Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows60
Duplicate rows (%)0.6%
Total size in memory761.7 KiB
Average record size in memory78.0 B

Variable types

Numeric6
Categorical1
Text1

Dataset

Description사용월,호선명,지하철역,유임승차인원,무임승차인원,유임하차인원,무임하차인원,작업일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12251/S/1/datasetView.do

Alerts

Dataset has 60 (0.6%) duplicate rowsDuplicates
사용월 is highly overall correlated with 작업일자High correlation
유임승차인원 is highly overall correlated with 무임승차인원 and 2 other fieldsHigh correlation
무임승차인원 is highly overall correlated with 유임승차인원 and 2 other fieldsHigh correlation
유임하차인원 is highly overall correlated with 유임승차인원 and 2 other fieldsHigh correlation
무임하차인원 is highly overall correlated with 유임승차인원 and 2 other fieldsHigh correlation
작업일자 is highly overall correlated with 사용월High correlation
유임하차인원 has 128 (1.3%) zerosZeros
무임하차인원 has 131 (1.3%) zerosZeros

Reproduction

Analysis started2024-05-11 02:10:49.058067
Analysis finished2024-05-11 02:11:08.938542
Duration19.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사용월
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201948.05
Minimum201501
Maximum202404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:11:09.344828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201501
5-th percentile201510
Q1201708
median201911
Q3202203
95-th percentile202312
Maximum202404
Range903
Interquartile range (IQR)495

Descriptive statistics

Standard deviation264.81211
Coefficient of variation (CV)0.0013112883
Kurtosis-1.1705664
Mean201948.05
Median Absolute Deviation (MAD)206
Skewness-0.014439859
Sum2.0194805 × 109
Variance70125.451
MonotonicityNot monotonic
2024-05-11T02:11:10.092900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202402 192
 
1.9%
202205 182
 
1.8%
201803 173
 
1.7%
201609 170
 
1.7%
201610 157
 
1.6%
201907 120
 
1.2%
202201 107
 
1.1%
202005 107
 
1.1%
202404 106
 
1.1%
201804 102
 
1.0%
Other values (102) 8584
85.8%
ValueCountFrequency (%)
201501 42
0.4%
201502 44
0.4%
201503 37
0.4%
201504 37
0.4%
201505 31
 
0.3%
201506 36
 
0.4%
201507 72
0.7%
201508 66
0.7%
201509 91
0.9%
201510 79
0.8%
ValueCountFrequency (%)
202404 106
1.1%
202403 91
0.9%
202402 192
1.9%
202401 88
0.9%
202312 93
0.9%
202311 86
0.9%
202310 87
0.9%
202309 82
0.8%
202308 100
1.0%
202307 89
0.9%

호선명
Categorical

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
5호선
944 
7호선
890 
2호선
855 
경부선
666 
6호선
659 
Other values (23)
5986 

Length

Max length8
Median length3
Mean length3.2121
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5호선
2nd row경의선
3rd row경원선
4th row경강선
5th row중앙선

Common Values

ValueCountFrequency (%)
5호선 944
 
9.4%
7호선 890
 
8.9%
2호선 855
 
8.6%
경부선 666
 
6.7%
6호선 659
 
6.6%
3호선 592
 
5.9%
분당선 583
 
5.8%
경원선 487
 
4.9%
경의선 479
 
4.8%
9호선 439
 
4.4%
Other values (18) 3406
34.1%

Length

2024-05-11T02:11:10.759567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5호선 944
 
9.2%
7호선 890
 
8.7%
2호선 855
 
8.4%
경부선 666
 
6.5%
6호선 659
 
6.4%
3호선 592
 
5.8%
분당선 583
 
5.7%
경원선 487
 
4.8%
경의선 479
 
4.7%
9호선 439
 
4.3%
Other values (18) 3636
35.5%

지하철역
Text

Distinct593
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:11:11.859979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length2
Mean length3.4643
Min length2

Characters and Unicode

Total characters34643
Distinct characters306
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row방이
2nd row서강대
3rd row보산
4th row여주
5th row팔당
ValueCountFrequency (%)
서울역 77
 
0.8%
종로3가 61
 
0.6%
홍대입구 55
 
0.5%
공덕 54
 
0.5%
김포공항 54
 
0.5%
도봉산 50
 
0.5%
디지털미디어시티 50
 
0.5%
고속터미널 49
 
0.5%
수서 43
 
0.4%
약수 42
 
0.4%
Other values (582) 9466
94.7%
2024-05-11T02:11:13.623016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 1185
 
3.4%
구 1072
 
3.1%
( 1036
 
3.0%
) 1036
 
3.0%
산 818
 
2.4%
동 781
 
2.3%
신 742
 
2.1%
원 698
 
2.0%
청 630
 
1.8%
정 551
 
1.6%
Other values (296) 26094
75.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32220
93.0%
Open Punctuation 1036
 
