Overview

Dataset statistics

Number of variables16
Number of observations119
Missing cells95
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 KiB
Average record size in memory143.1 B

Variable types

Categorical9
Numeric6
Text1

Dataset

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

Alerts

슬림게이트 1형 is highly overall correlated with 슬림게이트 2형 and 2 other fieldsHigh correlation
슬림게이트 2형 is highly overall correlated with 역번호 and 10 other fieldsHigh correlation
장애인게이트 설치 is highly overall correlated with 슬림게이트 3형 and 4 other fieldsHigh correlation
슬림게이트 5형 is highly overall correlated with 슬림게이트 1형 and 2 other fieldsHigh correlation
개집표기(턴스타일) EX is highly overall correlated with 슬림게이트 2형High correlation
호선 is highly overall correlated with 역번호 and 1 other fieldsHigh correlation
역번호 is highly overall correlated with 호선 and 1 other fieldsHigh correlation
개집표기(턴스타일) EN is highly overall correlated with 개집표기(턴스타일) REV and 3 other fieldsHigh correlation
개집표기(턴스타일) REV is highly overall correlated with 개집표기(턴스타일) EN and 3 other fieldsHigh correlation
개집표기(턴스타일) FL is highly overall correlated with 개집표기(턴스타일) EN and 3 other fieldsHigh correlation
슬림게이트 3형 is highly overall correlated with 슬림게이트 2형 and 1 other fieldsHigh correlation
스피드게이트 설치 is highly overall correlated with 개집표기(턴스타일) EN and 4 other fieldsHigh correlation
개집표기(턴스타일) EX is highly imbalanced (77.9%)Imbalance
개집표기(턴스타일) 철거 is highly imbalanced (93.0%)Imbalance
슬림게이트 1형 is highly imbalanced (54.4%)Imbalance
슬림게이트 2형 is highly imbalanced (91.2%)Imbalance
슬림게이트 5형 is highly imbalanced (54.8%)Imbalance
장애인게이트 설치 is highly imbalanced (93.0%)Imbalance
장애인게이트 철거 is highly imbalanced (93.0%)Imbalance
스피드게이트 철거 is highly imbalanced (93.0%)Imbalance
슬림게이트 3형 has 92 (77.3%) missing valuesMissing
역번호 has unique valuesUnique
개집표기(턴스타일) EN has 7 (5.9%) zerosZeros
개집표기(턴스타일) REV has 7 (5.9%) zerosZeros
개집표기(턴스타일) FL has 7 (5.9%) zerosZeros

Reproduction

Analysis started2024-04-29 16:49:47.181029
Analysis finished2024-04-29 16:49:51.740892
Duration4.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

호선
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2호선
50 
3호선
33 
4호선
26 
1호선
10 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1호선
2nd row1호선
3rd row1호선
4th row1호선
5th row1호선

Common Values

ValueCountFrequency (%)
2호선 50
42.0%
3호선 33
27.7%
4호선 26
21.8%
1호선 10
 
8.4%

Length

2024-04-30T01:49:51.799119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:51.882143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2호선 50
42.0%
3호선 33
27.7%
4호선 26
21.8%
1호선 10
 
8.4%

역번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.12605
Minimum150
Maximum434
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T01:49:51.986337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile155.9
Q1220.5
median250
Q3338.5
95-th percentile428.1
Maximum434
Range284
Interquartile range (IQR)118

Descriptive statistics

Standard deviation87.25227
Coefficient of variation (CV)0.30073918
Kurtosis-1.1541503
Mean290.12605
Median Absolute Deviation (MAD)68
Skewness0.27063256
Sum34525
Variance7612.9586
MonotonicityStrictly increasing
2024-04-30T01:49:52.110876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 1
 
0.8%
151 1
 
0.8%
338 1
 
0.8%
337 1
 
0.8%
336 1
 
0.8%
335 1
 
0.8%
334 1
 
0.8%
333 1
 
0.8%
332 1
 
0.8%
331 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
150 1
0.8%
151 1
0.8%
152 1
0.8%
153 1
0.8%
154 1
0.8%
155 1
0.8%
156 1
0.8%
157 1
0.8%
158 1
0.8%
159 1
0.8%
ValueCountFrequency (%)
434 1
0.8%
433 1
0.8%
432 1
0.8%
431 1
0.8%
430 1
0.8%
429 1
0.8%
428 1
0.8%
427 1
0.8%
426 1
0.8%
425 1
0.8%

