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_20191231
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 16:46:13.493812
Analysis finished2023-12-10 16:46:19.846145
Duration6.35 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

2023-12-11T01:46:19.908355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:46:20.006810image/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
2023-12-11T01:46:20.281149image/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%
2023-12-11T01:46:20.722344image/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%
Mean3058471.7
Minimum61707
Maximum14952243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:20.862997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61707
5-th percentile688130.5
Q11355041.2
median2295228
Q33774603.5
95-th percentile7803442.3
Maximum14952243
Range14890536
Interquartile range (IQR)2419562.2

Descriptive statistics

Standard deviation2466027.6
Coefficient of variation (CV)0.80629408
Kurtosis4.3296235
Mean3058471.7
Median Absolute Deviation (MAD)1111540
Skewness1.7861549
Sum3.4254883 × 108
Variance6.0812922 × 1012
MonotonicityNot monotonic
2023-12-11T01:46:21.020190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1861114 1
 
0.9%
2659214 1
 
0.9%
1850964 1
 
0.9%
2520715 1
 
0.9%
1326366 1
 
0.9%
881904 1
 
0.9%
1724774 1
 
0.9%
427702 1
 
0.9%
983763 1
 
0.9%
643619 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
61707 1
0.9%
270721 1
0.9%
377872 1
0.9%
427702 1
0.9%
636173 1
0.9%
643619 1
0.9%
724549 1
0.9%
727762 1
0.9%
740375 1
0.9%
757071 1
0.9%
ValueCountFrequency (%)
14952243 1
0.9%
10042635 1
0.9%
8709470 1
0.9%
8444543 1
0.9%
8117510 1
0.9%
7845167 1
0.9%
7769304 1
0.9%
7609153 1
0.9%
7573481 1
0.9%
7028686 1
0.9%

승차 일평균(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8379.3929
Minimum169
Maximum40965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:21.174015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum169
5-th percentile1885.1
Q13712.5
median6288
Q310341.5
95-th percentile21379.6
Maximum40965
Range40796
Interquartile range (IQR)6629

Descriptive statistics

Standard deviation6756.3073
Coefficient of variation (CV)0.80630034
Kurtosis4.3293759
Mean8379.3929
Median Absolute Deviation (MAD)3046
Skewness1.786125
Sum938492
Variance45647688
MonotonicityNot monotonic
2023-12-11T01:46:21.359034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10171 2
 
1.8%
5099 1
 
0.9%
3632 1
 
0.9%
5071 1
 
0.9%
6906 1
 
0.9%
3634 1
 
0.9%
2416 1
 
0.9%
4725 1
 
0.9%
1172 1
 
0.9%
2695 1
 
0.9%
Other values (101) 101
90.2%
ValueCountFrequency (%)
169 1
0.9%
742 1
0.9%
1035 1
0.9%
1172 1
0.9%
1743 1
0.9%
1763 1
0.9%
1985 1
0.9%
1994 1
0.9%
2028 1
0.9%
2074 1
0.9%
ValueCountFrequency (%)
40965 1
0.9%
27514 1
0.9%
23862 1
0.9%
23136 1
0.9%
22240 1
0.9%
21494 1
0.9%
21286 1
0.9%
20847 1
0.9%
20749 1
0.9%
19257 1
0.9%

하차(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3047453.5
Minimum53028
Maximum16899148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:21.533423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53028
5-th percentile649124.55
Q11322007
median2234520
Q33629888.8
95-th percentile7978639.1
Maximum16899148
Range16846120
Interquartile range (IQR)2307881.8

Descriptive statistics

Standard deviation2652989.6
Coefficient of variation (CV)0.8705595
Kurtosis6.4451595
Mean3047453.5
Median Absolute Deviation (MAD)1079343
Skewness2.1228792
Sum3.4131479 × 108
Variance7.0383536 × 1012
MonotonicityNot monotonic
2023-12-11T01:46:21.713723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1526410 1
 
0.9%
2865926 1
 
0.9%
1751091 1
 
0.9%
2508169 1
 
0.9%
1276162 1
 
0.9%
811346 1
 
0.9%
1831891 1
 
0.9%
580610 1
 
0.9%
809419 1
 
0.9%
482663 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
53028 1
0.9%
200705 1
0.9%
360094 1
0.9%
482663 1
0.9%
580610 1
0.9%
640819 1
0.9%
655920 1
0.9%
716620 1
0.9%
723381 1
0.9%
762368 1
0.9%
ValueCountFrequency (%)
16899148 1
0.9%
11446725 1
0.9%
9124084 1
0.9%
8739730 1
0.9%
8709870 1
0.9%
8086696 1
0.9%
7890229 1
0.9%
7738846 1
0.9%
7504705 1
0.9%
7332989 1
0.9%

