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

Description부산 도시철도 연도별 역사 수송 및 수입 현황 (역명, 승차인원, 하차인원, 일평균승차인원, 일평균하차인원, 승차권, 카드승차권 수익(원) 등 )
URLhttps://www.data.go.kr/data/3033569/fileData.do

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 started2023-12-12 11:23:53.890867
Analysis finished2023-12-12 11:24:02.473379
Duration8.58 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

2023-12-12T20:24:02.585727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:24:02.758020image/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
2023-12-12T20:24:03.231916image/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%
2023-12-12T20:24:04.005813image/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%
Mean2541457.3
Minimum65975
Maximum11112687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:24:04.294253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65975
5-th percentile596004.15
Q11202656
median1993928.5
Q33296330.5
95-th percentile6098685.6
Maximum11112687
Range11046712
Interquartile range (IQR)2093674.5

Descriptive statistics

Standard deviation1895588.4
Coefficient of variation (CV)0.74586671
Kurtosis3.152472
Mean2541457.3
Median Absolute Deviation (MAD)946097
Skewness1.5496152
Sum2.8464321 × 108
Variance3.5932553 × 1012
MonotonicityNot monotonic
2023-12-12T20:24:04.558589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1741337 1
 
0.9%
2878111 1
 
0.9%
1591615 1
 
0.9%
2031283 1
 
0.9%
1130518 1
 
0.9%
661744 1
 
0.9%
1466138 1
 
0.9%
407951 1
 
0.9%
843802 1
 
0.9%
537910 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
65975 1
0.9%
214429 1
0.9%
281809 1
0.9%
407951 1
0.9%
537910 1
0.9%
584722 1
0.9%
605235 1
0.9%
661744 1
0.9%
692428 1
0.9%
698035 1
0.9%
ValueCountFrequency (%)
11112687 1
0.9%
7795676 1
0.9%
7003430 1
0.9%
6891205 1
0.9%
6350700 1
0.9%
6274899 1
0.9%
5954511 1
0.9%
5744252 1
0.9%
5737934 1
0.9%
5585010 1
0.9%

승차 일평균(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6962.875
Minimum181
Maximum30446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:24:04.813195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum181
5-th percentile1632.8
Q13294.75
median5462.5
Q39031.25
95-th percentile16709.1
Maximum30446
Range30265
Interquartile range (IQR)5736.5

Descriptive statistics

Standard deviation5193.4055
Coefficient of variation (CV)0.74587085
Kurtosis3.1526031
Mean6962.875
Median Absolute Deviation (MAD)2592
Skewness1.5496296
Sum779842
Variance26971461
MonotonicityNot monotonic
2023-12-12T20:24:05.002564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4771 1
 
0.9%
7885 1
 
0.9%
4361 1
 
0.9%
5565 1
 
0.9%
3097 1
 
0.9%
1813 1
 
0.9%
4017 1
 
0.9%
1118 1
 
0.9%
2312 1
 
0.9%
1474 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
181 1
0.9%
587 1
0.9%
772 1
0.9%
1118 1
0.9%
1474 1
0.9%
1602 1
0.9%
1658 1
0.9%
1813 1
0.9%
1897 1
0.9%
1912 1
0.9%
ValueCountFrequency (%)
30446 1
0.9%
21358 1
0.9%
19187 1
0.9%
18880 1
0.9%
17399 1
0.9%
17192 1
0.9%
16314 1
0.9%
15738 1
0.9%
15720 1
0.9%
15301 1
0.9%

하차(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2533661.7
Minimum51616
Maximum12677349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:24:05.232342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51616
5-th percentile573756.45
Q11171237.8
median1960356
Q33279557.8
95-th percentile6206029.2
Maximum12677349
Range12625733
Interquartile range (IQR)2108320

Descriptive statistics

Standard deviation2014539.8
Coefficient of variation (CV)0.79511002
Kurtosis5.1842391
Mean2533661.7
Median Absolute Deviation (MAD)959307.5
Skewness1.8659486
Sum2.8377011 × 108
Variance4.0583705 × 1012
MonotonicityNot monotonic
2023-12-12T20:24:05.432151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440312 1
 
0.9%
3334691 1
 
0.9%
1523236 1
 
0.9%
2012429 1
 
0.9%
1104049 1
 
0.9%
602767 1
 
0.9%
1571832 1
 
0.9%
517219 1
 
0.9%
683620 1
 
0.9%
445850 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
51616 1
0.9%
172178 1
0.9%
265178 1
0.9%
445850 1
0.9%
517219 1
0.9%
538667 1
0.9%
602466 1
0.9%
602767 1
0.9%
670357 1
0.9%
672291 1
0.9%
ValueCountFrequency (%)
12677349 1
0.9%
8283072 1
0.9%
7197936 1
0.9%
7069864 1
0.9%
6227762 1
0.9%
6207055 1
0.9%
6205190 1
0.9%
6141151 1
0.9%
5888105 1
0.9%
5797770 1
0.9%