역 명
Text

Distinct118
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-30T01:49:52.402243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length3.4789916
Min length2

Characters and Unicode

Total characters414
Distinct characters146
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

Unique117 ?
Unique (%)98.3%

Sample

1st row서울역(1)
2nd row시청(1)
3rd row종각
4th row종로3가(1)
5th row종로5가
ValueCountFrequency (%)
대 3
 
2.2%
수 3
 
2.2%
동대문역사문화공원 2
 
1.5%
금 2
 
1.5%
원 2
 
1.5%
곡 1
 
0.7%
도 1
 
0.7%
봉 1
 
0.7%
잠 1
 
0.7%
재 1
 
0.7%
Other values (119) 119
87.5%
2024-04-30T01:49:52.856769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
 
7.7%
대 21
 
5.1%
신 15
 
3.6%
동 13
 
3.1%
( 13
 
3.1%
구 13
 
3.1%
) 13
 
3.1%
문 9
 
2.2%
지 8
 
1.9%
입 7
 
1.7%
Other values (136) 270
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 337
81.4%
Space Separator 32
 
7.7%
Decimal Number 19
 
4.6%
Open Punctuation 13
 
3.1%
Close Punctuation 13
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 21
 
6.2%
신 15
 
4.5%
동 13
 
3.9%
구 13
 
3.9%
문 9
 
2.7%
지 8
 
2.4%
입 7
 
2.1%
가 7
 
2.1%
로 6
 
1.8%
원 6
 
1.8%
Other values (128) 232
68.8%
Decimal Number
ValueCountFrequency (%)
3 6
31.6%
2 5
26.3%
1 5
26.3%
4 2
 
10.5%
5 1
 
5.3%
Space Separator
ValueCountFrequency (%)
32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 337
81.4%
Common 77
 
18.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 21
 
6.2%
신 15
 
4.5%
동 13
 
3.9%
구 13
 
3.9%
문 9
 
2.7%
지 8
 
2.4%
입 7
 
2.1%
가 7
 
2.1%
로 6
 
1.8%
원 6
 
1.8%
Other values (128) 232
68.8%
Common
ValueCountFrequency (%)
32
41.6%
( 13
16.9%
) 13
16.9%
3 6
 
7.8%
2 5
 
6.5%
1 5
 
6.5%
4 2
 
2.6%
5 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 337
81.4%
ASCII 77
 
18.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32
41.6%
( 13
16.9%
) 13
16.9%
3 6
 
7.8%
2 5
 
6.5%
1 5
 
6.5%
4 2
 
2.6%
5 1
 
1.3%
Hangul
ValueCountFrequency (%)
대 21
 
6.2%
신 15
 
4.5%
동 13
 
3.9%
구 13
 
3.9%
문 9
 
2.7%
지 8
 
2.4%
입 7
 
2.1%
가 7
 
2.1%
로 6
 
1.8%
원 6
 
1.8%
Other values (128) 232
68.8%

개집표기(턴스타일) EN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)9.3%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3.9491525
Minimum0
Maximum10
Zeros7
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T01:49:53.010423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2488267
Coefficient of variation (CV)0.56944539
Kurtosis0.51166675
Mean3.9491525
Median Absolute Deviation (MAD)1
Skewness0.58274873
Sum466
Variance5.0572215
MonotonicityNot monotonic
2024-04-30T01:49:53.129669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 34
28.6%
2 19
16.0%
5 17
14.3%
3 12
 
