Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory81.4 B

Variable types

Categorical2
Text2
Numeric5

Dataset

Description파일 다운로드
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-13214/F/1/datasetView.do

Alerts

합계 is highly overall correlated with 1호선 and 4 other fieldsHigh correlation
1호선 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
2호선 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
3호선 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
4호선 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
단위 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
1호선 has 3 (10.0%) zerosZeros

Reproduction

Analysis started2024-04-29 16:45:03.188291
Analysis finished2024-04-29 16:45:05.639119
Duration2.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Categorical

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
전력설비
12 
변전설비
11 
송배전
4 
전차선
3 

Length

Max length4
Median length4
Mean length3.7666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전력설비 12
40.0%
변전설비 11
36.7%
송배전 4
 
13.3%
전차선 3
 
10.0%

Length

2024-04-30T01:45:05.706700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:45:05.797167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전력설비 12
40.0%
변전설비 11
36.7%
송배전 4
 
13.3%
전차선 3
 
10.0%

설비명
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-30T01:45:05.940778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length3.9333333
Min length1

Characters and Unicode

Total characters118
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)33.3%

Sample

1st row변전소수
2nd row변전소
3rd row정류기
4th row정류기
5th row정류용변압기
ValueCountFrequency (%)
변압기 3
 
7.5%
량 3
 
7.5%
차 3
 
7.5%
단 3
 
7.5%
기 3
 
7.5%
차단기 3
 
7.5%
용 3
 
7.5%
계 2
 
5.0%
정류기 2
 
5.0%
정류용변압기 2
 
5.0%
Other values (12) 13
32.5%
2024-04-30T01:45:06.232766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 17
14.4%
10
 
8.5%
변 9
 
7.6%
전 9
 
7.6%
압 8
 
6.8%
용 7
 
5.9%
차 6
 
5.1%
단 6
 
5.1%
정 4
 
3.4%
류 4
 
3.4%
Other values (27) 38
32.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106
89.8%
Space Separator 10
 
8.5%
Close Punctuation 1
 
0.8%
Open Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 17
16.0%
변 9
 
8.5%
전 9
 
8.5%
압 8
 
7.5%
용 7
 
6.6%
차 6
 
5.7%
단 6
 
5.7%
정 4
 
3.8%
류 4
 
3.8%
량 3
 
2.8%
Other values (24) 33
31.1%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106
89.8%
Common 12
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 17
16.0%
변 9
 
8.5%
전 9
 
8.5%
압 8
 
7.5%
용 7
 
6.6%
차 6
 
5.7%
단 6
 
5.7%
정 4
 
3.8%
류 4
 
3.8%
량 3
 
2.8%
Other values (24) 33
31.1%
Common
ValueCountFrequency (%)
10
83.3%
) 1
 
8.3%
( 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106
89.8%
ASCII 12
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 17
16.0%
변 9
 
8.5%
전 9
 
8.5%
압 8
 
7.5%
용 7
 
6.6%
차 6
 
5.7%
단 6
 
5.7%
정 4
 
3.8%
류 4
 
3.8%
량 3
 
2.8%
Other values (24) 33
31.1%
ASCII
ValueCountFrequency (%)
10
83.3%
) 1
 
8.3%
( 1
 
8.3%

설비명.1
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-30T01:45:06.381658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.4666667
Min length1

Characters and Unicode

Total characters134
Distinct characters51
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row변전소수
2nd row수 전
3rd row실리콘정류기
4th row용 량
5th row수 량
ValueCountFrequency (%)
계 6
14.6%
량 5
12.2%
연장 3
 
7.3%
수 3
 
7.3%
용 3
 
7.3%
일 2
 
4.9%
반 2
 
4.9%
22.9kv 2
 
4.9%
터널환기 2
 
4.9%
690v(acb 1
 
2.4%
Other values (12) 12
29.3%
2024-04-30T01:45:06.854126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
 
8.2%
계 6
 
4.5%
( 6
 
4.5%
) 6
 
4.5%
. 5
 
3.7%
2 5
 
3.7%
기 5
 
3.7%
량 5
 
3.7%
k 4
 
3.0%
전 4
 
3.0%
Other values (41) 77
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
58.2%
Decimal Number 13
 
