 other fieldsHigh correlation
어른운임 is highly overall correlated with 어른인원 and 6 other fieldsHigh correlation
청소년인원 is highly overall correlated with 어른인원 and 6 other fieldsHigh correlation
청소년운임 is highly overall correlated with 어른인원 and 6 other fieldsHigh correlation
어린이인원 is highly overall correlated with 어른인원 and 6 other fieldsHigh correlation
어린이운임 is highly overall correlated with 어른인원 and 6 other fieldsHigh correlation
기타인원 is highly overall correlated with 기타운임High correlation
기타운임 is highly overall correlated with 기타인원High correlation
확정인원 is highly overall correlated with 어른인원 and 6 other fieldsHigh correlation
확정금액 is highly overall correlated with 어른인원 and 6 other fieldsHigh correlation
어른인원 is highly skewed (γ1 = 22.68746048)Skewed
어른운임 is highly skewed (γ1 = 21.35111808)Skewed
청소년인원 is highly skewed (γ1 = 25.40193187)Skewed
청소년운임 is highly skewed (γ1 = 27.82280538)Skewed
기타운임 is highly skewed (γ1 = 35.63561547)Skewed
확정인원 is highly skewed (γ1 = 21.89382929)Skewed
확정금액 is highly skewed (γ1 = 22.21720658)Skewed
어른인원 has 7080 (77.7%) zerosZeros
어른운임 has 7150 (78.5%) zerosZeros
청소년인원 has 7660 (84.1%) zerosZeros
청소년운임 has 7665 (84.2%) zerosZeros
어린이인원 has 8262 (90.7%) zerosZeros
어린이운임 has 8264 (90.7%) zerosZeros
기타인원 has 8638 (94.8%) zerosZeros
기타운임 has 8800 (96.6%) zerosZeros
확정인원 has 6549 (71.9%) zerosZeros
확정금액 has 6764 (74.3%) zerosZeros

Reproduction

Analysis started2023-12-12 10:47:15.291216
Analysis finished2023-12-12 10:47:34.540262
Duration19.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct172
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
Minimum2009-03-01 00:00:00
Maximum2023-06-01 00:00:00
2023-12-12T19:47:34.617095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:35.148638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

역사
Categorical

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
판암역
 
414
신흥역
 
414
대동역
 
414
대전역
 
414
중앙로역
 
414
Other values (17)
7038 

Length

Max length7
Median length3
Mean length3.6818182
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row판암역
2nd row판암역
3rd row신흥역
4th row신흥역
5th row대동역

Common Values

ValueCountFrequency (%)
판암역 414
 
4.5%
신흥역 414
 
4.5%
대동역 414
 
4.5%
대전역 414
 
4.5%
중앙로역 414
 
4.5%
중구청역 414
 
4.5%
서대전네거리역 414
 
4.5%
오룡역 414
 
4.5%
용문역 414
 
4.5%
탄방역 414
 
4.5%
Other values (12) 4968
54.5%

Length

2023-12-12T19:47:35.351232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
판암역 414
 
4.5%
신흥역 414
 
4.5%
지족역 414
 
4.5%
노은역 414
 
4.5%
월드컵경기장역 414
 
4.5%
현충원역 414
 
4.5%
구암역 414
 
4.5%
유성온천역 414
 
4.5%
갑천역 414
 
4.5%
월평역 414
 
4.5%
Other values (12) 4968
54.5%

이벤트코드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.3 KiB
쿠폰회수
3784 
대전시티즌입장권회수
2706 
환승손실금
2398 
꿈나무 사랑카드
 
220

Length

Max length10
Median length8
Mean length6.1425121
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전시티즌입장권회수
2nd row쿠폰회수
3rd row대전시티즌입장권회수
4th row쿠폰회수
5th row대전시티즌입장권회수

Common Values

ValueCountFrequency (%)
쿠폰회수 3784
41.5%
대전시티즌입장권회수 2706
29.7%
환승손실금 2398
26.3%
꿈나무 사랑카드 220
 