10.1%
6 9
 
7.6%
1 7
 
5.9%
0 7
 
5.9%
9 4
 
3.4%
7 4
 
3.4%
10 3
 
2.5%
ValueCountFrequency (%)
0 7
 
5.9%
1 7
 
5.9%
2 19
16.0%
3 12
 
10.1%
4 34
28.6%
5 17
14.3%
6 9
 
7.6%
7 4
 
3.4%
8 2
 
1.7%
9 4
 
3.4%
ValueCountFrequency (%)
10 3
 
2.5%
9 4
 
3.4%
8 2
 
1.7%
7 4
 
3.4%
6 9
 
7.6%
5 17
14.3%
4 34
28.6%
3 12
 
10.1%
2 19
16.0%
1 7
 
5.9%

개집표기(턴스타일) EX
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
111 
1
 
5
3
 
2
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0252101
Min length1

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0
2nd row<NA>
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
93.3%
1 5
 
4.2%
3 2
 
1.7%
<NA> 1
 
0.8%

Length

2024-04-30T01:49:53.242831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:53.635791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 111
93.3%
1 5
 
4.2%
3 2
 
1.7%
na 1
 
0.8%

개집표기(턴스타일) REV
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)29.7%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean15.110169
Minimum0
Maximum41
Zeros7
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T01:49:53.735221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.25
median14.5
Q320
95-th percentile32
Maximum41
Range41
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation8.9507522
Coefficient of variation (CV)0.5923661
Kurtosis0.21834297
Mean15.110169
Median Absolute Deviation (MAD)6
Skewness0.55198423
Sum1783
Variance80.115964
MonotonicityNot monotonic
2024-04-30T01:49:53.855229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
15 9
 
7.6%
12 8
 
6.7%
0 7
 
5.9%
13 7
 
5.9%
7 6
 
5.0%
8 6
 
5.0%
16 5
 
4.2%
22 5
 
4.2%
19 5
 
4.2%
20 5
 
4.2%
Other values (25) 55
46.2%
ValueCountFrequency (%)
0 7
5.9%
1 1
 
0.8%
2 1
 
0.8%
3 1
 
0.8%
4 2
 
1.7%
5 2
 
1.7%
6 4
3.4%
7 6
5.0%
8 6
5.0%
9 3
2.5%
ValueCountFrequency (%)
41 1
0.8%
39 1
0.8%
36 2
1.7%
35 1
0.8%
32 2
1.7%
30 2
1.7%
29 2
1.7%
28 1
0.8%
27 1
0.8%
26 2
1.7%

개집표기(턴스타일) FL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)6.8%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3.059322
Minimum0
Maximum7
Zeros7
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T01:49:53.959541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5.15
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5595987
Coefficient of variation (CV)0.50978574
Kurtosis-0.30395945
Mean3.059322
Median Absolute Deviation (MAD)1
Skewness0.023673081
Sum361
Variance2.4323483
MonotonicityNot monotonic
2024-04-30T01:49:54.061169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 34
28.6%
2 28
23.5%
3 20
16.8%
5 12
 
10.1%
1 11
 
9.2%
0 7
 
5.9%
6 4
 
3.4%
7 2
 
1.7%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
0 7
 
5.9%
1 11
 
9.2%
2 28
23.5%
3 20
16.8%
4 34
28.6%
5 12
 
10.1%
6 4
 
3.4%
7 2
 
1.7%
ValueCountFrequency (%)
7 2
 
1.7%
6 4
 
3.4%
5 12
 
10.1%
4 34
28.6%
3 20
16.8%
2 28
23.5%
1 11
 
9.2%
0 7
 
5.9%

개집표기(턴스타일) 철거
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
118 
57
 
1

Length

Max length4
Median length4
Mean length3.9831933
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 118
99.2%
57 1
 
0.8%

Length

2024-04-30T01:49:54.187711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:54.287425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 118
99.2%
57 1
 
0.8%

슬림게이트 1형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
92 
1
13 
2
 
8
4
 
4
5
 
1

Length

Max length4
Median length4
Mean length3.3193277
Min length1

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st row1
2nd row4
3rd row2
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 92
77.3%
1 13
 
10.9%
2 8
 
6.7%
4 4
 
3.4%
5 1
 
0.8%
6 1
 
0.8%

Length

2024-04-30T01:49:54.389075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:54.508199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 92
77.3%
1 13
 
10.9%
2 8
 
6.7%
4 4
 
3.4%
5 1
 
0.8%
6 1
 
0.8%

슬림게이트 2형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
117 
4
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.9495798
Min length1