9.7%
Space Separator 11
 
8.2%
Uppercase Letter 8
 
6.0%
Lowercase Letter 7
 
5.2%
Open Punctuation 6
 
4.5%
Close Punctuation 6
 
4.5%
Other Punctuation 5
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 6
 
7.7%
기 5
 
6.4%
량 5
 
6.4%
전 4
 
5.1%
수 4
 
5.1%
실 3
 
3.8%
널 3
 
3.8%
고 3
 
3.8%
용 3
 
3.8%
터 3
 
3.8%
Other values (26) 39
50.0%
Decimal Number
ValueCountFrequency (%)
2 5
38.5%
6 3
23.1%
9 3
23.1%
7 1
 
7.7%
0 1
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
V 3
37.5%
C 2
25.0%
B 2
25.0%
A 1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
k 4
57.1%
v 3
42.9%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
58.2%
Common 41
30.6%
Latin 15
 
11.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 6
 
7.7%
기 5
 
6.4%
량 5
 
6.4%
전 4
 
5.1%
수 4
 
5.1%
실 3
 
3.8%
널 3
 
3.8%
고 3
 
3.8%
용 3
 
3.8%
터 3
 
3.8%
Other values (26) 39
50.0%
Common
ValueCountFrequency (%)
11
26.8%
( 6
14.6%
) 6
14.6%
. 5
12.2%
2 5
12.2%
6 3
 
7.3%
9 3
 
7.3%
7 1
 
2.4%
0 1
 
2.4%
Latin
ValueCountFrequency (%)
k 4
26.7%
V 3
20.0%
v 3
20.0%
C 2
13.3%
B 2
13.3%
A 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
58.2%
ASCII 56
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
19.6%
( 6
10.7%
) 6
10.7%
. 5
8.9%
2 5
8.9%
k 4
 
7.1%
V 3
 
5.4%
6 3
 
5.4%
9 3
 
5.4%
v 3
 
5.4%
Other values (5) 7
12.5%
Hangul
ValueCountFrequency (%)
계 6
 
7.7%
기 5
 
6.4%
량 5
 
6.4%
전 4
 
5.1%
수 4
 
5.1%
실 3
 
3.8%
널 3
 
3.8%
고 3
 
3.8%
용 3
 
3.8%
터 3
 
3.8%
Other values (26) 39
50.0%

단위
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
대
12 
km
7 
개소
5 
kVA
5 
kW
 
1

Length

Max length3
Median length2
Mean length1.7666667
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row개소
2nd row개소
3rd row대
4th rowkW
5th row대

Common Values

ValueCountFrequency (%)
대 12
40.0%
km 7
23.3%
개소 5
16.7%
kVA 5
16.7%
kW 1
 
3.3%

Length

2024-04-30T01:45:06.983429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:45:07.096698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대 12
40.0%
km 7
23.3%
개소 5
16.7%
kva 5
16.7%
kw 1
 
3.3%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73041.7
Minimum42
Maximum643360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:07.204470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile51.9
Q1143
median491.65
Q31802.5
95-th percentile479885
Maximum643360
Range643318
Interquartile range (IQR)1659.5

Descriptive statistics

Standard deviation174691.14
Coefficient of variation (CV)2.3916631
Kurtosis4.8818514
Mean73041.7
Median Absolute Deviation (MAD)419.65
Skewness2.401185
Sum2191251
Variance3.0516994 × 1010
MonotonicityNot monotonic
2024-04-30T01:45:07.310027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
42.0 2
 
6.7%
64.0 2
 
6.7%
143.0 2
 
6.7%
283205.0 1
 
3.3%
1322.9 1
 
3.3%
437.3 1
 
3.3%
243.7 1
 
3.3%
2003.9 1
 
3.3%
198.0 1
 
3.3%
238.1 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
42.0 2
6.7%
64.0 2
6.7%
80.0 1
3.3%
120.0 1
3.3%
133.0 1
3.3%
143.0 2
6.7%
198.0 1
3.3%
238.1 1
3.3%
243.7 1
3.3%
400.0 1
3.3%
ValueCountFrequency (%)
643360.0 1
3.3%
572000.0 1
3.3%
367300.0 1
3.3%
297675.0 1
3.3%
283205.0 1
3.3%
14470.0 1
3.3%
2003.9 1
3.3%
1939.0 1
3.3%
1393.0 1
3.3%
1322.9 1
3.3%

1호선
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5273.6833
Minimum0
Maximum49720
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:07.423142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median26.55
Q397.45
95-th percentile34550
Maximum49720
Range49720
Interquartile range (IQR)87.45

