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

Description부산도시철도역별호선별수송,수입현황(2018년)
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
승차 일평균(명) has unique valuesUnique
하차(명) has unique valuesUnique
승차권(원) has unique valuesUnique
카드승차권(원) has unique valuesUnique
일평균(원) has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:45:23.707647
Analysis finished2023-12-10 16:45:29.636334
Duration5.93 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

2023-12-11T01:45:29.716578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:45:29.829006image/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
2023-12-11T01:45:30.186863image/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%
2023-12-11T01:45:30.638812image/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%
Mean3002171.6
Minimum57025
Maximum14625899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:45:30.816324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57025
5-th percentile684568.2
Q11346595.8
median2268394
Q33747049.5
95-th percentile7664863.6
Maximum14625899
Range14568874
Interquartile range (IQR)2400453.8

Descriptive statistics

Standard deviation2426999.9
Coefficient of variation (CV)0.8084148
Kurtosis4.2446571
Mean3002171.6
Median Absolute Deviation (MAD)1085628
Skewness1.7822162
Sum3.3624322 × 108
Variance5.8903286 × 1012
MonotonicityNot monotonic
2023-12-11T01:45:30.983472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1796020 1
 
0.9%
2471761 1
 
0.9%
1807286 1
 
0.9%
2503850 1
 
0.9%
1291486 1
 
0.9%
879932 1
 
0.9%
1520238 1
 
0.9%
418030 1
 
0.9%
977473 1
 
0.9%
637359 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
57025 1
0.9%
270745 1
0.9%
362423 1
0.9%
418030 1
0.9%
637359 1
0.9%
652985 1
0.9%
710409 1
0.9%
715287 1
0.9%
773020 1
0.9%
781704 1
0.9%
ValueCountFrequency (%)
14625899 1
0.9%
9931778 1
0.9%
8729685 1
0.9%
8218889 1
0.9%
8150808 1
0.9%
7683480 1
0.9%
7649632 1
0.9%
7540095 1
0.9%
7433902 1
0.9%
6962542 1
0.9%

승차 일평균(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8225.1071
Minimum156
Maximum40071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:45:31.131568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum156
5-th percentile1875.35
Q13689.25
median6215
Q310265.75
95-th percentile20999.85
Maximum40071
Range39915
Interquartile range (IQR)6576.5

Descriptive statistics

Standard deviation6649.3071
Coefficient of variation (CV)0.80841586
Kurtosis4.24473
Mean8225.1071
Median Absolute Deviation (MAD)2974.5
Skewness1.7822258
Sum921212
Variance44213285
MonotonicityNot monotonic
2023-12-11T01:45:31.297023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4921 1
 
0.9%
6772 1
 
0.9%
4951 1
 
0.9%
6860 1
 
0.9%
3538 1
 
0.9%
2411 1
 
0.9%
4165 1
 
0.9%
1145 1
 
0.9%
2678 1
 
0.9%
1746 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
156 1
0.9%
742 1
0.9%
993 1
0.9%
1145 1
0.9%
1746 1
0.9%
1789 1
0.9%
1946 1
0.9%
1960 1
0.9%
2118 1
0.9%
2142 1
0.9%
ValueCountFrequency (%)
40071 1
0.9%
27210 1
0.9%
23917 1
0.9%
22518 1
0.9%
22331 1
0.9%
21051 1
0.9%
20958 1
0.9%
20658 1
0.9%
20367 1
0.9%
19075 1
0.9%

하차(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2987886.8
Minimum45997
Maximum16320185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:45:31.450805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45997
5-th percentile644603.25
Q11270043
median2166238.5
Q33575026.8
95-th percentile7867710.4
Maximum16320185
Range16274188
Interquartile range (IQR)2304983.8

Descriptive statistics

Standard deviation2613536.6
Coefficient of variation (CV)0.87471073
Kurtosis5.9948225
Mean2987886.8
Median Absolute Deviation (MAD)1029884
Skewness2.082504
Sum3.3464332 × 108
Variance6.8305738 × 1012
MonotonicityNot monotonic
2023-12-11T01:45:31.610744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1493341 1
 
0.9%
2634164 1
 
0.9%
1685673 1
 
0.9%
2511231 1
 
0.9%
1239218 1
 
0.9%
819674 1
 
0.9%
1618406 1
 
0.9%
575014 1
 
0.9%
788974 1
 
0.9%
465845 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
45997 1
0.9%
200619 1
0.9%
339888 1
0.9%
465845 1
0.9%
575014 1
0.9%
623486 1
0.9%
661881 1
0.9%
729052 1
0.9%
734894 1
0.9%
750871 1
0.9%
ValueCountFrequency (%)
16320185 1
0.9%
11484847 1
0.9%
9154960 1
0.9%
8829819 1
0.9%
8530471 1
0.9%
7960980 1
0.9%
7791399 1
0.9%
7580077 1
0.9%
7567828 1
0.9%
7286209 1
0.9%