하차 일평균(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6941.5714
Minimum141
Maximum34732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:24:05.645117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile1572.25
Q13209
median5371
Q38985.25
95-th percentile17003.25
Maximum34732
Range34591
Interquartile range (IQR)5776.25

Descriptive statistics

Standard deviation5519.2306
Coefficient of variation (CV)0.79509814
Kurtosis5.1839977
Mean6941.5714
Median Absolute Deviation (MAD)2628.5
Skewness1.8659068
Sum777456
Variance30461906
MonotonicityNot monotonic
2023-12-12T20:24:05.855938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1651 2
 
1.8%
8517 2
 
1.8%
3946 1
 
0.9%
3789 1
 
0.9%
3025 1
 
0.9%
4306 1
 
0.9%
1417 1
 
0.9%
1873 1
 
0.9%
1222 1
 
0.9%
3912 1
 
0.9%
Other values (100) 100
89.3%
ValueCountFrequency (%)
141 1
0.9%
472 1
0.9%
727 1
0.9%
1222 1
0.9%
1417 1
0.9%
1476 1
0.9%
1651 2
1.8%
1837 1
0.9%
1842 1
0.9%
1873 1
0.9%
ValueCountFrequency (%)
34732 1
0.9%
22693 1
0.9%
19720 1
0.9%
19369 1
0.9%
17062 1
0.9%
17006 1
0.9%
17001 1
0.9%
16825 1
0.9%
16132 1
0.9%
15884 1
0.9%

승차권(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75577818
Minimum628900
Maximum2.9216329 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:24:06.126935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum628900
5-th percentile10130700
Q134906802
median58681905
Q31.0358756 × 108
95-th percentile1.8480908 × 108
Maximum2.9216329 × 108
Range2.9153439 × 108
Interquartile range (IQR)68680752

Descriptive statistics

Standard deviation59776776
Coefficient of variation (CV)0.79093017
Kurtosis2.5220467
Mean75577818
Median Absolute Deviation (MAD)28853100
Skewness1.4971933
Sum8.4647156 × 109
Variance3.573263 × 1015
MonotonicityNot monotonic
2023-12-12T20:24:06.394873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000544 1
 
0.9%
193922470 1
 
0.9%
37683369 1
 
0.9%
105331140 1
 
0.9%
67629040 1
 
0.9%
33394600 1
 
0.9%
61765080 1
 
0.9%
10324040 1
 
0.9%
29270900 1
 
0.9%
19261600 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
628900 1
0.9%
3381400 1
0.9%
4341950 1
0.9%
5579150 1
0.9%
8211940 1
0.9%
9896950 1
0.9%
10321950 1
0.9%
10324040 1
0.9%
12062499 1
0.9%
13631500 1
0.9%
ValueCountFrequency (%)
292163290 1
0.9%
278995480 1
0.9%
270784640 1
0.9%
247549630 1
0.9%
195532961 1
0.9%
193922470 1
0.9%
177352670 1
0.9%
167713460 1
0.9%
165752120 1
0.9%
164035650 1
0.9%

카드승차권(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8641055 × 109
Minimum26436764
Maximum9.0938176 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:24:06.736911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26436764
5-th percentile2.8498202 × 108
Q17.9638433 × 108
median1.4235259 × 109
Q32.5813584 × 109
95-th percentile4.7136449 × 109
Maximum9.0938176 × 109
Range9.0673808 × 109
Interquartile range (IQR)1.784974 × 109

Descriptive statistics

Standard deviation1.5800261 × 109
Coefficient of variation (CV)0.84760549
Kurtosis4.2521392
Mean1.8641055 × 109
Median Absolute Deviation (MAD)7.2183866 × 108
Skewness1.8178179
Sum2.0877982 × 1011
Variance2.4964824 × 1018
MonotonicityNot monotonic
2023-12-12T20:24:06.987336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1018399261 1
 
0.9%
3126885924 1
 
0.9%
904834468 1
 
0.9%
1591573686 1
 
0.9%
1023593867 1
 
0.9%
565985431 1
 
0.9%
1235698471 1
 
0.9%
219895329 1
 
0.9%
484225712 1
 
0.9%
298504185 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
26436764 1
0.9%
84843092 1
0.9%
163066279 1
0.9%
210512164 1
0.9%
219895329 1
0.9%
271890010 1
0.9%
295693667 1
0.9%
298504185 1
0.9%
335616835 1
0.9%
346364764 1
0.9%
ValueCountFrequency (%)
9093817577 1
0.9%
6696136324 1
0.9%
6586116158 1
0.9%
6091199283 1
0.9%
5769308456 1
0.9%
4795300562 1
0.9%
4646835766 1
0.9%
4512255340 1
0.9%
4453266999 1
0.9%
4034935619 1
0.9%