Descriptive statistics

Standard deviation13033.875
Coefficient of variation (CV)2.4714937
Kurtosis6.027125
Mean5273.6833
Median Absolute Deviation (MAD)26.55
Skewness2.5802363
Sum158210.5
Variance1.698819 × 108
MonotonicityNot monotonic
2024-04-30T01:45:07.539067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3.0 3
 
10.0%
0.0 3
 
10.0%
10.0 2
 
6.7%
20295.0 2
 
6.7%
11.0 2
 
6.7%
69.0 2
 
6.7%
67.0 1
 
3.3%
54.0 1
 
3.3%
80.8 1
 
3.3%
17.6 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
0.0 3
10.0%
3.0 3
10.0%
4.0 1
 
3.3%
10.0 2
6.7%
11.0 2
6.7%
11.8 1
 
3.3%
17.6 1
 
3.3%
18.1 1
 
3.3%
21.1 1
 
3.3%
32.0 1
 
3.3%
ValueCountFrequency (%)
49720.0 1
3.3%
44000.0 1
3.3%
23000.0 1
3.3%
20295.0 2
6.7%
157.0 1
3.3%
110.1 1
3.3%
103.0 1
3.3%
80.8 1
3.3%
69.0 2
6.7%
67.0 1
3.3%

2호선
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28524.89
Minimum15
Maximum257640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:07.636536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile17.25
Q157
median203.05
Q3778.275
95-th percentile182624.25
Maximum257640
Range257625
Interquartile range (IQR)721.275

Descriptive statistics

Standard deviation68831.68
Coefficient of variation (CV)2.4130393
Kurtosis5.3049017
Mean28524.89
Median Absolute Deviation (MAD)175
Skewness2.4643196
Sum855746.7
Variance4.7378002 × 109
MonotonicityNot monotonic
2024-04-30T01:45:07.746464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
15.0 2
 
6.7%
20.0 2
 
6.7%
375.0 2
 
6.7%
57.0 2
 
6.7%
4250.0 1
 
3.3%
569.3 1
 
3.3%
190.1 1
 
3.3%
81.4 1
 
3.3%
840.7 1
 
3.3%
103.4 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
15.0 2
6.7%
20.0 2
6.7%
25.0 1
3.3%
50.0 1
3.3%
56.0 1
3.3%
57.0 2
6.7%
81.4 1
3.3%
89.7 1
3.3%
103.4 1
3.3%
154.0 1
3.3%
ValueCountFrequency (%)
257640.0 1
3.3%
228000.0 1
3.3%
127165.0 1
3.3%
122915.0 1
3.3%
110200.0 1
3.3%
4250.0 1
3.3%
855.0 1
3.3%
840.7 1
3.3%
591.0 1
3.3%
569.3 1
3.3%

3호선
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22497.663
Minimum13
Maximum183680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:07.848696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile18.85
Q141
median130.55
Q3516
95-th percentile152300
Maximum183680
Range183667
Interquartile range (IQR)475

Descriptive statistics

Standard deviation52670.335
Coefficient of variation (CV)2.3411469
Kurtosis3.8276387
Mean22497.663
Median Absolute Deviation (MAD)111.05
Skewness2.2470158
Sum674929.9
Variance2.7741641 × 109
MonotonicityNot monotonic
2024-04-30T01:45:07.944803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
13.0 2
 
6.7%
29.0 2
 
6.7%
251.0 2
 
6.7%
41.0 2
 
6.7%
7120.0 1
 
3.3%
391.2 1
 
3.3%
122.8 1
 
3.3%
76.8 1
 
3.3%
590.9 1
 
3.3%
52.0 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
13.0 2
6.7%
26.0 1
3.3%
29.0 2
6.7%
34.0 1
3.3%
40.0 1
3.3%
41.0 2
6.7%
52.0 1
3.3%
76.8 1
3.3%
77.1 1
3.3%
120.0 1
3.3%
ValueCountFrequency (%)
183680.0 1
3.3%
164000.0 1
3.3%
138000.0 1
3.3%
92665.0 1
3.3%
85545.0 1
3.3%
7120.0 1
3.3%
590.9 1
3.3%
549.0 1
3.3%
417.0 1
3.3%
391.2 1
3.3%

4호선
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16747.597
Minimum11
Maximum152320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:08.051325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12.8
Q134
median101.4
Q3354
95-th percentile118045
Maximum152320
Range152309
Interquartile range (IQR)320

Descriptive statistics

Standard deviation41023.653
Coefficient of variation (CV)2.4495248
Kurtosis5.4448107
Mean16747.597
Median Absolute Deviation (MAD)81.4
Skewness2.513003
Sum502427.9
Variance1.6829401 × 109
MonotonicityNot monotonic
2024-04-30T01:45:08.175554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11.0 2
 