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 117
98.3%
4 1
 
0.8%
2 1
 
0.8%

Length

2024-04-30T01:49:54.607448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:54.700239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 117
98.3%
4 1
 
0.8%
2 1
 
0.8%

슬림게이트 3형
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)63.0%
Missing92
Missing (%)77.3%
Infinite0
Infinite (%)0.0%
Mean15.481481
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T01:49:54.796387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median14
Q322.5
95-th percentile29.4
Maximum56
Range55
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation12.292494
Coefficient of variation (CV)0.79401278
Kurtosis3.0305808
Mean15.481481
Median Absolute Deviation (MAD)8
Skewness1.3875164
Sum418
Variance151.10541
MonotonicityNot monotonic
2024-04-30T01:49:54.930851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
28 3
 
2.5%
14 3
 
2.5%
5 2
 
1.7%
20 2
 
1.7%
7 2
 
1.7%
16 2
 
1.7%
6 2
 
1.7%
2 2
 
1.7%
1 1
 
0.8%
3 1
 
0.8%
Other values (7) 7
 
5.9%
(Missing) 92
77.3%
ValueCountFrequency (%)
1 1
 
0.8%
2 2
1.7%
3 1
 
0.8%
5 2
1.7%
6 2
1.7%
7 2
1.7%
9 1
 
0.8%
10 1
 
0.8%
14 3
2.5%
16 2
1.7%
ValueCountFrequency (%)
56 1
 
0.8%
30 1
 
0.8%
28 3
2.5%
26 1
 
0.8%
24 1
 
0.8%
21 1
 
0.8%
20 2
1.7%
16 2
1.7%
14 3
2.5%
10 1
 
0.8%

슬림게이트 5형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
92 
1
13 
2
 
9
4
 
3
5
 
1

Length

Max length4
Median length4
Mean length3.3193277
Min length1

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st row1
2nd row4
3rd row2
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 92
77.3%
1 13
 
10.9%
2 9
 
7.6%
4 3
 
2.5%
5 1
 
0.8%
6 1
 
0.8%

Length

2024-04-30T01:49:55.049963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:55.185867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 92
77.3%
1 13
 
10.9%
2 9
 
7.6%
4 3
 
2.5%
5 1
 
0.8%
6 1
 
0.8%

장애인게이트 설치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
118 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 118
99.2%
4 1
 
0.8%

Length

2024-04-30T01:49:55.302879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:55.392613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 118
99.2%
4 1
 
0.8%

장애인게이트 철거
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
118 
6
 
1

Length

Max length4
Median length4
Mean length3.9747899
Min length1

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 118
99.2%
6 1
 
0.8%

Length

2024-04-30T01:49:55.491053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:55.598222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 118
99.2%
6 1
 
0.8%

스피드게이트 설치
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8319328
Minimum0
Maximum6
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-30T01:49:55.720737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.181204
Coefficient of variation (CV)0.4171017
Kurtosis-0.1151652
Mean2.8319328
Median Absolute Deviation (MAD)1
Skewness0.39526503
Sum337
Variance1.3952428
MonotonicityNot monotonic
2024-04-30T01:49:55.802803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 45
37.8%
4 31
26.1%
3 25
21.0%
1 10
 
8.4%
5 4
 
3.4%
6 3
 
2.5%
0 1
 
0.8%
ValueCountFrequency (%)
0 1
 
0.8%
1 10
 
8.4%
2 45
37.8%
3 25
21.0%
4 31
26.1%
5 4
 
3.4%
6 3
 
2.5%
ValueCountFrequency (%)
6 3
 
2.5%
5 4
 
3.4%
4 31
26.1%
3 25
21.0%
2 45
37.8%
1 10
 
8.4%
0 1
 
0.8%

스피드게이트 철거
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
118 
1
 
1

Length

Max length4
Median length4
Mean length3.9747899
Min length1

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 118
99.2%
1 1
 
0.8%

Length

2024-04-30T01:49:55.915065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:49:56.027427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 118
99.2%
1 1
 