하차 일평균(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8185.9821
Minimum126
Maximum44713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:45:31.809166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile1765.75
Q13479.75
median5935
Q39794.25
95-th percentile21555.25
Maximum44713
Range44587
Interquartile range (IQR)6314.5

Descriptive statistics

Standard deviation7160.3705
Coefficient of variation (CV)0.87471123
Kurtosis5.9948648
Mean8185.9821
Median Absolute Deviation (MAD)2822
Skewness2.082492
Sum916830
Variance51270906
MonotonicityNot monotonic
2023-12-11T01:45:31.971016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3348 2
 
1.8%
4091 1
 
0.9%
7870 1
 
0.9%
4618 1
 
0.9%
6880 1
 
0.9%
3395 1
 
0.9%
2246 1
 
0.9%
4434 1
 
0.9%
1575 1
 
0.9%
2162 1
 
0.9%
Other values (101) 101
90.2%
ValueCountFrequency (%)
126 1
0.9%
550 1
0.9%
931 1
0.9%
1276 1
0.9%
1575 1
0.9%
1708 1
0.9%
1813 1
0.9%
1997 1
0.9%
2013 1
0.9%
2057 1
0.9%
ValueCountFrequency (%)
44713 1
0.9%
31465 1
0.9%
25082 1
0.9%
24191 1
0.9%
23371 1
0.9%
21811 1
0.9%
21346 1
0.9%
20767 1
0.9%
20734 1
0.9%
19962 1
0.9%

승차권(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7967292 × 108
Minimum1611800
Maximum9.486935 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:45:32.098696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1611800
5-th percentile24746097
Q171636618
median1.237548 × 108
Q32.1693288 × 108
95-th percentile4.7963178 × 108
Maximum9.486935 × 108
Range9.470817 × 108
Interquartile range (IQR)1.4529626 × 108

Descriptive statistics

Standard deviation1.7155674 × 108
Coefficient of variation (CV)0.95482802
Kurtosis6.0609569
Mean1.7967292 × 108
Median Absolute Deviation (MAD)63161200
Skewness2.2169369
Sum2.0123367 × 1010
Variance2.9431715 × 1016
MonotonicityNot monotonic
2023-12-11T01:45:32.269834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86383130 1
 
0.9%
330398360 1
 
0.9%
92153470 1
 
0.9%
214182190 1
 
0.9%
140152150 1
 
0.9%
69777650 1
 
0.9%
116770360 1
 
0.9%
16971210 1
 
0.9%
55598140 1
 
0.9%
38561150 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
1611800 1
0.9%
10381100 1
0.9%
13264680 1
0.9%
14847650 1
0.9%
16971210 1
0.9%
21232950 1
0.9%
27620490 1
0.9%
28490050 1
0.9%
32112470 1
0.9%
33577200 1
0.9%
ValueCountFrequency (%)
948693500 1
0.9%
898265440 1
0.9%
761896680 1
0.9%
572832430 1
0.9%
554469530 1
0.9%
513957890 1
0.9%
451546790 1
0.9%
446137580 1
0.9%
439674030 1
0.9%
430942980 1
0.9%

카드승차권(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2534787 × 109
Minimum27694169
Maximum1.225767 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:45:32.416064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27694169
5-th percentile3.4938645 × 108
Q19.6898447 × 108
median1.5745072 × 109
Q32.9578359 × 109
95-th percentile6.341364 × 109
Maximum1.225767 × 1010
Range1.2229976 × 1010
Interquartile range (IQR)1.9888514 × 109

Descriptive statistics

Standard deviation2.0278276 × 109
Coefficient of variation (CV)0.89986547
Kurtosis5.577485
Mean2.2534787 × 109
Median Absolute Deviation (MAD)8.3773769 × 108
Skewness2.0623775
Sum2.5238961 × 1011
Variance4.112085 × 1018
MonotonicityNot monotonic
2023-12-11T01:45:32.842994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1161975467 1
 
0.9%
2457196487 1
 
0.9%
1060917399 1
 
0.9%
2059382506 1
 
0.9%
1216632813 1
 
0.9%
793582643 1
 
0.9%
1338934225 1
 
0.9%
274361165 1
 
0.9%
627621191 1
 
0.9%
406627670 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
27694169 1
0.9%
120153657 1
0.9%
223483177 1
0.9%
254204992 1
0.9%
274361165 1
0.9%
310875658 1
0.9%
380895277 1
0.9%
406627670 1
0.9%
436954001 1
0.9%
447153581 1
0.9%
ValueCountFrequency (%)
12257669985 1
0.9%
8734350803 1
0.9%
7545793199 1
0.9%
7382089407 1
0.9%
7249272878 1
0.9%
6403245247 1
0.9%
6290733840 1
0.9%
6077988072 1
0.9%
5529619300 1
0.9%
5465168241 1
0.9%