2.4%

Length

2023-12-12T19:47:35.522278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:35.660071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
쿠폰회수 3784
40.6%
대전시티즌입장권회수 2706
29.0%
환승손실금 2398
25.7%
꿈나무 220
 
2.4%
사랑카드 220
 
2.4%

어른인원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct275
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.555775
Minimum0
Maximum14586
Zeros7080
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:35.816867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile33
Maximum14586
Range14586
Interquartile range (IQR)0

Descriptive statistics

Standard deviation291.07946
Coefficient of variation (CV)9.8484801
Kurtosis831.68142
Mean29.555775
Median Absolute Deviation (MAD)0
Skewness22.68746
Sum269194
Variance84727.254
MonotonicityNot monotonic
2023-12-12T19:47:36.037190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7080
77.7%
1 175
 
1.9%
2 172
 
1.9%
3 129
 
1.4%
4 120
 
1.3%
6 96
 
1.1%
5 95
 
1.0%
7 74
 
0.8%
9 59
 
0.6%
10 57
 
0.6%
Other values (265) 1051
 
11.5%
ValueCountFrequency (%)
0 7080
77.7%
1 175
 
1.9%
2 172
 
1.9%
3 129
 
1.4%
4 120
 
1.3%
5 95
 
1.0%
6 96
 
1.1%
7 74
 
0.8%
8 57
 
0.6%
9 59
 
0.6%
ValueCountFrequency (%)
14586 1
< 0.1%
7774 1
< 0.1%
7066 1
< 0.1%
4811 1
< 0.1%
3822 1
< 0.1%
3498 1
< 0.1%
3397 1
< 0.1%
3391 1
< 0.1%
3342 1
< 0.1%
3295 1
< 0.1%

어른운임
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct375
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30873.164
Minimum0
Maximum16044600
Zeros7150
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:36.218622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18500
Maximum16044600
Range16044600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation332134.67
Coefficient of variation (CV)10.758038
Kurtosis731.96658
Mean30873.164
Median Absolute Deviation (MAD)0
Skewness21.351118
Sum2.8119278 × 108
Variance1.1031344 × 1011
MonotonicityNot monotonic
2023-12-12T19:47:36.412888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7150
78.5%
1400 120
 
1.3%
700 119
 
1.3%
2100 72
 
0.8%
2800 71
 
0.8%
3500 70
 
0.8%
4200 57
 
0.6%
3000 46
 
0.5%
4900 36
 
0.4%
6000 35
 
0.4%
Other values (365) 1332
 
14.6%
ValueCountFrequency (%)
0 7150
78.5%
500 26
 
0.3%
600 22
 
0.2%
700 119
 
1.3%
1000 22
 
0.2%
1200 28
 
0.3%
1250 6
 
0.1%
1400 120
 
1.3%
1500 25
 
0.3%
1800 28
 
0.3%
ValueCountFrequency (%)
16044600 1
< 0.1%
8551400 1
< 0.1%
7772600 1
< 0.1%
6013750 1
< 0.1%
4204200 1
< 0.1%
3847800 1
< 0.1%
3736700 1
< 0.1%
3730100 1
< 0.1%
3676200 1
< 0.1%
3662500 1
< 0.1%

청소년인원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct133
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.027119
Minimum0
Maximum1399
Zeros7660
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:36.804008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum1399
Range1399
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.119419
Coefficient of variation (CV)8.6284743
Kurtosis1039.0616
Mean3.027119
Median Absolute Deviation (MAD)0
Skewness25.401932
Sum27571
Variance682.22404
MonotonicityNot monotonic
2023-12-12T19:47:37.106726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7660
84.1%
1 292
 