3.0%
Close Punctuation 1036
 
3.0%
Decimal Number 219
 
0.6%
Uppercase Letter 84
 
0.2%
Other Punctuation 47
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 1185
 
3.7%
구 1072
 
3.3%
산 818
 
2.5%
동 781
 
2.4%
신 742
 
2.3%
원 698
 
2.2%
청 630
 
2.0%
정 551
 
1.7%
천 548
 
1.7%
서 476
 
1.5%
Other values (283) 24719
76.7%
Decimal Number
ValueCountFrequency (%)
3 98
44.7%
4 50
22.8%
1 27
 
12.3%
5 15
 
6.8%
9 15
 
6.8%
2 14
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
D 56
66.7%
P 28
33.3%
Other Punctuation
ValueCountFrequency (%)
. 40
85.1%
? 7
 
14.9%
Open Punctuation
ValueCountFrequency (%)
( 1036
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1036
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32220
93.0%
Common 2339
 
6.8%
Latin 84
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 1185
 
3.7%
구 1072
 
3.3%
산 818
 
2.5%
동 781
 
2.4%
신 742
 
2.3%
원 698
 
2.2%
청 630
 
2.0%
정 551
 
1.7%
천 548
 
1.7%
서 476
 
1.5%
Other values (283) 24719
76.7%
Common
ValueCountFrequency (%)
( 1036
44.3%
) 1036
44.3%
3 98
 
4.2%
4 50
 
2.1%
. 40
 
1.7%
1 27
 
1.2%
5 15
 
0.6%
9 15
 
0.6%
2 14
 
0.6%
? 7
 
0.3%
Latin
ValueCountFrequency (%)
D 56
66.7%
P 28
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32220
93.0%
ASCII 2423
 
7.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 1185
 
3.7%
구 1072
 
3.3%
산 818
 
2.5%
동 781
 
2.4%
신 742
 
2.3%
원 698
 
2.2%
청 630
 
2.0%
정 551
 
1.7%
천 548
 
1.7%
서 476
 
1.5%
Other values (283) 24719
76.7%
ASCII
ValueCountFrequency (%)
( 1036
42.8%
) 1036
42.8%
3 98
 
4.0%
D 56
 
2.3%
4 50
 
2.1%
. 40
 
1.7%
P 28
 
1.2%
1 27
 
1.1%
5 15
 
0.6%
9 15
 
0.6%
Other values (3) 22
 
0.9%

유임승차인원
Real number (ℝ)

HIGH CORRELATION 

Distinct9768
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289474.88
Minimum1
Maximum3266271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:11:14.213424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21333.5
Q192222.25
median207479.5
Q3377352.75
95-th percentile835678.9
Maximum3266271
Range3266270
Interquartile range (IQR)285130.5

Descriptive statistics

Standard deviation305084.73
Coefficient of variation (CV)1.0539247
Kurtosis14.379013
Mean289474.88
Median Absolute Deviation (MAD)130409.5
Skewness2.996217
Sum2.8947488 × 109
Variance9.3076694 × 1010
MonotonicityNot monotonic
2024-05-11T02:11:14.832593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
0.2%
2 11
 
0.1%
3 10
 
0.1%
4 8
 
0.1%
5 7
 
0.1%
7 6
 
0.1%
6 6
 
0.1%
11 5
 
0.1%
14 5
 
0.1%
16 4
 
< 0.1%
Other values (9758) 9920
99.2%
ValueCountFrequency (%)
1 18
0.2%
2 11
0.1%
3 10
0.1%
4 8
0.1%
5 7
 
0.1%
6 6
 
0.1%
7 6
 
0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
3266271 1
< 0.1%
3221329 1
< 0.1%
3132653 1
< 0.1%
3034426 1
< 0.1%
2974554 1
< 0.1%
2870951 1
< 0.1%
2848004 1
< 0.1%
2790176 1
< 0.1%
2784689 1
< 0.1%
2716424 1
< 0.1%

무임승차인원
Real number (ℝ)

HIGH CORRELATION 

Distinct9462
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73817.542
Minimum0
Maximum3353256
Zeros79
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:11:15.261628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6211.95
Q123497.5
median46482.5
Q380627.5
95-th percentile210766.2
Maximum3353256
Range3353256
Interquartile range (IQR)57130

Descriptive statistics

Standard deviation125504.93
Coefficient of variation (CV)1.7002047
Kurtosis134.69628
Mean73817.542
Median Absolute Deviation (MAD)26668
Skewness9.0520007
Sum7.3817542 × 108
Variance1.5751487 × 1010
MonotonicityNot monotonic
2024-05-11T02:11:15.951512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
0.8%
1 30
 
