Overview

Dataset statistics

Number of variables9
Number of observations112
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory81.2 B

Variable types

Categorical1
Text1
Numeric7

Dataset

Description부산교통공사_도시철도역별수송수입현황_20201231
Author부산교통공사
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3033569

Alerts

승차(명) is highly overall correlated with 승차 일평균(명) and 5 other fieldsHigh correlation
승차 일평균(명) is highly overall correlated with 승차(명) and 5 other fieldsHigh correlation
하차(명) is highly overall correlated with 승차(명) and 5 other fieldsHigh correlation
하차 일평균(명) is highly overall correlated with 승차(명) and 5 other fieldsHigh correlation
승차권(원) is highly overall correlated with 승차(명) and 5 other fieldsHigh correlation
카드승차권(원) is highly overall correlated with 승차(명) and 5 other fieldsHigh correlation
일평균(원) is highly overall correlated with 승차(명) and 5 other fieldsHigh correlation
승차(명) has unique valuesUnique
하차(명) has unique valuesUnique
하차 일평균(명) has unique valuesUnique
승차권(원) has unique valuesUnique
카드승차권(원) has unique valuesUnique
일평균(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:45:59.747891
Analysis finished2023-12-10 16:46:06.342722
Duration6.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

호선
Categorical

Distinct4
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2
43 
1
40 
3
16 
4
13 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 43
38.4%
1 40
35.7%
3 16
 
14.3%
4 13
 
11.6%

Length

2023-12-11T01:46:06.440623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:46:06.593645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 43
38.4%
1 40
35.7%
3 16
 
14.3%
4 13
 
11.6%

역명
Text

Distinct108
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-11T01:46:07.062043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.5446429
Min length3

Characters and Unicode

Total characters397
Distinct characters133
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)92.9%

Sample

1st row다대포해수욕장역
2nd row다대포항역
3rd row낫개역
4th row신장림역
5th row장림역
ValueCountFrequency (%)
연산역 2
 
1.8%
서면역 2
 
1.8%
덕천역 2
 
1.8%
동래역 2
 
1.8%
고촌역 1
 
0.9%
남양산역 1
 
0.9%
부산대양산캠퍼스역 1
 
0.9%
증산역 1
 
0.9%
호포역 1
 
0.9%
금곡역 1
 
0.9%
Other values (98) 98
87.5%
2023-12-11T01:46:07.726632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 112
28.2%
산 19
 
4.8%
대 16
 
4.0%
동 11
 
2.8%
부 9
 
2.3%
장 9
 
2.3%
포 8
 
2.0%
남 7
 
1.8%
서 6
 
1.5%
사 6
 
1.5%
Other values (123) 194
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 396
99.7%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 112
28.3%
산 19
 
4.8%
대 16
 
4.0%
동 11
 
2.8%
부 9
 
2.3%
장 9
 
2.3%
포 8
 
2.0%
남 7
 
1.8%
서 6
 
1.5%
사 6
 
1.5%
Other values (122) 193
48.7%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 396
99.7%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 112
28.3%
산 19
 
4.8%
대 16
 
4.0%
동 11
 
2.8%
부 9
 
2.3%
장 9
 
2.3%
포 8
 
2.0%
남 7
 
1.8%
서 6
 
1.5%
사 6
 
1.5%
Other values (122) 193
48.7%
Common
ValueCountFrequency (%)
· 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 396
99.7%
None 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 112
28.3%
산 19
 
4.8%
대 16
 
4.0%
동 11
 
2.8%
부 9
 
2.3%
장 9
 
2.3%
포 8
 
2.0%
남 7
 
1.8%
서 6
 
1.5%
사 6
 
1.5%
Other values (122) 193
48.7%
None
ValueCountFrequency (%)
· 1
100.0%

승차(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2200846.5
Minimum63084
Maximum9963778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:08.005899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63084
5-th percentile545899.75
Q11048825.2
median1671593.5
Q32807876
95-th percentile5173440.4
Maximum9963778
Range9900694
Interquartile range (IQR)1759050.8

Descriptive statistics

Standard deviation1643616.8
Coefficient of variation (CV)0.74681116
Kurtosis3.741195
Mean2200846.5
Median Absolute Deviation (MAD)794720.5
Skewness1.6361384
Sum2.4649481 × 108
Variance2.701476 × 1012
MonotonicityNot monotonic
2023-12-11T01:46:08.364089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1465238 1
 