하차 일평균(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8349.1875
Minimum145
Maximum46299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:21.966658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum145
5-th percentile1778.55
Q13621.75
median6122
Q39944.75
95-th percentile21859.1
Maximum46299
Range46154
Interquartile range (IQR)6323

Descriptive statistics

Standard deviation7268.4405
Coefficient of variation (CV)0.87055662
Kurtosis6.445216
Mean8349.1875
Median Absolute Deviation (MAD)2957
Skewness2.12288
Sum935109
Variance52830227
MonotonicityNot monotonic
2023-12-11T01:46:22.138928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4182 1
 
0.9%
7852 1
 
0.9%
4798 1
 
0.9%
6872 1
 
0.9%
3496 1
 
0.9%
2223 1
 
0.9%
5019 1
 
0.9%
1591 1
 
0.9%
2218 1
 
0.9%
1322 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
145 1
0.9%
550 1
0.9%
987 1
0.9%
1322 1
0.9%
1591 1
0.9%
1756 1
0.9%
1797 1
0.9%
1963 1
0.9%
1982 1
0.9%
2089 1
0.9%
ValueCountFrequency (%)
46299 1
0.9%
31361 1
0.9%
24997 1
0.9%
23944 1
0.9%
23863 1
0.9%
22155 1
0.9%
21617 1
0.9%
21202 1
0.9%
20561 1
0.9%
20090 1
0.9%

승차권(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5635605 × 108
Minimum1677550
Maximum7.7869034 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:22.291793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1677550
5-th percentile20294093
Q165019832
median1.1037386 × 108
Q32.0011941 × 108
95-th percentile4.2151743 × 108
Maximum7.7869034 × 108
Range7.7701279 × 108
Interquartile range (IQR)1.3509958 × 108

Descriptive statistics

Standard deviation1.4586058 × 108
Coefficient of variation (CV)0.93287452
Kurtosis5.1863984
Mean1.5635605 × 108
Median Absolute Deviation (MAD)54807940
Skewness2.0822502
Sum1.7511878 × 1010
Variance2.1275308 × 1016
MonotonicityNot monotonic
2023-12-11T01:46:22.466863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77818610 1
 
0.9%
320083050 1
 
0.9%
75711450 1
 
0.9%
182412790 1
 
0.9%
124270790 1
 
0.9%
63978460 1
 
0.9%
111822200 1
 
0.9%
16165900 1
 
0.9%
48871270 1
 
0.9%
32569300 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
1677550 1
0.9%
7403850 1
0.9%
10254920 1
0.9%
13353600 1
0.9%
16165900 1
0.9%
19025100 1
0.9%
21332360 1
0.9%
22395450 1
0.9%
23473260 1
0.9%
24775600 1
0.9%
ValueCountFrequency (%)
778690340 1
0.9%
738531210 1
0.9%
672180930 1
0.9%
499804520 1
0.9%
478273410 1
0.9%
448580420 1
0.9%
399374980 1
0.9%
389108800 1
0.9%
388811650 1
0.9%
364311880 1
0.9%

카드승차권(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2527462 × 109
Minimum26327260
Maximum1.2438965 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:22.629707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26327260
5-th percentile3.2656262 × 108
Q19.5241518 × 108
median1.6183913 × 109
Q32.9849552 × 109
95-th percentile6.2085732 × 109
Maximum1.2438965 × 1010
Range1.2412638 × 1010
Interquartile range (IQR)2.03254 × 109

Descriptive statistics

Standard deviation2.0389917 × 109
Coefficient of variation (CV)0.90511383
Kurtosis5.8281501
Mean2.2527462 × 109
Median Absolute Deviation (MAD)8.3219568 × 108
Skewness2.0856169
Sum2.5230757 × 1011
Variance4.1574872 × 1018
MonotonicityNot monotonic
2023-12-11T01:46:22.794656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1177128062 1
 
0.9%
2689555254 1
 
0.9%
1068106629 1
 
0.9%
2039008172 1
 
0.9%
1222968722 1
 
0.9%
775508941 1
 
0.9%
1515777657 1
 
0.9%
268943268 1
 
0.9%
607564798 1
 
0.9%
391103090 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
26327260 1
0.9%
113040904 1
0.9%
205317152 1
0.9%
246910321 1
0.9%
268943268 1
0.9%
287547793 1
0.9%
358483836 1
0.9%
391103090 1
0.9%
414845469 1
0.9%
440832075 1
0.9%
ValueCountFrequency (%)
12438964902 1
0.9%
8814558282 1
0.9%
7690746514 1
0.9%
7299848677 1
0.9%
7210499171 1
0.9%
6285482499 1
0.9%
6145647399 1
0.9%
5891290131 1
0.9%
5583353618 1
0.9%
5533385848 1
0.9%