0.8%

Interactions

2024-04-30T01:49:50.728156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:48.219506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:48.883613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.331718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.767373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.252566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.811781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:48.294782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:48.955157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.398417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.836612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.327057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.906298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:48.369800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.029418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.466391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.919878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.409202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.976960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:48.440732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.105441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.535196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.995086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.494729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:51.058361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:48.528509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.187089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.616585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.081418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.570143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:51.129809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:48.814356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.262598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:49.693565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.165449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:49:50.642511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:49:56.104132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선역번호개집표기(턴스타일) EN개집표기(턴스타일) EX개집표기(턴스타일) REV개집표기(턴스타일) FL슬림게이트 1형슬림게이트 2형슬림게이트 3형슬림게이트 5형장애인게이트 설치스피드게이트 설치
호선1.0001.0000.0000.1020.4690.3620.000NaN0.4710.1270.3920.369
역번호1.0001.0000.0000.2980.4290.4650.0000.0000.5830.0500.2200.543
개집표기(턴스타일) EN0.0000.0001.0000.2490.7660.8640.0000.0000.0000.0000.0000.416
개집표기(턴스타일) EX0.1020.2980.2491.0000.0780.5270.000NaN0.0000.0000.0000.000
개집표기(턴스타일) REV0.4690.4290.7660.0781.0000.6350.0000.0000.0000.0000.0000.546
개집표기(턴스타일) FL0.3620.4650.8640.5270.6351.0000.3990.0000.0000.0000.0000.583
슬림게이트 1형0.0000.0000.0000.0000.0000.3991.0000.0000.5050.999NaN0.496
슬림게이트 2형NaN0.0000.000NaN0.0000.0000.0001.0000.0000.000NaN0.000
슬림게이트 3형0.4710.5830.0000.0000.0000.0000.5050.0001.0000.612NaN0.000
슬림게이트 5형0.1270.0500.0000.0000.0000.0000.9990.0000.6121.000NaN0.463
장애인게이트 설치0.3920.2200.0000.0000.0000.000NaNNaNNaNNaN1.0001.000
스피드게이트 설치0.3690.5430.4160.0000.5460.5830.4960.0000.0000.4631.0001.000
2024-04-30T01:49:56.284627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개집표기(턴스타일) 철거슬림게이트 1형장애인게이트 철거슬림게이트 2형장애인게이트 설치스피드게이트 철거슬림게이트 5형개집표기(턴스타일) EX호선
개집표기(턴스타일) 철거1.000NaNNaNNaNNaNNaNNaNNaNNaN
슬림게이트 1형NaN1.000NaN1.0001.000NaN0.9490.0000.000
장애인게이트 철거NaNNaN1.000NaNNaNNaNNaNNaNNaN
슬림게이트 2형NaN1.000NaN1.0001.000NaN1.0001.0001.000
장애인게이트 설치NaN1.000NaN1.0001.000NaN1.0000.0000.260
스피드게이트 철거NaNNaNNaNNaNNaN1.000NaNNaNNaN
슬림게이트 5형NaN0.949NaN1.0001.000NaN1.0000.0000.063
개집표기(턴스타일) EXNaN0.000NaN1.0000.000NaN0.0001.0000.095
호선NaN0.000NaN1.0000.260NaN0.0630.0951.000
2024-04-30T01:49:56.411499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역번호개집표기(턴스타일) EN개집표기(턴스타일) REV개집표기(턴스타일) FL슬림게이트 3형스피드게이트 설치호선개집표기(턴스타일) EX개집표기(턴스타일) 철거슬림게이트 1형슬림게이트 2형슬림게이트 5형장애인게이트 설치장애인게이트 철거스피드게이트 철거
역번호1.000-0.251-0.349-0.2520.085-0.3780.9870.207NaN0.0001.0000.0000.205NaNNaN
개집표기(턴스타일) EN-0.2511.0000.6100.788-0.4270.5300.0660.207NaN0.0001.0000.0000.000NaNNaN
개집표기(턴스타일) REV-0.3490.6101.0000.670-0.3580.6470.2900.037NaN0.0001.0000.0000.000NaNNaN
개집표기(턴스타일) FL-0.2520.7880.6701.000-0.4960.6770.1640.384NaN0.2131.0000.0000.000NaNNaN
슬림게이트 3형0.085-0.427-0.358-0.4961.000-0.0480.3060.0000.0000.3251.0000.4231.0000.0000.000
스피드게이트 설치-0.3780.5300.6470.677-0.0481.0000.2580.000NaN0.3451.0000.3160.978NaNNaN
호선0.9870.0660.2900.1640.3060.2581.0000.095NaN0.0001.0000.0630.260NaNNaN
개집표기(턴스타일) EX0.2070.2070.0370.3840.0000.0000.0951.000NaN0.0001.0000.0000.000NaNNaN
개집표기(턴스타일) 철거NaNNaNNaNNaN0.000NaNNaNNaN1.0000.0000.0000.000NaNNaNNaN
슬림게이트 1형0.0000.0000.0000.2130.3250.3450.0000.0000.0001.0001.0000.9491.0000.0000.000
슬림게이트 2형1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0000.0000.000
슬림게이트 5형0.0000.0000.0000.0000.4230.3160.0630.0000.0000.9491.0001.0001.0000.0000.000
장애인게이트 설치0.2050.0000.0000.0001.0000.9780.2600.000NaN1.0001.0001.0001.000NaNNaN
장애인게이트 철거NaNNaNNaNNaN0.000NaNNaNNaNNaN0.0000.0000.000NaN1.000NaN
스피드게이트 철거NaNNaNNaNNaN0.000NaNNaNNaNNaN0.0000.0000.000NaNNaN1.000

