Overview

Dataset statistics

Number of variables11
Number of observations287
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.6 KiB
Average record size in memory98.5 B

Variable types

Numeric7
Text1
Categorical3

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-13294/F/1/datasetView.do

Alerts

연번 is highly overall correlated with 호선 and 3 other fieldsHigh correlation
호선 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
턴스타일개집표기 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
스피드개집표기 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
플랩형개집표기 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
우대자용개집표기 is highly overall correlated with 플랩형개집표기High correlation
개방형개집표기 is highly imbalanced (95.8%)Imbalance
EV용개집표기 is highly imbalanced (60.3%)Imbalance
연번 has unique valuesUnique
턴스타일개집표기 has 180 (62.7%) zerosZeros
슬림개집표기 has 255 (88.9%) zerosZeros
스피드개집표기 has 169 (58.9%) zerosZeros
1~8호선 자동개집표기 has 282 (98.3%) zerosZeros
플랩형개집표기 has 127 (44.3%) zerosZeros

Reproduction

Analysis started2024-04-29 16:49:57.399068
Analysis finished2024-04-29 16:50:02.419579
Duration5.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct287
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144
Minimum1
Maximum287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-30T01:50:02.482510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.3
Q172.5
median144
Q3215.5
95-th percentile272.7
Maximum287
Range286
Interquartile range (IQR)143

Descriptive statistics

Standard deviation82.993976
Coefficient of variation (CV)0.57634705
Kurtosis-1.2
Mean144
Median Absolute Deviation (MAD)72
Skewness0
Sum41328
Variance6888
MonotonicityStrictly increasing
2024-04-30T01:50:02.606392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
2 1
 
0.3%
197 1
 
0.3%
196 1
 
0.3%
195 1
 
0.3%
194 1
 
0.3%
193 1
 
0.3%
192 1
 
0.3%
191 1
 
0.3%
190 1
 
0.3%
Other values (277) 277
96.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
287 1
0.3%
286 1
0.3%
285 1
0.3%
284 1
0.3%
283 1
0.3%
282 1
0.3%
281 1
0.3%
280 1
0.3%
279 1
0.3%
278 1
0.3%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7108014
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-30T01:50:02.705867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0113285
Coefficient of variation (CV)0.42696101
Kurtosis-1.1650028
Mean4.7108014
Median Absolute Deviation (MAD)2
Skewness-0.13858462
Sum1352
Variance4.0454424
MonotonicityIncreasing
2024-04-30T01:50:02.793337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 58
20.2%
7 53
18.5%
2 50
17.4%
6 39
13.6%
3 33
11.5%
4 26
9.1%
8 18
 
6.3%
1 10
 
3.5%
ValueCountFrequency (%)
1 10
 
3.5%
2 50
17.4%
3 33
11.5%
4 26
9.1%
5 58
20.2%
6 39
13.6%
7 53
18.5%
8 18
 
6.3%
ValueCountFrequency (%)
8 18
 
6.3%
7 53
18.5%
6 39
13.6%
5 58
20.2%
4 26
9.1%
3 33
11.5%
2 50
17.4%
1 10
 
3.5%

역사명
Text

Distinct271
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-30T01:50:03.055679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.2996516
Min length2

Characters and Unicode

Total characters947
Distinct characters221
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique256 ?
Unique (%)89.2%

Sample

1st row서울역(1)
2nd row시청(1)
3rd row종각
4th row종로3가(1)
5th row종로5가
ValueCountFrequency (%)
동대문역사문화공원 3
 
1.0%
대 3
 
1.0%
수 3
 
1.0%
영등포구청 2
 
0.7%
노원 2
 
0.7%
합정 2
 
0.7%
가락시장 2
 
0.7%
삼각지 2
 
0.7%
금 2
 
0.7%
충정로 2
 
0.7%
Other values (272) 281
92.4%
2024-04-30T01:50:03.453619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
 
3.4%
대 32
 
3.4%
구 27
 
2.9%
동 25
 
2.6%
신 25
 
2.6%
) 24
 
2.5%
( 24
 
2.5%
지 21
 
2.2%
산 20
 
2.1%
청 16
 
1.7%
Other values (211) 701
74.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 836
88.3%
Space Separator 32
 
3.4%
Decimal Number 31
 
3.3%
Close Punctuation 24
 
2.5%
Open Punctuation 24
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 32
 