3.2%
2 241
 
2.6%
3 142
 
1.6%
4 116
 
1.3%
5 77
 
0.8%
6 69
 
0.8%
7 46
 
0.5%
8 44
 
0.5%
9 38
 
0.4%
Other values (123) 383
 
4.2%
ValueCountFrequency (%)
0 7660
84.1%
1 292
 
3.2%
2 241
 
2.6%
3 142
 
1.6%
4 116
 
1.3%
5 77
 
0.8%
6 69
 
0.8%
7 46
 
0.5%
8 44
 
0.5%
9 38
 
0.4%
ValueCountFrequency (%)
1399 1
< 0.1%
691 1
< 0.1%
604 1
< 0.1%
442 1
< 0.1%
431 1
< 0.1%
405 2
< 0.1%
379 1
< 0.1%
340 1
< 0.1%
316 1
< 0.1%
293 1
< 0.1%

청소년운임
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct222
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2242.0751
Minimum0
Maximum1231120
Zeros7665
Zeros (%)84.2%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:37.350025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4800
Maximum1231120
Range1231120
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22092.772
Coefficient of variation (CV)9.8537165
Kurtosis1208.0014
Mean2242.0751
Median Absolute Deviation (MAD)0
Skewness27.822805
Sum20420820
Variance4.880906 × 108
MonotonicityNot monotonic
2023-12-12T19:47:37.537261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7665
84.2%
700 132
 
1.4%
600 93
 
1.0%
1400 89
 
1.0%
1200 76
 
0.8%
2100 57
 
0.6%
1000 55
 
0.6%
1800 47
 
0.5%
3000 44
 
0.5%
2400 44
 
0.5%
Other values (212) 806
 
8.8%
ValueCountFrequency (%)
0 7665
84.2%
500 41
 
0.5%
600 93
 
1.0%
700 132
 
1.4%
880 25
 
0.3%
1000 55
 
0.6%
1200 76
 
0.8%
1400 89
 
1.0%
1500 31
 
0.3%
1760 18
 
0.2%
ValueCountFrequency (%)
1231120 1
< 0.1%
608080 1
< 0.1%
531520 1
< 0.1%
379280 1
< 0.1%
356400 2
< 0.1%
333520 1
< 0.1%
299200 1
< 0.1%
278080 1
< 0.1%
257840 1
< 0.1%
237600 1
< 0.1%

어린이인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86956522
Minimum0
Maximum160
Zeros8262
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:37.743180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum160
Range160
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.5898094
Coefficient of variation (CV)6.4282808
Kurtosis216.0305
Mean0.86956522
Median Absolute Deviation (MAD)0
Skewness12.52005
Sum7920
Variance31.245969
MonotonicityNot monotonic
2023-12-12T19:47:37.942489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8262
90.7%
1 209
 
2.3%
2 128
 
1.4%
3 96
 
1.1%
4 60
 
0.7%
5 44
 
0.5%
6 36
 
0.4%
7 30
 
0.3%
8 29
 
0.3%
12 17
 
0.2%
Other values (62) 197
 
2.2%
ValueCountFrequency (%)
0 8262
90.7%
1 209
 
2.3%
2 128
 
1.4%
3 96
 
1.1%
4 60
 
0.7%
5 44
 
0.5%
6 36
 
0.4%
7 30
 
0.3%
8 29
 
0.3%
9 12
 
0.1%
ValueCountFrequency (%)
160 1
< 0.1%
124 1
< 0.1%
123 1
< 0.1%
118 1
< 0.1%
93 1
< 0.1%
92 1
< 0.1%
90 1
< 0.1%
83 1
< 0.1%
80 1
< 0.1%
78 1
< 0.1%

어린이운임
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct119
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.07905
Minimum0
Maximum64900
Zeros8264
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:38.135940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile900
Maximum64900
Range64900
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1709.6973
Coefficient of variation (CV)6.4497639
Kurtosis343.97006
Mean265.07905
Median Absolute Deviation (MAD)0
Skewness14.528403
Sum2414340
Variance2923064.9
MonotonicityNot monotonic
2023-12-12T19:47:38.288774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8264
90.7%
300 132
 