Missing values

2024-04-30T01:49:51.269495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:49:51.450304image/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.
2024-04-30T01:49:51.614066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

호선역번호역 명개집표기(턴스타일) EN개집표기(턴스타일) EX개집표기(턴스타일) REV개집표기(턴스타일) FL개집표기(턴스타일) 철거슬림게이트 1형슬림게이트 2형슬림게이트 3형슬림게이트 5형장애인게이트 설치장애인게이트 철거스피드게이트 설치스피드게이트 철거
01호선150서울역(1)100306<NA>1<NA>1410<NA>4<NA>
11호선151시청(1)<NA><NA><NA><NA><NA>4<NA>5640<NA>4<NA>
21호선152종각60274<NA>2<NA>720<NA>5<NA>
31호선153종로3가(1)50245<NA><NA><NA><NA><NA>0<NA>4<NA>
41호선154종로5가40234<NA><NA><NA><NA><NA>0<NA>3<NA>
51호선155동대문(1)50225<NA><NA><NA><NA><NA>0<NA>5<NA>
61호선156신설동(1)40214<NA><NA><NA><NA><NA>0<NA>4<NA>
71호선157제기동40133<NA><NA><NA><NA><NA>0<NA>2<NA>
81호선158청량리80235<NA><NA><NA><NA><NA>0<NA>3<NA>
91호선159동묘앞2082<NA><NA><NA><NA><NA>4<NA>0<NA>
호선역번호역 명개집표기(턴스타일) EN개집표기(턴스타일) EX개집표기(턴스타일) REV개집표기(턴스타일) FL개집표기(턴스타일) 철거슬림게이트 1형슬림게이트 2형슬림게이트 3형슬림게이트 5형장애인게이트 설치장애인게이트 철거스피드게이트 설치스피드게이트 철거
1094호선425회현40194<NA><NA><NA><NA><NA>0<NA>2<NA>
1104호선426서울역2052<NA>1<NA>610<NA>2<NA>
1114호선427숙대입구40204<NA><NA><NA><NA><NA>0<NA>4<NA>
1124호선428삼각지40174<NA><NA><NA><NA><NA>0<NA>2<NA>
1134호선429신용산40154<NA><NA><NA><NA><NA>0<NA>4<NA>
1144호선430이 촌40132<NA><NA><NA><NA><NA>0<NA>2<NA>
1154호선431동 작2062<NA><NA><NA><NA><NA>0<NA>2<NA>
1164호선432총신대40154<NA><NA><NA><NA><NA>0<NA>3<NA>
1174호선433사당(4)60136<NA><NA><NA><NA><NA>0<NA>3<NA>
1184호선434남태령2041<NA><NA><NA><NA><NA>0<NA>1<NA>