3.8%
구 27
 
3.2%
동 25
 
3.0%
신 25
 
3.0%
지 21
 
2.5%
산 20
 
2.4%
청 16
 
1.9%
문 15
 
1.8%
원 14
 
1.7%
화 12
 
1.4%
Other values (200) 629
75.2%
Decimal Number
ValueCountFrequency (%)
3 7
22.6%
5 5
16.1%
1 5
16.1%
2 5
16.1%
4 3
9.7%
6 3
9.7%
7 2
 
6.5%
8 1
 
3.2%
Space Separator
ValueCountFrequency (%)
32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 836
88.3%
Common 111
 
11.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 32
 
3.8%
구 27
 
3.2%
동 25
 
3.0%
신 25
 
3.0%
지 21
 
2.5%
산 20
 
2.4%
청 16
 
1.9%
문 15
 
1.8%
원 14
 
1.7%
화 12
 
1.4%
Other values (200) 629
75.2%
Common
ValueCountFrequency (%)
32
28.8%
) 24
21.6%
( 24
21.6%
3 7
 
6.3%
5 5
 
4.5%
1 5
 
4.5%
2 5
 
4.5%
4 3
 
2.7%
6 3
 
2.7%
7 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 836
88.3%
ASCII 111
 
11.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32
28.8%
) 24
21.6%
( 24
21.6%
3 7
 
6.3%
5 5
 
4.5%
1 5
 
4.5%
2 5
 
4.5%
4 3
 
2.7%
6 3
 
2.7%
7 2
 
1.8%
Hangul
ValueCountFrequency (%)
대 32
 
3.8%
구 27
 
3.2%
동 25
 
3.0%
신 25
 
3.0%
지 21
 
2.5%
산 20
 
2.4%
청 16
 
1.9%
문 15
 
1.8%
원 14
 
1.7%
화 12
 
1.4%
Other values (200) 629
75.2%

턴스타일개집표기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5749129
Minimum0
Maximum51
Zeros180
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-30T01:50:03.600986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q318
95-th percentile34.7
Maximum51
Range51
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.77045
Coefficient of variation (CV)1.4892805
Kurtosis0.51837813
Mean8.5749129
Median Absolute Deviation (MAD)0
Skewness1.2667443
Sum2461
Variance163.0844
MonotonicityNot monotonic
2024-04-30T01:50:03.716447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 180
62.7%
19 8
 
2.8%
34 5
 
1.7%
23 5
 
1.7%
20 5
 
1.7%
18 5
 
1.7%
24 5
 
1.7%
10 4
 
1.4%
37 4
 
1.4%
17 4
 
1.4%
Other values (29) 62
 
21.6%
ValueCountFrequency (%)
0 180
62.7%
3 1
 
0.3%
5 2
 
0.7%
6 1
 
0.3%
7 2
 
0.7%
9 1
 
0.3%
10 4
 
1.4%
11 2
 
0.7%
12 3
 
1.0%
13 4
 
1.4%
ValueCountFrequency (%)
51 1
 
0.3%
49 1
 
0.3%
48 2
 
0.7%
44 1
 
0.3%
40 2
 
0.7%
38 1
 
0.3%
37 4
1.4%
36 1
 
0.3%
35 2
 
0.7%
34 5
1.7%

슬림개집표기
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2125436
Minimum0
Maximum72
Zeros255
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-30T01:50:03.819184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.3352052
Coefficient of variation (CV)3.7672502
Kurtosis31.356391
Mean2.2125436
Median Absolute Deviation (MAD)0
Skewness5.1460914
Sum635
Variance69.475646
MonotonicityNot monotonic
2024-04-30T01:50:03.925741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 255
88.9%
7 4
 
