Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory117.4 B

Variable types

Categorical2
Text2
Numeric9

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 8 other fieldsHigh correlation
1호선 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
2호선 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
3호선 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
4호선 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
5호선 is highly overall correlated with 합계 and 7 other fieldsHigh correlation
6호선 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
7호선 is highly overall correlated with 합계 and 7 other fieldsHigh correlation
8호선 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
단위 is highly overall correlated with 합계 and 6 other fieldsHigh correlation
1호선 has 4 (13.3%) zerosZeros

Reproduction

Analysis started2024-04-29 16:44:50.120621
Analysis finished2024-04-29 16:44:58.041050
Duration7.92 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

2024-04-30T01:44:58.096076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:44:58.186001image/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
2024-04-30T01:44:58.339570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length3.9333333
Min length1

Characters and Unicode

Total characters118
Distinct characters36
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%
소 2
 
5.0%
정류기 2
 
5.0%
정류용변압기 2
 
5.0%
Other values (12) 13
32.5%
2024-04-30T01:44:58.655028image/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 (26) 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%
소 4
 
3.8%
Other values (23) 32
30.2%
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%
소 4
 
3.8%
Other values (23) 32
30.2%
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%
소 4
 
3.8%
Other values (23) 32
30.2%
ASCII
ValueCountFrequency (%)
10
83.3%
) 1
 
8.3%
( 1
 
8.3%

설비명.1
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-30T01:44:58.816976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.1333333
Min length1

Characters and Unicode

Total characters124
Distinct characters45
Distinct categories7 ?
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 (%)
량 5
 
12.5%
소계 4
 
10.0%
연장 3
 
7.5%
수 3
 
7.5%
용 3
 
7.5%
터널용 2
 
5.0%
22.9kv 2
 
5.0%
계 2
 
5.0%
역사용 2
 
5.0%
본선(터널)전기실 1
 
2.5%
Other values (13) 13
32.5%
2024-04-30T01:44:59.069463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
 
8.1%
용 7
 
5.6%
계 6
 
4.8%
소 5
 
4.0%
량 5
 
4.0%
사 4
 
3.2%
전 4
 
3.2%
) 4
 
3.2%
기 4
 
3.2%
2 4
 
3.2%
Other values (35) 71
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
71.0%
Space Separator 10
 
8.1%
Decimal Number 8
 
6.5%
Lowercase Letter 6
 
4.8%
Close Punctuation 4
 
3.2%
Other Punctuation 4
 
3.2%
Open Punctuation 4
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 7
 
8.0%
계 6
 
6.8%
소 5
 
5.7%
량 5
 
5.7%
사 4
 
4.5%
전 4
 
4.5%
기 4
 
4.5%
역 4
 
4.5%
널 4
 
4.5%
터 4
 
4.5%
Other values (26) 41
46.6%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
6 2
25.0%
9 2
25.0%
Lowercase Letter
ValueCountFrequency (%)
v 3
50.0%
k 3
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
71.0%
Common 30
 
24.2%
Latin 6
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 7
 
8.0%
계 6
 
6.8%
소 5
 
5.7%
량 5
 
5.7%
사 4
 
4.5%
전 4
 
4.5%
기 4
 
4.5%
역 4
 
4.5%
널 4
 
4.5%
터 4
 
4.5%
Other values (26) 41
46.6%
Common
ValueCountFrequency (%)
10
33.3%
) 4
 
13.3%
2 4
 
13.3%
. 4
 
13.3%
( 4
 
13.3%
6 2
 
6.7%
9 2
 
6.7%
Latin
ValueCountFrequency (%)
v 3
50.0%
k 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
71.0%
ASCII 36
29.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10
27.8%
) 4
 
11.1%
2 4
 
11.1%
. 4
 
11.1%
( 4
 
11.1%
v 3
 
8.3%
k 3
 
8.3%
6 2
 
5.6%
9 2
 
5.6%
Hangul
ValueCountFrequency (%)
용 7
 
8.0%
계 6
 
6.8%
소 5
 
5.7%
량 5
 
5.7%
사 4
 
4.5%
전 4
 
4.5%
기 4
 
4.5%
역 4
 
4.5%
널 4
 
4.5%
터 4
 
4.5%
Other values (26) 41
46.6%

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

2024-04-30T01:44:59.192260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:44:59.293062image/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 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153773.09
Minimum88
Maximum1186890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:44:59.404072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile137.15
Q1303.5
median951.45
Q32760.425
95-th percentile961650
Maximum1186890
Range1186802
Interquartile range (IQR)2456.925

Descriptive statistics

Standard deviation351453.52
Coefficient of variation (CV)2.2855333
Kurtosis2.9313593
Mean153773.09
Median Absolute Deviation (MAD)766.45
Skewness2.0877935
Sum4613192.8
Variance1.2351958 × 1011
MonotonicityNot monotonic
2024-04-30T01:44:59.539761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
308.0 2
 