0.9%
2144464 1
 
0.9%
1418928 1
 
0.9%
1804786 1
 
0.9%
1019514 1
 
0.9%
646368 1
 
0.9%
1312448 1
 
0.9%
400959 1
 
0.9%
758908 1
 
0.9%
503635 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
63084 1
0.9%
213939 1
0.9%
216770 1
0.9%
400959 1
0.9%
496669 1
0.9%
503635 1
0.9%
580480 1
0.9%
589978 1
0.9%
612516 1
0.9%
645572 1
0.9%
ValueCountFrequency (%)
9963778 1
0.9%
6699955 1
0.9%
5865741 1
0.9%
5572200 1
0.9%
5439768 1
0.9%
5242279 1
0.9%
5117118 1
0.9%
5110754 1
0.9%
5030416 1
0.9%
4930083 1
0.9%

승차 일평균(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6013.2321
Minimum172
Maximum27223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:08.672556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile1491.5
Q12865.5
median4567.5
Q37672
95-th percentile14134.9
Maximum27223
Range27051
Interquartile range (IQR)4806.5

Descriptive statistics

Standard deviation4490.7506
Coefficient of variation (CV)0.74681144
Kurtosis3.7409253
Mean6013.2321
Median Absolute Deviation (MAD)2171
Skewness1.6361086
Sum673482
Variance20166841
MonotonicityNot monotonic
2023-12-11T01:46:08.948466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2852 2
 
1.8%
4003 1
 
0.9%
4529 1
 
0.9%
3877 1
 
0.9%
4931 1
 
0.9%
2786 1
 
0.9%
1766 1
 
0.9%
3586 1
 
0.9%
1096 1
 
0.9%
2074 1
 
0.9%
Other values (101) 101
90.2%
ValueCountFrequency (%)
172 1
0.9%
585 1
0.9%
592 1
0.9%
1096 1
0.9%
1357 1
0.9%
1376 1
0.9%
1586 1
0.9%
1612 1
0.9%
1674 1
0.9%
1764 1
0.9%
ValueCountFrequency (%)
27223 1
0.9%
18306 1
0.9%
16027 1
0.9%
15225 1
0.9%
14863 1
0.9%
14323 1
0.9%
13981 1
0.9%
13964 1
0.9%
13744 1
0.9%
13470 1
0.9%

하차(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2194812.9
Minimum50625
Maximum11489896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:09.185021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50625
5-th percentile522330.25
Q11021289.8
median1616226.5
Q32802054.2
95-th percentile5448634.5
Maximum11489896
Range11439271
Interquartile range (IQR)1780764.5

Descriptive statistics

Standard deviation1769869.2
Coefficient of variation (CV)0.80638726
Kurtosis6.291336
Mean2194812.9
Median Absolute Deviation (MAD)722152
Skewness2.0275596
Sum2.4581905 × 108
Variance3.1324369 × 1012
MonotonicityNot monotonic
2023-12-11T01:46:09.457200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1218265 1
 
0.9%
2329663 1
 
0.9%
1368974 1
 
0.9%
1803951 1
 
0.9%
986280 1
 
0.9%
596987 1
 
0.9%
1392493 1
 
0.9%
520452 1
 
0.9%
626459 1
 
0.9%
385297 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
50625 1
0.9%
166114 1
0.9%
202296 1
0.9%
385297 1
0.9%
519795 1
0.9%
520452 1
0.9%
523867 1
0.9%
577326 1
0.9%
596987 1
0.9%
598329 1
0.9%
ValueCountFrequency (%)
11489896 1
0.9%
7513269 1
0.9%
6109255 1
0.9%
5667927 1
0.9%
5533984 1
0.9%
5513787 1
0.9%
5395328 1
0.9%
5265558 1
0.9%
5219976 1
0.9%
4969761 1
0.9%

하차 일평균(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5996.7411
Minimum138
Maximum31393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:09.670171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138
5-th percentile1426.95
Q12790.5
median4416
Q37655.5
95-th percentile14886.8
Maximum31393
Range31255
Interquartile range (IQR)4865

Descriptive statistics

Standard deviation4835.6717
Coefficient of variation (CV)0.80638328
Kurtosis6.2914247
Mean5996.7411
Median Absolute Deviation (MAD)1973
Skewness2.027587
Sum671635
Variance23383721
MonotonicityNot monotonic
2023-12-11T01:46:09.891237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3329 1
 
