Overview

Dataset statistics

Number of variables10
Number of observations273
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.9 KiB
Average record size in memory89.5 B

Variable types

Numeric7
Text1
Categorical2

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-13208/F/1/datasetView.do

Alerts

연번 is highly overall correlated with 호선 and 5 other fieldsHigh correlation
호선 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
역번호 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
발매기 is highly overall correlated with 연번 and 7 other fieldsHigh correlation
환급기 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
무인정산기 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
휴대용정산기 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
발권기 is highly overall correlated with 발매기 and 1 other fieldsHigh correlation
유인충전기 is highly overall correlated with 발매기 and 2 other fieldsHigh correlation
발권기 is highly imbalanced (66.3%)Imbalance
유인충전기 is highly imbalanced (61.4%)Imbalance
연번 has unique valuesUnique
역번호 has unique valuesUnique
무인정산기 has 83 (30.4%) zerosZeros

Reproduction

Analysis started2024-04-29 16:43:27.349398
Analysis finished2024-04-29 16:43:32.398223
Duration5.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137
Minimum1
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:43:32.459582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.6
Q169
median137
Q3205
95-th percentile259.4
Maximum273
Range272
Interquartile range (IQR)136

Descriptive statistics

Standard deviation78.952517
Coefficient of variation (CV)0.57629575
Kurtosis-1.2
Mean137
Median Absolute Deviation (MAD)68
Skewness0
Sum37401
Variance6233.5
MonotonicityStrictly increasing
2024-04-30T01:43:32.587115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
181 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
184 1
 
0.4%
183 1
 
0.4%
182 1
 
0.4%
180 1
 
0.4%
206 1
 
0.4%
Other values (263) 263
96.3%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%
265 1
0.4%
264 1
0.4%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6043956
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:43:32.700905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.6
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0339713
Coefficient of variation (CV)0.44174557
Kurtosis-1.1324876
Mean4.6043956
Median Absolute Deviation (MAD)2
Skewness-0.091375877
Sum1257
Variance4.1370394
MonotonicityIncreasing
2024-04-30T01:43:33.034020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 55
20.1%
2 46
16.8%
7 43
15.8%
6 38
13.9%
3 33
12.1%
4 26
9.5%
8 18
 
6.6%
1 14
 
5.1%
ValueCountFrequency (%)
1 14
 
5.1%
2 46
16.8%
3 33
12.1%
4 26
9.5%
5 55
20.1%
6 38
13.9%
7 43
15.8%
8 18
 
6.6%
ValueCountFrequency (%)
8 18
 
6.6%
7 43
15.8%
6 38
13.9%
5 55
20.1%
4 26
9.5%
3 33
12.1%
2 46
16.8%
1 14
 
5.1%

역번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1619.326
Minimum150
Maximum2828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:43:33.140101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile204.6
Q1317
median2528
Q32640
95-th percentile2814.4
Maximum2828
Range2678
Interquartile range (IQR)2323

Descriptive statistics

Standard deviation1174.3885
Coefficient of variation (CV)0.72523289
Kurtosis-1.922557
Mean1619.326
Median Absolute Deviation (MAD)283
Skewness-0.25469603
Sum442076
Variance1379188.3
MonotonicityNot monotonic
2024-04-30T01:43:33.271402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1
 
0.4%
2617 1
 
0.4%
2623 1
 
0.4%
2622 1
 
0.4%
2621 1
 
0.4%
2620 1
 
0.4%
2619 1
 
0.4%
2618 1
 
0.4%
2616 1
 
0.4%
2642 1
 
0.4%
Other values (263) 263
96.3%
ValueCountFrequency (%)
150 1
0.4%
151 1
0.4%
152 1
0.4%
153 1
0.4%
154 1
0.4%
155 1
0.4%
156 1
0.4%
157 1
0.4%
158 1
0.4%
159 1
0.4%
ValueCountFrequency (%)
2828 1
0.4%
2827 1
0.4%
2826 1
0.4%
2825 1
0.4%
2824 1
0.4%
2823 1
0.4%
2822 1
0.4%
2821 1
0.4%
2820 1
0.4%
2819 1
0.4%