1.4%
600 80
 
0.9%
900 56
 
0.6%
250 41
 
0.5%
500 37
 
0.4%
1500 37
 
0.4%
550 35
 
0.4%
1200 35
 
0.4%
750 29
 
0.3%
Other values (109) 362
 
4.0%
ValueCountFrequency (%)
0 8264
90.7%
250 41
 
0.5%
300 132
 
1.4%
500 37
 
0.4%
550 35
 
0.4%
600 80
 
0.9%
750 29
 
0.3%
900 56
 
0.6%
1000 22
 
0.2%
1100 11
 
0.1%
ValueCountFrequency (%)
64900 1
< 0.1%
40000 1
< 0.1%
37200 1
< 0.1%
30750 1
< 0.1%
27600 1
< 0.1%
27000 1
< 0.1%
24000 1
< 0.1%
23400 1
< 0.1%
23250 1
< 0.1%
21300 1
< 0.1%

기타인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct152
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9657444
Minimum0
Maximum1615
Zeros8638
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:38.484516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1615
Range1615
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64.912627
Coefficient of variation (CV)10.880893
Kurtosis270.99411
Mean5.9657444
Median Absolute Deviation (MAD)0
Skewness15.276507
Sum54336
Variance4213.6492
MonotonicityNot monotonic
2023-12-12T19:47:38.664688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8638
94.8%
1 61
 
0.7%
2 56
 
0.6%
3 32
 
0.4%
4 27
 
0.3%
5 16
 
0.2%
10 14
 
0.2%
6 11
 
0.1%
7 10
 
0.1%
9 8
 
0.1%
Other values (142) 235
 
2.6%
ValueCountFrequency (%)
0 8638
94.8%
1 61
 
0.7%
2 56
 
0.6%
3 32
 
0.4%
4 27
 
0.3%
5 16
 
0.2%
6 11
 
0.1%
7 10
 
0.1%
8 6
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
1615 1
< 0.1%
1601 1
< 0.1%
1549 1
< 0.1%
1541 1
< 0.1%
1206 1
< 0.1%
1110 1
< 0.1%
1066 1
< 0.1%
1052 1
< 0.1%
1035 1
< 0.1%
1031 1
< 0.1%

기타운임
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct52
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.11473
Minimum0
Maximum265350
Zeros8800
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:38.877479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum265350
Range265350
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4090.5186
Coefficient of variation (CV)11.887078
Kurtosis2008.5072
Mean344.11473
Median Absolute Deviation (MAD)0
Skewness35.635615
Sum3134197
Variance16732342
MonotonicityNot monotonic
2023-12-12T19:47:39.048623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8800
96.6%
909 60
 
0.7%
1818 56
 
0.6%
2727 30
 
0.3%
3636 24
 
0.3%
4545 14
 
0.2%
5454 11
 
0.1%
9090 10
 
0.1%
6363 10
 
0.1%
8181 6
 
0.1%
Other values (42) 87
 
1.0%
ValueCountFrequency (%)
0 8800
96.6%
152 1
 
< 0.1%
800 1
 
< 0.1%
909 60
 
0.7%
1818 56
 
0.6%
2727 30
 
0.3%
3636 24
 
0.3%
4545 14
 
0.2%
5454 11
 
0.1%
6363 10
 
0.1%
ValueCountFrequency (%)
265350 1
< 0.1%
83628 1
< 0.1%
68175 2
< 0.1%
66357 1
< 0.1%
56358 1
< 0.1%
55449 1
< 0.1%
48177 2
< 0.1%
47268 2
< 0.1%
43632 2
< 0.1%
41814 1
< 0.1%

확정인원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct371
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.305885
Minimum0
Maximum16104
Zeros6549
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:39.225857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile68
Maximum16104
Range16104
Interquartile range (IQR)2

Descriptive statistics

Standard deviation324.44429
Coefficient of variation (CV)8.2543439
Kurtosis796.17918
Mean39.305885
Median Absolute Deviation (MAD)0
Skewness21.893829
Sum357998
Variance105264.1
MonotonicityNot monotonic
2023-12-12T19:47:39.465988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6549
71.9%
2 187
 