0.9%
6365 1
 
0.9%
3740 1
 
0.9%
4929 1
 
0.9%
2695 1
 
0.9%
1631 1
 
0.9%
3805 1
 
0.9%
1422 1
 
0.9%
1712 1
 
0.9%
1053 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
138 1
0.9%
454 1
0.9%
553 1
0.9%
1053 1
0.9%
1420 1
0.9%
1422 1
0.9%
1431 1
0.9%
1577 1
0.9%
1631 1
0.9%
1635 1
0.9%
ValueCountFrequency (%)
31393 1
0.9%
20528 1
0.9%
16692 1
0.9%
15486 1
0.9%
15120 1
0.9%
15065 1
0.9%
14741 1
0.9%
14387 1
0.9%
14262 1
0.9%
13579 1
0.9%

승차권(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85985798
Minimum1139300
Maximum3.351041 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:10.134486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1139300
5-th percentile12825675
Q140725032
median68537805
Q31.2367954 × 108
95-th percentile2.1547303 × 108
Maximum3.351041 × 108
Range3.339648 × 108
Interquartile range (IQR)82954505

Descriptive statistics

Standard deviation65786915
Coefficient of variation (CV)0.76509048
Kurtosis2.2435562
Mean85985798
Median Absolute Deviation (MAD)32170000
Skewness1.4220076
Sum9.6304093 × 109
Variance4.3279182 × 1015
MonotonicityNot monotonic
2023-12-11T01:46:10.403416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39557600 1
 
0.9%
264677040 1
 
0.9%
49030550 1
 
0.9%
111631950 1
 
0.9%
83822100 1
 
0.9%
43372800 1
 
0.9%
73299000 1
 
0.9%
12321890 1
 
0.9%
35640840 1
 
0.9%
21473320 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
1139300 1
0.9%
4743600 1
0.9%
6263550 1
0.9%
8334800 1
0.9%
12321890 1
0.9%
12822100 1
0.9%
12828600 1
0.9%
13724700 1
0.9%
13840600 1
0.9%
13898300 1
0.9%
ValueCountFrequency (%)
335104100 1
0.9%
304409950 1
0.9%
264677040 1
0.9%
261265650 1
0.9%
245893650 1
0.9%
228229210 1
0.9%
205036150 1
0.9%
192522040 1
0.9%
190776470 1
0.9%
169822500 1
0.9%

카드승차권(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.619648 × 109
Minimum25731240
Maximum8.2702432 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:10.697447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25731240
5-th percentile2.5038019 × 108
Q17.3102059 × 108
median1.2279802 × 109
Q32.1772831 × 109
95-th percentile4.0419782 × 109
Maximum8.2702432 × 109
Range8.244512 × 109
Interquartile range (IQR)1.4462625 × 109

Descriptive statistics

Standard deviation1.3423733 × 109
Coefficient of variation (CV)0.8288056
Kurtosis5.2124599
Mean1.619648 × 109
Median Absolute Deviation (MAD)6.3707391 × 108
Skewness1.8959001
Sum1.8140058 × 1011
Variance1.8019662 × 1018
MonotonicityNot monotonic
2023-12-11T01:46:10.944907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
873834085 1
 
0.9%
2226787483 1
 
0.9%
828877537 1
 
0.9%
1452956646 1
 
0.9%
929158727 1
 
0.9%
557053331 1
 
0.9%
1141885632 1
 
0.9%
219497322 1
 
0.9%
450152108 1
 
0.9%
301886815 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
25731240 1
0.9%
88392855 1
0.9%
146497258 1
0.9%
211744303 1
0.9%
219497322 1
0.9%
225992030 1
0.9%
270334133 1
0.9%
301886815 1
0.9%
337694619 1
0.9%
358027906 1
0.9%
ValueCountFrequency (%)
8270243229 1
0.9%
5799319262 1
0.9%
4890488855 1
0.9%
4786963476 1
0.9%
4718035053 1
0.9%
4108937479 1
0.9%
3987193364 1
0.9%
3859204834 1
0.9%
3817766181 1
0.9%
3636777627 1
0.9%

일평균(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4660201.7
Minimum73417
Maximum23511878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:11.466075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73417
5-th percentile720749.15
Q12109512.8
median3541514.5
Q36583799.2
95-th percentile11613575
Maximum23511878
Range23438461
Interquartile range (IQR)4474286.5