역사명
Text

Distinct255
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-30T01:43:33.534085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.1941392
Min length2

Characters and Unicode

Total characters872
Distinct characters217
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique238 ?
Unique (%)87.2%

Sample

1st row서울역(1)
2nd row시청(1)
3rd row종각
4th row종로3가(1)
5th row종로5가
ValueCountFrequency (%)
동대문역사문화공원 3
 
1.1%
연신내 2
 
0.7%
뚝섬 2
 
0.7%
건대입구 2
 
0.7%
신대방 2
 
0.7%
강남 2
 
0.7%
대림 2
 
0.7%
노원 2
 
0.7%
영등포구청 2
 
0.7%
삼각지 2
 
0.7%
Other values (249) 259
92.5%
2024-04-30T01:43:33.927434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 32
 
3.7%
구 26
 
3.0%
( 25
 
2.9%
) 25
 
2.9%
신 23
 
2.6%
동 22
 
2.5%
산 19
 
2.2%
지 15
 
1.7%
문 15
 
1.7%
원 14
 
1.6%
Other values (207) 656
75.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 780
89.4%
Decimal Number 34
 
3.9%
Open Punctuation 25
 
2.9%
Close Punctuation 25
 
2.9%
Control 6
 
0.7%
Space Separator 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 32
 
4.1%
구 26
 
3.3%
신 23
 
2.9%
동 22
 
2.8%
산 19
 
2.4%
지 15
 
1.9%
문 15
 
1.9%
원 14
 
1.8%
청 14
 
1.8%
가 12
 
1.5%
Other values (195) 588
75.4%
Decimal Number
ValueCountFrequency (%)
3 9
26.5%
1 5
14.7%
5 5
14.7%
2 5
14.7%
4 4
11.8%
6 3
 
8.8%
7 2
 
5.9%
8 1
 
2.9%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Control
ValueCountFrequency (%)
6
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 780
89.4%
Common 92
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 32
 
4.1%
구 26
 
3.3%
신 23
 
2.9%
동 22
 
2.8%
산 19
 
2.4%
지 15
 
1.9%
문 15
 
1.9%
원 14
 
1.8%
청 14
 
1.8%
가 12
 
1.5%
Other values (195) 588
75.4%
Common
ValueCountFrequency (%)
( 25
27.2%
) 25
27.2%
3 9
 
9.8%
6
 
6.5%
1 5
 
5.4%
5 5
 
5.4%
2 5
 
5.4%
4 4
 
4.3%
6 3
 
3.3%
2
 
2.2%
Other values (2) 3
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 780
89.4%
ASCII 92
 
10.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 32
 
4.1%
구 26
 
3.3%
신 23
 
2.9%
동 22
 
2.8%
산 19
 
2.4%
지 15
 
1.9%
문 15
 
1.9%
원 14
 
1.8%
청 14
 
1.8%
가 12
 
1.5%
Other values (195) 588
75.4%
ASCII
ValueCountFrequency (%)
( 25
27.2%
) 25
27.2%
3 9
 
9.8%
6
 
6.5%
1 5
 
5.4%
5 5
 
5.4%
2 5
 
5.4%
4 4
 
4.3%
6 3
 
3.3%
2
 
2.2%
Other values (2) 3
 
3.3%

발매기
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8205128
Minimum0
Maximum16
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:43:34.040887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median4
Q34
95-th percentile7.4
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2197353
Coefficient of variation (CV)0.58100454
Kurtosis7.7990162
Mean3.8205128
Median Absolute Deviation (MAD)2
Skewness2.2702288
Sum1043
Variance4.9272247
MonotonicityNot monotonic
2024-04-30T01:43:34.133979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 99
36.3%
4 88
32.2%
5 28
 