0.3%
2 13
 
0.1%
36111 3
 
< 0.1%
42330 3
 
< 0.1%
50143 3
 
< 0.1%
156172 3
 
< 0.1%
39602 3
 
< 0.1%
13197 3
 
< 0.1%
3 3
 
< 0.1%
Other values (9452) 9857
98.6%
ValueCountFrequency (%)
0 79
0.8%
1 30
 
0.3%
2 13
 
0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
27 1
 
< 0.1%
80 1
 
< 0.1%
86 1
 
< 0.1%
ValueCountFrequency (%)
3353256 1
< 0.1%
2935195 1
< 0.1%
2150490 1
< 0.1%
2035737 1
< 0.1%
1879268 1
< 0.1%
1858869 1
< 0.1%
1797395 1
< 0.1%
1773468 1
< 0.1%
1682965 1
< 0.1%
1674131 1
< 0.1%

유임하차인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9728
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270222.64
Minimum0
Maximum3265282
Zeros128
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:11:16.367868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16833.75
Q176260.5
median184091.5
Q3358658.5
95-th percentile796343.55
Maximum3265282
Range3265282
Interquartile range (IQR)282398

Descriptive statistics

Standard deviation300579.45
Coefficient of variation (CV)1.11234
Kurtosis14.433296
Mean270222.64
Median Absolute Deviation (MAD)121867.5
Skewness3.0044089
Sum2.7022264 × 109
Variance9.0348003 × 1010
MonotonicityNot monotonic
2024-05-11T02:11:16.805738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
 
1.3%
398521 3
 
< 0.1%
678391 3
 
< 0.1%
101857 2
 
< 0.1%
508767 2
 
< 0.1%
20012 2
 
< 0.1%
1079 2
 
< 0.1%
288959 2
 
< 0.1%
38942 2
 
< 0.1%
236001 2
 
< 0.1%
Other values (9718) 9852
98.5%
ValueCountFrequency (%)
0 128
1.3%
3 2
 
< 0.1%
8 1
 
< 0.1%
19 1
 
< 0.1%
33 1
 
< 0.1%
39 1
 
< 0.1%
113 1
 
< 0.1%
139 2
 
< 0.1%
279 1
 
< 0.1%
286 1
 
< 0.1%
ValueCountFrequency (%)
3265282 1
< 0.1%
3161388 1
< 0.1%
3090108 1
< 0.1%
2996605 1
< 0.1%
2900273 1
< 0.1%
2866156 1
< 0.1%
2829635 1
< 0.1%
2802762 1
< 0.1%
2633063 1
< 0.1%
2606386 1
< 0.1%

무임하차인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9432
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55210.34
Minimum0
Maximum381040
Zeros131
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:11:17.245366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5746.55
Q122164.5
median43665
Q372553
95-th percentile146615.35
Maximum381040
Range381040
Interquartile range (IQR)50388.5

Descriptive statistics

Standard deviation47186.952
Coefficient of variation (CV)0.85467599
Kurtosis5.8514105
Mean55210.34
Median Absolute Deviation (MAD)24516.5
Skewness1.9519421
Sum5.521034 × 108
Variance2.2266084 × 109
MonotonicityNot monotonic
2024-05-11T02:11:17.678114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 131
 
1.3%
33617 3
 
< 0.1%
28506 3
 
< 0.1%
5760 3
 
< 0.1%
12530 3
 
< 0.1%
25423 3
 
< 0.1%
8741 3
 
< 0.1%
40986 3
 
< 0.1%
15091 3
 
< 0.1%
45503 3
 
< 0.1%
Other values (9422) 9842
98.4%
ValueCountFrequency (%)
0 131
1.3%
53 1
 
< 0.1%
64 1
 
< 0.1%
66 2
 
< 0.1%
70 1
 
< 0.1%
71 1
 
< 0.1%
75 2
 
< 0.1%
78 1
 
< 0.1%
80 1
 
< 0.1%
81 1
 
< 0.1%
ValueCountFrequency (%)
381040 1
< 0.1%
371373 1
< 0.1%
362545 1
< 0.1%
360425 1
< 0.1%
359562 1
< 0.1%
353918 1
< 0.1%
350732 1
< 0.1%
343528 1
< 0.1%
340621 1
< 0.1%
337733 1
< 0.1%

작업일자
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20195881
Minimum20150206
Maximum20240503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:11:18.126613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150206
5-th percentile20151108
Q120170903
median20200203
Q320220503
95-th percentile20240103
Maximum20240503
Range90297
Interquartile range (IQR)49600

Descriptive statistics

Standard deviation26618.045
Coefficient of variation (CV)0.0013179938
Kurtosis-1.1876221
Mean20195881
Median Absolute Deviation (MAD)20500
Skewness-0.041579576
Sum2.0195881 × 1011
Variance7.0852034 × 108
MonotonicityNot monotonic
2024-05-11T02:11:18.566128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240303 192
 