일평균(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6666168.8
Minimum80290
Maximum36043659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:45:32.991629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80290
5-th percentile1040736.5
Q12884767.5
median4580914.5
Q38683447.2
95-th percentile18486864
Maximum36043659
Range35963369
Interquartile range (IQR)5798679.8

Descriptive statistics

Standard deviation5995686.3
Coefficient of variation (CV)0.89942011
Kurtosis5.4571367
Mean6666168.8
Median Absolute Deviation (MAD)2379412.5
Skewness2.0496456
Sum7.4661091 × 108
Variance3.5948254 × 1013
MonotonicityNot monotonic
2023-12-11T01:45:33.125777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3420161 1
 
0.9%
7637246 1
 
0.9%
3159098 1
 
0.9%
6228944 1
 
0.9%
3717219 1
 
0.9%
2365371 1
 
0.9%
3988232 1
 
0.9%
798171 1
 
0.9%
1871834 1
 
0.9%
1219695 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
80290 1
0.9%
357629 1
0.9%
652961 1
0.9%
732794 1
0.9%
798171 1
0.9%
929769 1
0.9%
1131528 1
0.9%
1219695 1
0.9%
1283251 1
0.9%
1295633 1
0.9%
ValueCountFrequency (%)
36043659 1
0.9%
25499132 1
0.9%
22760794 1
0.9%
22460182 1
0.9%
21405568 1
0.9%
18685919 1
0.9%
18324001 1
0.9%
18171117 1
0.9%
16177650 1
0.9%
16147948 1
0.9%

Interactions

2023-12-11T01:45:28.441328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:23.999580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.498633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.031474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.586250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:26.378129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:27.543849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:28.581460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.073763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.573734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.119897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.669286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:26.498806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:27.674791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:28.730604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.148761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.646233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.203369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.827837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:26.938210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:27.809779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:28.880631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.221551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.722714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.281448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.927284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:27.051226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:27.961407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:28.999968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.284704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.785269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.349592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:26.041333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:27.156826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:28.093722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:29.105089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.347535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.869308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.422599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:26.145284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:27.281891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:28.195827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:29.231440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.417754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:24.948489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:25.500556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:26.261492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:27.420268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:28.305617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:45:33.216365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
호선1.0000.5910.5910.5180.5320.3360.3890.400
승차(명)0.5911.0001.0000.9870.9870.8450.9240.920
승차 일평균(명)0.5911.0001.0000.9870.9870.8450.9240.920
하차(명)0.5180.9870.9871.0001.0000.8750.9370.947
하차 일평균(명)0.5320.9870.9871.0001.0000.8750.9370.947
승차권(원)0.3360.8450.8450.8750.8751.0000.9550.964
카드승차권(원)0.3890.9240.9240.9370.9370.9551.0000.996
일평균(원)0.4000.9200.9200.9470.9470.9640.9961.000
2023-12-11T01:45:33.333003image/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)1.0001.0000.9920.9920.9310.9800.9800.299
승차 일평균(명)1.0001.0000.9920.9920.9310.9800.9800.299
하차(명)0.9920.9921.0001.0000.9330.9780.9770.255
하차 일평균(명)0.9920.9921.0001.0000.9330.9780.9770.255
승차권(원)0.9310.9310.9330.9331.0000.9560.9620.224
카드승차권(원)0.9800.9800.9780.9780.9561.0000.9990.259
일평균(원)0.9800.9800.9770.9770.9620.9991.0000.268
호선0.2990.2990.2550.2550.2240.2590.2681.000

Missing values

2023-12-11T01:45:29.383619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:45:29.571040image/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)
01다대포해수욕장역17960204921149334140918638313011619754673420161
11다대포항역1245755341314235793900765635309460106362801573
21낫개역179425649161789570490311044579013875043104103973
31신장림역1145773313910153752782586888408860354692588286
41장림역1199193328510629312912646155109485172042775706
51동매역13728923761118666732516593578010781771763134556
61신평역249254468292447700670614887451019625051815784602
71하단역743390220367679707518622364381600552961930016147948
81당리역253036469332564229702512340485017432802575114206
91사하역177690748681515406415210182285012922766693819451
호선역명승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
1024충렬사역1257461344512803183508409967605605073161647956
1034명장역1133096310410732872941528778206310854221873872
1044서동역84508623158797722410359519704369540011295633
1054금사역71040919466234861708212329504471535811283251
1064반여농산물시장역7152871960734894201313264680254204992732794
1074석대역570251564599712616118002769416980290
1084영산대역12634033461123692433891095552509295827842846954
1094동부산대학역1083523296910231172803641073106884342062061758
1104고촌역27074574220061955010381100120153657357629
1114안평역77302021187290521997321124703808952771131528