10.3%
3 19
 
7.0%
6 16
 
5.9%
7 7
 
2.6%
8 5
 
1.8%
14 2
 
0.7%
13 2
 
0.7%
10 2
 
0.7%
Other values (4) 5
 
1.8%
ValueCountFrequency (%)
0 2
 
0.7%
2 99
36.3%
3 19
 
7.0%
4 88
32.2%
5 28
 
10.3%
6 16
 
5.9%
7 7
 
2.6%
8 5
 
1.8%
9 1
 
0.4%
10 2
 
0.7%
ValueCountFrequency (%)
16 1
 
0.4%
14 2
 
0.7%
13 2
 
0.7%
12 1
 
0.4%
10 2
 
0.7%
9 1
 
0.4%
8 5
 
1.8%
7 7
 
2.6%
6 16
5.9%
5 28
10.3%

환급기
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4029304
Minimum0
Maximum10
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:43:34.242624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5335436
Coefficient of variation (CV)0.63819728
Kurtosis3.0086839
Mean2.4029304
Median Absolute Deviation (MAD)1
Skewness1.4262919
Sum656
Variance2.3517561
MonotonicityNot monotonic
2024-04-30T01:43:34.347087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 90
33.0%
2 87
31.9%
4 48
17.6%
3 25
 
9.2%
5 11
 
4.0%
6 6
 
2.2%
0 2
 
0.7%
7 1
 
0.4%
10 1
 
0.4%
9 1
 
0.4%
ValueCountFrequency (%)
0 2
 
0.7%
1 90
33.0%
2 87
31.9%
3 25
 
9.2%
4 48
17.6%
5 11
 
4.0%
6 6
 
2.2%
7 1
 
0.4%
8 1
 
0.4%
9 1
 
0.4%
ValueCountFrequency (%)
10 1
 
0.4%
9 1
 
0.4%
8 1
 
0.4%
7 1
 
0.4%
6 6
 
2.2%
5 11
 
4.0%
4 48
17.6%
3 25
 
9.2%
2 87
31.9%
1 90
33.0%

무인정산기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5457875
Minimum0
Maximum6
Zeros83
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:43:34.445730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4163925
Coefficient of variation (CV)0.91629186
Kurtosis-0.51234535
Mean1.5457875
Median Absolute Deviation (MAD)1
Skewness0.63754297
Sum422
Variance2.0061679
MonotonicityNot monotonic
2024-04-30T01:43:34.539439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 83
30.4%
1 65
23.8%
2 60
22.0%
4 31
 
11.4%
3 29
 
10.6%
5 4
 
1.5%
6 1
 
0.4%
ValueCountFrequency (%)
0 83
30.4%
1 65
23.8%
2 60
22.0%
3 29
 
10.6%
4 31
 
11.4%
5 4
 
1.5%
6 1
 
0.4%
ValueCountFrequency (%)
6 1
 
0.4%
5 4
 
1.5%
4 31
 
11.4%
3 29
 
10.6%
2 60
22.0%
1 65
23.8%
0 83
30.4%

발권기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
0
242 
1
30 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 242
88.6%
1 30
 
11.0%
2 1
 
0.4%

Length

2024-04-30T01:43:34.631453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:43:34.714398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 242
88.6%
1 30
 
11.0%
2 1
 
0.4%

휴대용정산기
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)2.2%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.1029412
Minimum0
Maximum5
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-04-30T01:43:34.792815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2166096
Coefficient of variation (CV)0.57852764
Kurtosis-1.064596
Mean2.1029412
Median Absolute Deviation (MAD)1
Skewness0.55701938
Sum572
Variance1.4801389
MonotonicityNot monotonic
2024-04-30T01:43:34.899863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 123
45.1%
3 49
 