2.1%
1 181
 
2.0%
3 148
 
1.6%
4 119
 
1.3%
6 106
 
1.2%
5 106
 
1.2%
7 81
 
0.9%
10 78
 
0.9%
8 77
 
0.8%
Other values (361) 1476
 
16.2%
ValueCountFrequency (%)
0 6549
71.9%
1 181
 
2.0%
2 187
 
2.1%
3 148
 
1.6%
4 119
 
1.3%
5 106
 
1.2%
6 106
 
1.2%
7 81
 
0.9%
8 77
 
0.8%
9 65
 
0.7%
ValueCountFrequency (%)
16104 1
< 0.1%
8540 1
< 0.1%
7781 1
< 0.1%
5258 1
< 0.1%
4272 1
< 0.1%
4230 1
< 0.1%
3831 1
< 0.1%
3682 1
< 0.1%
3650 1
< 0.1%
3588 1
< 0.1%

확정금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct638
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33667.977
Minimum0
Maximum17605970
Zeros6764
Zeros (%)74.3%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-12-12T19:47:39.641744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3700
95-th percentile28232.5
Maximum17605970
Range17605970
Interquartile range (IQR)700

Descriptive statistics

Standard deviation355063.55
Coefficient of variation (CV)10.546032
Kurtosis796.0193
Mean33667.977
Median Absolute Deviation (MAD)0
Skewness22.217207
Sum3.0664794 × 108
Variance1.2607013 × 1011
MonotonicityNot monotonic
2023-12-12T19:47:39.850830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6764
74.3%
1400 88
 
1.0%
700 74
 
0.8%
2100 72
 
0.8%
909 60
 
0.7%
4200 59
 
0.6%
1818 56
 
0.6%
3500 56
 
0.6%
2800 48
 
0.5%
2727 30
 
0.3%
Other values (628) 1801
 
19.8%
ValueCountFrequency (%)
0 6764
74.3%
300 3
 
< 0.1%
500 21
 
0.2%
600 19
 
0.2%
700 74
 
0.8%
900 1
 
< 0.1%
909 60
 
0.7%
1000 13
 
0.1%
1200 24
 
0.3%
1250 9
 
0.1%
ValueCountFrequency (%)
17605970 1
< 0.1%
9088572 1
< 0.1%
8393880 1
< 0.1%
6401830 1
< 0.1%
4553670 1
< 0.1%
4135230 1
< 0.1%
3979250 1
< 0.1%
3957030 1
< 0.1%
3894380 1
< 0.1%
3889050 1
< 0.1%