Descriptive statistics

Standard deviation3827066.4
Coefficient of variation (CV)0.82122335
Kurtosis5.038464
Mean4660201.7
Median Absolute Deviation (MAD)1779680
Skewness1.8617786
Sum5.2194259 × 108
Variance1.4646437 × 1013
MonotonicityNot monotonic
2023-12-11T01:46:11.629490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2495606 1
 
0.9%
6807280 1
 
0.9%
2398656 1
 
0.9%
4274832 1
 
0.9%
2767707 1
 
0.9%
1640509 1
 
0.9%
3320177 1
 
0.9%
633386 1
 
0.9%
1327303 1
 
0.9%
883498 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
73417 1
0.9%
254471 1
0.9%
423038 1
0.9%
595650 1
0.9%
633386 1
0.9%
652498 1
0.9%
776591 1
0.9%
883498 1
0.9%
977346 1
0.9%
1013269 1
0.9%
ValueCountFrequency (%)
23511878 1
0.9%
16366382 1
0.9%
14193713 1
0.9%
13562647 1
0.9%
13454587 1
0.9%
11850182 1
0.9%
11419987 1
0.9%
10991263 1
0.9%
10984621 1
0.9%
10287357 1
0.9%

Interactions

2023-12-11T01:46:05.072156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.112035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.794004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.528701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.217589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.967868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.208005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:05.177931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.211474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.902632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.615763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.312122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.091146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.318462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:05.307624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.317541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.986327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.716206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.418993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.572629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.418413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:05.490926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.412109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.078790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.813483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.511686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.713031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.539012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:05.622853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.496472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.199690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.910696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.620684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.860324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.667292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:05.743567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.599404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.330981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.019057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.746509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.001599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.808002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:05.859843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.701293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.428423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.125998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.854158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.104768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.926418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:46:11.756890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
호선1.0000.6650.6650.6020.6020.5870.6380.684
승차(명)0.6651.0001.0000.9840.9840.7780.9790.969
승차 일평균(명)0.6651.0001.0000.9840.9840.7780.9790.969
하차(명)0.6020.9840.9841.0001.0000.8040.9820.974
하차 일평균(명)0.6020.9840.9841.0001.0000.8040.9820.974
승차권(원)0.5870.7780.7780.8040.8041.0000.7990.794
카드승차권(원)0.6380.9790.9790.9820.9820.7991.0000.997
일평균(원)0.6840.9690.9690.9740.9740.7940.9971.000
2023-12-11T01:46:11.928068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)호선
승차(명)1.0001.0000.9910.9910.8970.9770.9770.350
승차 일평균(명)1.0001.0000.9910.9910.8970.9770.9770.350
하차(명)0.9910.9911.0001.0000.9000.9730.9730.306
하차 일평균(명)0.9910.9911.0001.0000.9000.9730.9730.306
승차권(원)0.8970.8970.9000.9001.0000.9250.9320.417
카드승차권(원)0.9770.9770.9730.9730.9251.0001.0000.329
일평균(원)0.9770.9770.9730.9730.9321.0001.0000.363
호선0.3500.3500.3060.3060.4170.3290.3631.000

Missing values

2023-12-11T01:46:06.047858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:46:06.267804image/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

호선역명승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
01다대포해수욕장역1465238400312182653329395576008738340852495606
11다대포항역1021578279111730633205437101507161625352076155
21낫개역14621543995148161040487672740010476535513072079
31신장림역98709526978987882456416520507145354492066086
41장림역99086727078928902440430605507552712382181234
51동매역1216831332510800802951433605509112515882608230
61신평역19726875390198685254297988997014707904714236832
71하단역543976814863496976113579161166510385920483410984621
81당리역19268485265200914454896789901012474587803593874
91사하역1315207359311499383142606687009039950542635693
호선역명승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
1024충렬사역93402025529641792634179238503890400061111923
1034명장역93039425428984162455287069604867998921408489
1044서동역6929131893727515198820014050337694619977346
1054금사역58048015865238671431128286003580279061013269
1064반여농산물시장역651982178167143618356263550211744303595650
1074석대역630841725062513811393002573124073417
1084영산대역87932424038636522360583545505827504391751653
1094동부산대학역82511422547728202112343462004653781931365367
1104고촌역213939585166114454474360088392855254471
1114안평역6125161674598329163513898300270334133776591