1.9%
20220613 182
 
1.8%
20180403 173
 
1.7%
20161008 170
 
1.7%
20161108 157
 
1.6%
20190803 120
 
1.2%
20220203 107
 
1.1%
20200603 107
 
1.1%
20240503 106
 
1.1%
20180503 102
 
1.0%
Other values (102) 8584
85.8%
ValueCountFrequency (%)
20150206 42
0.4%
20150311 44
0.4%
20150408 37
0.4%
20150508 37
0.4%
20150608 31
 
0.3%
20150708 36
 
0.4%
20150808 72
0.7%
20150908 66
0.7%
20151008 91
0.9%
20151108 79
0.8%
ValueCountFrequency (%)
20240503 106
1.1%
20240403 91
0.9%
20240303 192
1.9%
20240203 88
0.9%
20240103 93
0.9%
20231203 86
0.9%
20231103 87
0.9%
20231003 82
0.8%
20230903 100
1.0%
20230803 89
0.9%

Interactions

2024-05-11T02:11:05.776656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:54.008925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:56.450435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:58.871912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:01.445031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:03.519358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:06.048383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:54.400538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:56.799968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:59.285248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:01.737147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:03.830676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:06.359003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:54.793762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:57.188756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:59.735605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:02.043395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:04.207834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:06.854574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:55.213581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:57.627791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:00.185786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:02.381914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:04.732285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:07.239586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:55.637920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:58.026104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:00.592694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:02.684663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:05.049994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:07.565008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:56.052547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:10:58.486370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:01.043905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:03.124929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:11:05.427332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:11:18.772320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용월호선명유임승차인원무임승차인원유임하차인원무임하차인원작업일자
사용월1.0000.1680.1330.3880.1980.1330.998
호선명0.1681.0000.5040.1410.4830.6260.166
유임승차인원0.1330.5041.0000.5780.9860.7180.127
무임승차인원0.3880.1410.5781.0000.0000.2370.413
유임하차인원0.1980.4830.9860.0001.0000.6890.203
무임하차인원0.1330.6260.7180.2370.6891.0000.135
작업일자0.9980.1660.1270.4130.2030.1351.000
2024-05-11T02:11:19.064111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용월유임승차인원무임승차인원유임하차인원무임하차인원작업일자호선명
사용월1.000-0.110-0.1640.035-0.0430.9910.062
유임승차인원-0.1101.0000.8190.9220.849-0.1080.206
무임승차인원-0.1640.8191.0000.6640.938-0.1610.053
유임하차인원0.0350.9220.6641.0000.7980.0370.195
무임하차인원-0.0430.8490.9380.7981.000-0.0400.279
작업일자0.991-0.108-0.1610.037-0.0401.0000.062
호선명0.0620.2060.0530.1950.2790.0621.000

Missing values

2024-05-11T02:11:08.028422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:11:08.653495image/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

사용월호선명지하철역유임승차인원무임승차인원유임하차인원무임하차인원작업일자
100422023015호선방이154154422291563694192320230203
8430202304경의선서강대5841411768583291108320230503
18240202201경원선보산2494910838233851032620220203
7744202305경강선여주7002520913701132082720230603
28745202008중앙선팔당18982679618775687920200903
527482017045호선천호(풍납토성)49243014908951659115735220170503
9152024039호선신방화161649531441569765292520240403
353732019082호선신촌1244032138992128890313689020190903
187432021129호선사평7548217366736661764120220103
39109201903중앙선덕소147684528091461355295520190403
사용월호선명지하철역유임승차인원무임승차인원유임하차인원무임하차인원작업일자
55192201612경부선진위45451804440423749220170108
6568202307경원선도봉9987171163947237098020230803
398592019015호선동대문역사문화공원77768897590305973320190203
456392018031호선종로3가69869140137564780137137320180403
38383201904경춘선강촌18108556415320534820190503
572102016092호선신정네거리246735634772462296419720161008
53664201703분당선정자455960876745092178659520170403
61480201603경인선인천9954744402800113918320160408
28728202008장항선온양온천6020730475604353135220200903
264452020114호선동대문역사문화공원(DDP)342642469403234684225320201203

Duplicate rows

Most frequently occurring

사용월호선명지하철역유임승차인원무임승차인원유임하차인원무임하차인원작업일자# duplicates
34201907경원선청량리(서울시립대입구)370626156172398521164858201908033
02016092호선신정네거리2467356347724622964197201610082
12016093호선양재9968951453481066988145884201610082
22016094호선상계539600112455471998110826201610082
32016097호선광명사거리665438140973634363144048201610082
4201609경부선가산디지털단지4286532872150876731101201610082
5201609경부선의왕2194893700020448436891201610082
6201609경인선부개2565466241823600160566201610082
7201609과천선선바위2329143180018570930197201610082
8201609분당선대모산입구77163280086731027307201610082