17.9%
4 47
 
17.2%
2 47
 
17.2%
5 4
 
1.5%
0 2
 
0.7%
(Missing) 1
 
0.4%
ValueCountFrequency (%)
0 2
 
0.7%
1 123
45.1%
2 47
 
17.2%
3 49
 
17.9%
4 47
 
17.2%
5 4
 
1.5%
ValueCountFrequency (%)
5 4
 
1.5%
4 47
 
17.2%
3 49
 
17.9%
2 47
 
17.2%
1 123
45.1%
0 2
 
0.7%

유인충전기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
1
215 
2
51 
0
 
3
<NA>
 
3
3
 
1

Length

Max length4
Median length1
Mean length1.032967
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 215
78.8%
2 51
 
18.7%
0 3
 
1.1%
<NA> 3
 
1.1%
3 1
 
0.4%

Length

2024-04-30T01:43:35.005001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:43:35.124237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 215
78.8%
2 51
 
18.7%
0 3
 
1.1%
na 3
 
1.1%
3 1
 
0.4%

Interactions

2024-04-30T01:43:31.601964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.157032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.776089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.426841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.924058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.486396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.049072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.684135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.232985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.867631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.499439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.000495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.557537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.118535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.762931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.333508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.971645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.575955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.082074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.644805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.209838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.838254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.415623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.060655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.644361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.156536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.723626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.281631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.916566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.516878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.161300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.718427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.244714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.829233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.363257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.986014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.606516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.257610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.786296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.326022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.898299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.429370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:32.061202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:28.695563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.345299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:29.857457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.409586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:30.980303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:43:31.509570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:43:35.206357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호발매기환급기무인정산기발권기휴대용정산기유인충전기
연번1.0000.9220.9160.5470.6500.5720.0000.6190.103
호선0.9221.0000.9940.4400.5430.5890.0000.6310.159
역번호0.9160.9941.0000.4980.5780.6140.0000.6750.000
발매기0.5470.4400.4981.0000.9680.6770.8330.7450.847
환급기0.6500.5430.5780.9681.0000.7520.4240.7770.604
무인정산기0.5720.5890.6140.6770.7521.0000.1770.6100.300
발권기0.0000.0000.0000.8330.4240.1771.0000.2660.696
휴대용정산기0.6190.6310.6750.7450.7770.6100.2661.0000.745
유인충전기0.1030.1590.0000.8470.6040.3000.6960.7451.000
2024-04-30T01:43:35.318773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유인충전기발권기
유인충전기1.0000.730
발권기0.7301.000
2024-04-30T01:43:35.410296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선역번호발매기환급기무인정산기휴대용정산기발권기유인충전기
연번1.0000.9890.996-0.548-0.677-0.721-0.7260.0000.060
호선0.9891.0000.983-0.531-0.674-0.714-0.7260.0000.071
역번호0.9960.9831.000-0.562-0.688-0.735-0.7350.0000.000
발매기-0.548-0.531-0.5621.0000.8890.7920.7570.7380.687
환급기-0.677-0.674-0.6880.8891.0000.8630.8300.2950.224
무인정산기-0.721-0.714-0.7350.7920.8631.0000.8140.1190.209
휴대용정산기-0.726-0.726-0.7350.7570.8300.8141.0000.1130.580
발권기0.0000.0000.0000.7380.2950.1190.1131.0000.730
유인충전기0.0600.0710.0000.6870.2240.2090.5800.7301.000

Missing values

2024-04-30T01:43:32.189281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:43:32.346380image/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

연번호선역번호역사명발매기환급기무인정산기발권기휴대용정산기유인충전기
011150서울역(1)1475141
121151시청(1)844041
231152종각755041
341153종로3가(1)764132
451154종로5가543041
561155동대문(1)642041
671156신설동(1)864021
781157제기동552041
891158청량리653142
9101159동묘앞432041
연번호선역번호역사명발매기환급기무인정산기발권기휴대용정산기유인충전기
26326482819문정320011
26426582820장지430011
26526682821복정210011
26626782822산성210011
26726882823남한산성입구210011
26826982824단대오거리310011
26927082825신흥210011
27027182826수진210011
27127282827모란210011
27227382828남위례422011