일평균(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6600280
Minimum76726
Maximum36212754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:46:22.954906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76726
5-th percentile956363.05
Q12817403.5
median4695741
Q38740497.2
95-th percentile17951795
Maximum36212754
Range36136028
Interquartile range (IQR)5923093.8

Descriptive statistics

Standard deviation5960960.6
Coefficient of variation (CV)0.90313754
Kurtosis5.7146589
Mean6600280
Median Absolute Deviation (MAD)2399169.5
Skewness2.0715757
Sum7.3923136 × 108
Variance3.5533052 × 1013
MonotonicityNot monotonic
2023-12-11T01:46:23.158243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3438210 1
 
0.9%
8245584 1
 
0.9%
3133748 1
 
0.9%
6086085 1
 
0.9%
3691067 1
 
0.9%
2299965 1
 
0.9%
4459178 1
 
0.9%
781121 1
 
0.9%
1798455 1
 
0.9%
1160746 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
76726 1
0.9%
329986 1
0.9%
599098 1
0.9%
704562 1
0.9%
781121 1
0.9%
846247 1
0.9%
1046458 1
0.9%
1160746 1
0.9%
1220050 1
0.9%
1259883 1
0.9%
ValueCountFrequency (%)
36212754 1
0.9%
25518802 1
0.9%
22912130 1
0.9%
21778165 1
0.9%
20997700 1
0.9%
18102339 1
0.9%
17828622 1
0.9%
17450859 1
0.9%
16225199 1
0.9%
16152841 1
0.9%

Interactions

2023-12-11T01:46:18.473412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:13.973247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:14.934342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:15.830553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.564423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.174729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.818094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:18.583315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:14.118933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:15.057103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:15.975278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.649961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.276225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.900472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:18.708891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:14.263834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:15.190356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.075529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.740455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.367615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.992642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:19.146413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:14.423199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:15.327908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.176463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.831383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.450765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:18.097297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:19.254292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:14.545281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:15.444781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.256919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.903760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.544545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:18.180796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:19.362864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:14.676002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:15.548504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.360699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.992813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.640520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:18.282017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:19.478984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:14.811150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:15.671086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:16.464830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.079718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:17.728229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:18.371611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:46:23.289003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
호선1.0000.5710.5710.5280.5280.3950.4140.378
승차(명)0.5711.0001.0000.9890.9890.8090.9160.918
승차 일평균(명)0.5711.0001.0000.9890.9890.8090.9160.918
하차(명)0.5280.9890.9891.0001.0000.8460.9390.947
하차 일평균(명)0.5280.9890.9891.0001.0000.8460.9390.947
승차권(원)0.3950.8090.8090.8460.8461.0000.9550.943
카드승차권(원)0.4140.9160.9160.9390.9390.9551.0000.998
일평균(원)0.3780.9180.9180.9470.9470.9430.9981.000
2023-12-11T01:46:23.427828image/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)1.0001.0000.9900.9900.9270.9800.9800.287
승차 일평균(명)1.0001.0000.9900.9900.9270.9800.9800.287
하차(명)0.9900.9901.0001.0000.9300.9760.9760.260
하차 일평균(명)0.9900.9901.0001.0000.9300.9760.9760.260
승차권(원)0.9270.9270.9300.9301.0000.9540.9590.265
카드승차권(원)0.9800.9800.9760.9760.9541.0001.0000.277
일평균(원)0.9800.9800.9760.9760.9591.0001.0000.253
호선0.2870.2870.2600.2600.2650.2770.2531.000

Missing values

2023-12-11T01:46:19.638007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:46:19.789591image/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)
01다대포해수욕장역18611145099152641041827781861011771280623438210
11다대포항역1291768353914840444066686613209376524182757024
21낫개역187010051241882288515711100002013785700314081014
31신장림역1252820343211202033069564194108931084952601446
41장림역1308695358511733213215650834809953669942905344
51동매역14697904027127341934896440709010941060293174009
61신평역255173669912521023690713083827019250849285632666
71하단역776930421286712883119531312433430558335361816152841
81당리역256968970402685201735710649694016689902854864349
91사하역17524084801151071941398958478012124735553567283
호선역명승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
1024충렬사역1253914343512899253534311414205123181811488930
1034명장역1132453310310872642979423187705882649821727627
1044서동역85750823499047692479304729004148454691220050
1054금사역72454919856408191756190251004408320751259883
1064반여농산물시장역7277621994762368208910254920246910321704562
1074석대역617071695302814516775502632726076726
1084영산대역1254896343812267963361954757708830175912680804
1094동부산대학역1091017298910240052805604185506301129751891867
1104고촌역2707217422007055507403850113040904329986
1114안평역75707120747233811982234732603584838361046458