일평균(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5314200.9
Minimum74153
Maximum25715016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:24:07.268375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74153
5-th percentile806450.8
Q12285350.2
median4030443.5
Q37418019.2
95-th percentile13329059
Maximum25715016
Range25640863
Interquartile range (IQR)5132669

Descriptive statistics

Standard deviation4474197.4
Coefficient of variation (CV)0.84193229
Kurtosis4.1710629
Mean5314200.9
Median Absolute Deviation (MAD)1965985
Skewness1.8011195
Sum5.9519051 × 108
Variance2.0018442 × 1013
MonotonicityNot monotonic
2023-12-12T20:24:08.005406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2899725 1
 
0.9%
9098105 1
 
0.9%
2582241 1
 
0.9%
4649054 1
 
0.9%
2989652 1
 
0.9%
1642137 1
 
0.9%
3554695 1
 
0.9%
630738 1
 
0.9%
1406840 1
 
0.9%
870591 1
 
0.9%
Other values (102) 102
91.1%
ValueCountFrequency (%)
74153 1
0.9%
241711 1
0.9%
462042 1
0.9%
588641 1
0.9%
630738 1
0.9%
767403 1
0.9%
838399 1
0.9%
870591 1
0.9%
946613 1
0.9%
993906 1
0.9%
ValueCountFrequency (%)
25715016 1
0.9%
18794992 1
0.9%
18786030 1
0.9%
17120785 1
0.9%
16570696 1
0.9%
13591925 1
0.9%
13113986 1
0.9%
12898050 1
0.9%
12592124 1
0.9%
11540240 1
0.9%

Interactions

2023-12-12T20:24:01.016257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:54.350279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:55.263210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:56.383683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:57.362424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:58.394078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:59.979067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:01.177911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:54.464538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:55.453712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:56.534629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:57.506152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:58.995962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:00.132344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:01.381593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:54.598678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:55.626806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:56.687351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:57.650697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:59.141365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:00.273716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:01.531294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:54.732701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:55.777851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:56.821479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:57.782634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:59.320825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:00.426266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:01.698374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:54.862962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:55.937423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:56.971829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:57.932479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:59.502942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:00.581915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:01.849488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:54.983686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:56.079172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:57.099250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:58.088823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:59.688057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:00.728829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:01.973480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:55.103436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:56.219393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:57.222616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:58.231025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:59.839854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:24:00.861456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:24:08.172855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
호선1.0000.6750.6750.5580.5580.5700.4410.485
승차(명)0.6751.0001.0000.9780.9780.7830.8890.901
승차 일평균(명)0.6751.0001.0000.9780.9780.7830.8890.901
하차(명)0.5580.9780.9781.0001.0000.8020.8910.889
하차 일평균(명)0.5580.9780.9781.0001.0000.8020.8910.889
승차권(원)0.5700.7830.7830.8020.8021.0000.8830.890
카드승차권(원)0.4410.8890.8890.8910.8910.8831.0000.998
일평균(원)0.4850.9010.9010.8890.8890.8900.9981.000
2023-12-12T20:24:08.379399image/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)1.0001.0000.9910.9910.8800.9760.9750.358
승차 일평균(명)1.0001.0000.9910.9910.8800.9760.9750.358
하차(명)0.9910.9911.0001.0000.8810.9740.9730.279
하차 일평균(명)0.9910.9911.0001.0000.8810.9740.9730.279
승차권(원)0.8800.8800.8810.8811.0000.9050.9140.403
카드승차권(원)0.9760.9760.9740.9740.9051.0000.9990.298
일평균(원)0.9750.9750.9730.9730.9140.9991.0000.331
호선0.3580.3580.2790.2790.4030.2980.3311.000

Missing values

2023-12-12T20:24:02.181589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:24:02.385595image/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)
01다대포해수욕장역17413374771144031239464000054410183992612899725
11다대포항역1170576320713286913640382685007707630722216525
21낫개역16324854473166261145556093693011000378713180753
31신장림역1112879304910185142790350465007585163042174145
41장림역1202299329411142233053318777508698231392470413
51동매역14624784007129663835523393150010129583572868191
61신평역21827765980223587461266807065016081857944592483
71하단역635070017399579777015884142858390445326699912592124
81당리역20971275746224415961485388286013103905383737735
91사하역1441112394812603493453444617609791770762804490
호선역명승차(명)승차 일평균(명)하차(명)하차 일평균(명)승차권(원)카드승차권(원)일평균(원)
1024충렬사역1032272282810773782952136315004059675381149586
1034명장역102543128099835832695211966005030248181436223
1044서동역7631612091795861218016410870346364764993906
1054금사역605235165853866714769896950335616835946613
1064반여농산물시장역692428189771911119704341950210512164588641
1074석대역65975181516161416289002643676474153
1084영산대역94676425949225852528464097006321888501859174
1094동부산대학역85274523367963592182279617004438065861292516
1104고촌역214429587172178472338140084843092241711
1114안평역6980351912672291184210321950295693667838399