1.4%
4 3
 
1.0%
28 3
 
1.0%
11 3
 
1.0%
16 2
 
0.7%
24 1
 
0.3%
5 1
 
0.3%
8 1
 
0.3%
32 1
 
0.3%
Other values (13) 13
 
4.5%
ValueCountFrequency (%)
0 255
88.9%
3 1
 
0.3%
4 3
 
1.0%
5 1
 
0.3%
7 4
 
1.4%
8 1
 
0.3%
9 1
 
0.3%
10 1
 
0.3%
11 3
 
1.0%
14 1
 
0.3%
ValueCountFrequency (%)
72 1
 
0.3%
64 1
 
0.3%
40 1
 
0.3%
38 1
 
0.3%
34 1
 
0.3%
32 1
 
0.3%
30 1
 
0.3%
28 3
1.0%
25 1
 
0.3%
24 1
 
0.3%

스피드개집표기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1463415
Minimum0
Maximum6
Zeros169
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-30T01:50:04.017033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5665356
Coefficient of variation (CV)1.3665523
Kurtosis0.048736101
Mean1.1463415
Median Absolute Deviation (MAD)0
Skewness1.0798236
Sum329
Variance2.4540338
MonotonicityNot monotonic
2024-04-30T01:50:04.103227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 169
58.9%
2 46
 
16.0%
4 29
 
10.1%
3 25
 
8.7%
1 12
 
4.2%
6 4
 
1.4%
5 2
 
0.7%
ValueCountFrequency (%)
0 169
58.9%
1 12
 
4.2%
2 46
 
16.0%
3 25
 
8.7%
4 29
 
10.1%
5 2
 
0.7%
6 4
 
1.4%
ValueCountFrequency (%)
6 4
 
1.4%
5 2
 
0.7%
4 29
 
10.1%
3 25
 
8.7%
2 46
 
16.0%
1 12
 
4.2%
0 169
58.9%

1~8호선 자동개집표기
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3902439
Minimum0
Maximum40
Zeros282
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-30T01:50:04.198213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.3167791
Coefficient of variation (CV)8.4992464
Kurtosis100.14008
Mean0.3902439
Median Absolute Deviation (MAD)0
Skewness9.6847502
Sum112
Variance11.001023
MonotonicityNot monotonic
2024-04-30T01:50:04.289817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 282
98.3%
18 1
 
0.3%
40 1
 
0.3%
16 1
 
0.3%
31 1
 
0.3%
7 1
 
0.3%
ValueCountFrequency (%)
0 282
98.3%
7 1
 
0.3%
16 1
 
0.3%
18 1
 
0.3%
31 1
 
0.3%
40 1
 
0.3%
ValueCountFrequency (%)
40 1
 
0.3%
31 1
 
0.3%
18 1
 
0.3%
16 1
 
0.3%
7 1
 
0.3%
0 282
98.3%

플랩형개집표기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2508711
Minimum0
Maximum52
Zeros127
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-30T01:50:04.414873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q314
95-th percentile24.7
Maximum52
Range52
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.3359852
Coefficient of variation (CV)1.1315151
Kurtosis2.4929219
Mean8.2508711
Median Absolute Deviation (MAD)8
Skewness1.3105051
Sum2368
Variance87.16062
MonotonicityNot monotonic
2024-04-30T01:50:04.820252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 127
44.3%
8 17
 
5.9%
12 16
 
5.6%
16 13
 
4.5%
10 12
 
4.2%
14 11
 
3.8%
9 11
 
3.8%
11 9
 
3.1%
18 8
 
2.8%
19 8
 
2.8%
Other values (21) 55
19.2%
ValueCountFrequency (%)
0 127
44.3%
6 5
 
1.7%
7 7
 
2.4%
8 17
 
5.9%
9 11
 
3.8%
10 12
 
4.2%
11 9
 
3.1%
12 16
 
5.6%
13 7
 
2.4%
14 11
 
3.8%
ValueCountFrequency (%)
52 1
0.3%
49 1
0.3%
41 1
0.3%
35 1
0.3%
34 1
0.3%
32 1
0.3%
31 2
0.7%
30 2
0.7%
29 1
0.3%
28 2
0.7%

개방형개집표기
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
285 
5
 
1
19
 
1

Length

Max length2
Median length1
Mean length1.0034843
Min length1

Unique

Unique2 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 285
99.3%
5 1
 
0.3%
19 1
 
0.3%

Length

2024-04-30T01:50:04.923483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:50:05.013276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 285
99.3%
5 1
 
0.3%
19 1
 
0.3%

우대자용개집표기
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
176 
2
64 
4
39 
6
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 176
61.3%
2 64
 
22.3%
4 39
 
13.6%
6 8
 
2.8%

Length

2024-04-30T01:50:05.113348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:50:05.198464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 176
61.3%
2 64
 