6.7%
15.0 2
 
6.7%
169.0 2
 
6.7%
34.0 2
 
6.7%
3100.0 1
 
3.3%
281.7 1
 
3.3%
106.8 1
 
3.3%
73.8 1
 
3.3%
462.2 1
 
3.3%
39.5 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
11.0 2
6.7%
15.0 2
6.7%
25.0 1
3.3%
26.0 1
3.3%
27.0 1
3.3%
34.0 2
6.7%
39.5 1
3.3%
53.2 1
3.3%
73.8 1
3.3%
92.7 1
3.3%
ValueCountFrequency (%)
152320.0 1
3.3%
136000.0 1
3.3%
96100.0 1
3.3%
57550.0 1
3.3%
54450.0 1
3.3%
3100.0 1
3.3%
462.2 1
3.3%
378.0 1
3.3%
282.0 1
3.3%
281.7 1
3.3%

Interactions

2024-04-30T01:45:05.027878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:03.469355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:03.843905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.229120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.597437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:05.116601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:03.544415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:03.909025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.308175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.683411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:05.194110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:03.621160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:03.977585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.373042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.756499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:05.265654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:03.688155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.043341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.449328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.833566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:05.352496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:03.761321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.122593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.520080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:04.924042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:45:08.257392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분설비명설비명.1단위합계1호선2호선3호선4호선
구 분1.0000.9620.8490.5770.0000.0000.0000.0000.000
설비명0.9621.0000.8930.8950.0000.0000.0000.0000.000
설비명.10.8490.8931.0000.2870.0000.0000.0000.0000.000
단위0.5770.8950.2871.0000.9140.7740.7740.7090.914
합계0.0000.0000.0000.9141.0001.0001.0001.0001.000
1호선0.0000.0000.0000.7741.0001.0001.0001.0001.000
2호선0.0000.0000.0000.7741.0001.0001.0001.0001.000
3호선0.0000.0000.0000.7091.0001.0001.0001.0001.000
4호선0.0000.0000.0000.9141.0001.0001.0001.0001.000
2024-04-30T01:45:08.353242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단위구 분
단위1.0000.490
구 분0.4901.000
2024-04-30T01:45:08.430584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계1호선2호선3호선4호선구 분단위
합계1.0000.8560.9940.9980.9990.0000.594
1호선0.8561.0000.8520.8530.8540.0000.710
2호선0.9940.8521.0000.9940.9920.0000.710
3호선0.9980.8530.9941.0000.9960.0000.559
4호선0.9990.8540.9920.9961.0000.0000.594
구 분0.0000.0000.0000.0000.0001.0000.490
단위0.5940.7100.7100.5590.5940.4901.000

Missing values

2024-04-30T01:45:05.468902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:45:05.595870image/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

구 분설비명설비명.1단위합계1호선2호선3호선4호선
0변전설비변전소수변전소수개소42.03.015.013.011.0
1변전설비변전소수 전개소42.03.015.013.011.0
2변전설비정류기실리콘정류기대143.011.057.041.034.0
3변전설비정류기용 량kW572000.044000.0228000.0164000.0136000.0
4변전설비정류용변압기수 량대143.011.057.041.034.0
5변전설비정류용변압기용 량kVA643360.049720.0257640.0183680.0152320.0
6변전설비배전용변압기수 량대80.04.025.026.025.0
7변전설비배전용변압기용 량kVA367300.023000.0110200.0138000.096100.0
8변전설비차단기계대994.067.0367.0307.0253.0
9변전설비차단기고압.특고압대594.035.0213.0187.0159.0
구 분설비명설비명.1단위합계1호선2호선3호선4호선
20전력설비차 단 기계대1939.0157.0855.0549.0378.0
21전력설비차 단 기7.2kV(VCB)대1393.0103.0591.0417.0282.0
22전력설비차 단 기690V(ACB)대546.054.0264.0132.096.0
23전차선계계km436.121.1193.1129.192.7
24전차선강 체지하부km238.118.189.777.153.2
25전차선카테나리지상부km198.03.0103.452.039.5
26송배전계계km2003.9110.1840.7590.9462.2
27송배전수전선로(22.9kv) 연장km243.711.881.476.873.8
28송배전연락송전(22.9kv) 연장km437.317.6190.1122.8106.8
29송배전고압배전(6.6kv) 연장km1322.980.8569.3391.2281.7