Interactions

2023-12-12T19:47:32.655899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:18.564410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:20.145345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:21.606754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.143600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:24.684645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:26.237261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:27.965496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:29.413300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:30.780120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:32.829147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:18.719167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:20.267915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:21.734469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.305049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:24.859239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:26.382971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:28.086946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:29.547047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:31.002362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:32.984808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:18.867289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:20.427953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:21.869120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.485422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:25.014746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:26.894275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:28.219902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:29.677754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:31.200494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:33.143437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:19.035498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:20.562381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.046070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.645534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:25.176057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:27.049477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:28.360165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:29.808190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:31.379398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:33.285927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:19.206146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:20.692538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.227603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.807086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:25.343421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:27.178823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:28.525271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:29.918152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:31.563066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:33.447319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:19.377194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:20.834838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.379139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.976256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:25.489506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:27.314412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:28.651834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:30.041995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:31.776661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:33.603153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:19.536057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:20.977150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.540599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:24.133211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:25.630676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:27.447191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:28.829697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:30.157626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:31.966362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:33.750171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:19.696337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:21.152687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.705045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:24.270608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:25.786246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:27.585597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:28.973348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:30.313316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:32.162851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:33.876504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:19.845217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:21.285412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.850158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:24.403530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:25.927655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:27.708876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:29.114019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:30.456202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:32.314761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:34.001105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:19.982511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:21.432021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.004474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:24.542267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:26.079240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:27.836221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:29.271833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:30.608483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:32.468991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:47:40.029495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역사이벤트코드어른인원어른운임청소년인원청소년운임어린이인원어린이운임기타인원기타운임확정인원확정금액
역사1.0000.0000.2930.2820.2050.2100.0880.0760.1160.0290.2990.279
이벤트코드0.0001.0000.1110.1140.0870.0900.1980.1520.4870.0440.1100.112
어른인원0.2930.1111.0000.9990.9010.9320.4780.6750.0000.6571.0000.999
어른운임0.2820.1140.9991.0000.9020.9330.4620.6710.0000.6570.9991.000
청소년인원0.2050.0870.9010.9021.0000.9980.5160.7090.0930.6400.9040.903
청소년운임0.2100.0900.9320.9330.9981.0000.4990.7000.0930.6400.9350.934
어린이인원0.0880.1980.4780.4620.5160.4991.0000.9710.0000.4380.4850.465
어린이운임0.0760.1520.6750.6710.7090.7000.9711.0000.0000.6740.6740.673
기타인원0.1160.4870.0000.0000.0930.0930.0000.0001.0000.0000.0570.000
기타운임0.0290.0440.6570.6570.6400.6400.4380.6740.0001.0000.6570.657
확정인원0.2990.1101.0000.9990.9040.9350.4850.6740.0570.6571.0000.999
확정금액0.2790.1120.9991.0000.9030.9340.4650.6730.0000.6570.9991.000
2023-12-12T19:47:40.224410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역사이벤트코드
역사1.0000.000
이벤트코드0.0001.000
2023-12-12T19:47:40.349002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
어른인원어른운임청소년인원청소년운임어린이인원어린이운임기타인원기타운임확정인원확정금액역사이벤트코드
어른인원1.0000.9730.7900.7880.6210.621-0.096-0.0900.8690.8830.1290.076
어른운임0.9731.0000.8060.8080.6310.633-0.110-0.0920.8440.9080.1240.078
청소년인원0.7900.8061.0000.9980.6160.616-0.091-0.0750.7350.7840.0950.056
청소년운임0.7880.8080.9981.0000.6130.615-0.090-0.0750.7340.7850.0970.058
어린이인원0.6210.6310.6160.6131.0000.999-0.066-0.0530.5720.5990.0330.088
어린이운임0.6210.6330.6160.6150.9991.000-0.065-0.0530.5720.6010.0310.069
기타인원-0.096-0.110-0.091-0.090-0.066-0.0651.0000.7890.3620.1900.0450.333
기타운임-0.090-0.092-0.075-0.075-0.053-0.0530.7891.0000.2490.2890.0140.036
확정인원0.8690.8440.7350.7340.5720.5720.3620.2491.0000.9220.1320.076
확정금액0.8830.9080.7840.7850.5990.6010.1900.2890.9221.0000.1230.077
역사0.1290.1240.0950.0970.0330.0310.0450.0140.1320.1231.0000.000
이벤트코드0.0760.0780.0560.0580.0880.0690.3330.0360.0760.0770.0001.000

Missing values

2023-12-12T19:47:34.196402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:47:34.437437image/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

영업월역사이벤트코드어른인원어른운임청소년인원청소년운임어린이인원어린이운임기타인원기타운임확정인원확정금액
02009-03판암역대전시티즌입장권회수33165002100041000003918500
12009-03판암역쿠폰회수0000006545465454
22009-03신흥역대전시티즌입장권회수6300021000000084000
32009-03신흥역쿠폰회수000000109090109090
42009-03대동역대전시티즌입장권회수136500552750051250007335250
52009-03대동역쿠폰회수00000053481775348177
62009-03대전역대전시티즌입장권회수804000044222100018450000540265500
72009-03대전역쿠폰회수00000037336333733633
82009-03중앙로역대전시티즌입장권회수68340008643000820000016279000
92009-03중앙로역쿠폰회수00000052472685247268
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