22.3%
4 39
 
13.6%
6 8
 
2.8%

EV용개집표기
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0
254 
1
 
18
2
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 254
88.5%
1 18
 
6.3%
2 15
 
5.2%

Length

2024-04-30T01:50:05.288914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:50:05.370319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 254
88.5%
1 18
 
6.3%
2 15
 
5.2%

Interactions

2024-04-30T01:50:01.612863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:57.818427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.405703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.012419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.846967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.457904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.018651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.695681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:57.888449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.509061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.096372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.951485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.527907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.099093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.785945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:57.968760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.608555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.182223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.036247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.613531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.187636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.863765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.053892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.687359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.261294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.126108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.701260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.265598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.950163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.134189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.774353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.366219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.205434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.784766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.347513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:02.030317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.233406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.852594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.444623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.287106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.862887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.442204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:02.111665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.331854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:58.938863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:59.771374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.378875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:00.949139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:50:01.534073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:50:05.431185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선턴스타일개집표기슬림개집표기스피드개집표기1~8호선 자동개집표기플랩형개집표기개방형개집표기우대자용개집표기EV용개집표기
연번1.0000.9320.7170.2630.6570.0000.6220.0000.6540.344
호선0.9321.0000.6300.5090.6520.1050.5970.0000.7950.338
턴스타일개집표기0.7170.6301.0000.0000.7720.0000.4850.0000.4730.127
슬림개집표기0.2630.5090.0001.0000.4190.4420.0000.0000.0000.000
스피드개집표기0.6570.6520.7720.4191.0000.2500.5740.0000.4900.230
1~8호선 자동개집표기0.0000.1050.0000.4420.2501.0000.0000.0000.0000.000
플랩형개집표기0.6220.5970.4850.0000.5740.0001.0000.0000.6930.421
개방형개집표기0.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
우대자용개집표기0.6540.7950.4730.0000.4900.0000.6930.0001.0000.180
EV용개집표기0.3440.3380.1270.0000.2300.0000.4210.0000.1801.000
2024-04-30T01:50:05.563876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우대자용개집표기EV용개집표기개방형개집표기
우대자용개집표기1.0000.1700.000
EV용개집표기0.1701.0000.000
개방형개집표기0.0000.0001.000
2024-04-30T01:50:05.658418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선턴스타일개집표기슬림개집표기스피드개집표기1~8호선 자동개집표기플랩형개집표기개방형개집표기우대자용개집표기EV용개집표기
연번1.0000.987-0.773-0.402-0.836-0.1770.7450.0000.4500.217
호선0.9871.000-0.772-0.402-0.836-0.1730.7570.0000.4590.226
턴스타일개집표기-0.773-0.7721.0000.1680.915-0.003-0.7520.0000.2980.074
슬림개집표기-0.402-0.4020.1681.0000.4290.212-0.3570.0000.0000.000
스피드개집표기-0.836-0.8360.9150.4291.0000.058-0.8100.0000.3570.157
1~8호선 자동개집표기-0.177-0.173-0.0030.2120.0581.000-0.1350.0000.0000.000
플랩형개집표기0.7450.757-0.752-0.357-0.810-0.1351.0000.0000.5220.202
개방형개집표기0.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
우대자용개집표기0.4500.4590.2980.0000.3570.0000.5220.0001.0000.170
EV용개집표기0.2170.2260.0740.0000.1570.0000.2020.0000.1701.000

Missing values

2024-04-30T01:50:02.215636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:50:02.360142image/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

연번호선역사명턴스타일개집표기슬림개집표기스피드개집표기1~8호선 자동개집표기플랩형개집표기개방형개집표기우대자용개집표기EV용개집표기
011서울역(1)29163180000
121시청(1)064400000
231종각3711500000
341종로3가(1)340400000
451종로5가310300000
561동대문(1)250300000
671신설동(1)2311400000
781제기동200200000
891청량리360300000
9101동묘앞120200000
연번호선역사명턴스타일개집표기슬림개집표기스피드개집표기1~8호선 자동개집표기플랩형개집표기개방형개집표기우대자용개집표기EV용개집표기
2772788문정000017020
2782798장지000017020
2792808복정00008020
2802818산성000011020
2812828남한산성입구000013041
2822838단대오거리000013022
2832848신흥00006020
2842858수진00009020
2852868모란00006041
2862878모란기지00000000