6.7%
185.0 2
 
6.7%
98.0 1
 
3.3%
698960.0 1
 
3.3%
2827.9 1
 
3.3%
995.0 1
 
3.3%
609.1 1
 
3.3%
4432.0 1
 
3.3%
327.2 1
 
3.3%
580.7 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
88.0 1
3.3%
98.0 1
3.3%
185.0 2
6.7%
195.0 1
3.3%
269.0 1
3.3%
277.0 1
3.3%
302.0 1
3.3%
308.0 2
6.7%
327.2 1
3.3%
580.7 1
3.3%
ValueCountFrequency (%)
1186890.0 1
3.3%
1053000.0 1
3.3%
850000.0 1
3.3%
748555.0 1
3.3%
698960.0 1
3.3%
49995.0 1
3.3%
4432.0 1
3.3%
2827.9 1
3.3%
2558.0 1
3.3%
2289.0 1
3.3%

1호선
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5270.2133
Minimum0
Maximum49720
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:44:59.649804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median19.6
Q398.95
95-th percentile34550
Maximum49720
Range49720
Interquartile range (IQR)93.45

Descriptive statistics

Standard deviation13035.306
Coefficient of variation (CV)2.4733924
Kurtosis6.0262546
Mean5270.2133
Median Absolute Deviation (MAD)19.6
Skewness2.5801019
Sum158106.4
Variance1.699192 × 108
MonotonicityNot monotonic
2024-04-30T01:44:59.759695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 4
 
13.3%
3.0 3
 
10.0%
69.0 2
 
6.7%
105.0 2
 
6.7%
10.0 2
 
6.7%
11.0 2
 
6.7%
20295.0 2
 
6.7%
80.8 1
 
3.3%
17.6 1
 
3.3%
11.7 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
0.0 4
13.3%
3.0 3
10.0%
4.0 1
 
3.3%
10.0 2
6.7%
11.0 2
6.7%
11.7 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%
110.1 1
3.3%
105.0 2
6.7%
80.8 1
3.3%
69.0 2
6.7%
67.0 1
3.3%
35.0 1
3.3%

2호선
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29396.953
Minimum15
Maximum262160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:44:59.878534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.8
Q156.5
median190.1
Q3599.25
95-th percentile185949.25
Maximum262160
Range262145
Interquartile range (IQR)542.75

Descriptive statistics

Standard deviation70568.202
Coefficient of variation (CV)2.4005277
Kurtosis5.0480672
Mean29396.953
Median Absolute Deviation (MAD)170.6
Skewness2.4220909
Sum881908.6
Variance4.9798712 × 109
MonotonicityNot monotonic
2024-04-30T01:44:59.996988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
15.0 2
 
6.7%
20.0 2
 
6.7%
58.0 2
 
6.7%
125415.0 1
 
3.3%
555.3 1
 
3.3%
187.4 1
 
3.3%
75.8 1
 
3.3%
818.5 1
 
3.3%
103.1 1
 
3.3%
89.7 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
15.0 2
6.7%
19.0 1
3.3%
20.0 2
6.7%
28.0 1
3.3%
50.0 1
3.3%
56.0 1
3.3%
58.0 2
6.7%
75.8 1
3.3%
89.7 1
3.3%
103.1 1
3.3%
ValueCountFrequency (%)
262160.0 1
3.3%
232000.0 1
3.3%
129665.0 1
3.3%
125415.0 1
3.3%
123400.0 1
3.3%
4250.0 1
3.3%
818.5 1
3.3%
604.0 1
3.3%
585.0 1
3.3%
555.3 1
3.3%

3호선
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22546.793
Minimum13
Maximum183680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:00.102876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile18.85
Q141
median125.7
Q3431.55
95-th percentile152300
Maximum183680
Range183667
Interquartile range (IQR)390.55

Descriptive statistics

Standard deviation52721.411
Coefficient of variation (CV)2.3383108
Kurtosis3.7983971
Mean22546.793
Median Absolute Deviation (MAD)99.5
Skewness2.2416098
Sum676403.8
Variance2.7795472 × 109
MonotonicityNot monotonic
2024-04-30T01:45:00.215326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
13.0 2
 
6.7%
41.0 2
 
6.7%
93315.0 1
 
3.3%
394.2 1
 
3.3%
122.3 1
 
3.3%
76.8 1
 
3.3%
593.3 1
 
3.3%
52.0 1
 
3.3%
77.1 1
 
3.3%
129.1 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
13.0 2
6.7%
26.0 1
3.3%
29.0 1
3.3%
30.0 1
3.3%
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%
77.0 1
3.3%
ValueCountFrequency (%)
183680.0 1
3.3%
164000.0 1
3.3%
138000.0 1
3.3%
93315.0 1
3.3%
86095.0 1
3.3%
7620.0 1
3.3%
593.3 1
3.3%
444.0 1
3.3%
394.2 1
3.3%
367.0 1
3.3%

4호선
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16755.153
Minimum11
Maximum152320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:00.331969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12.8
Q134
median100.4
Q3294.625
95-th percentile118045
Maximum152320
Range152309
Interquartile range (IQR)260.625

Descriptive statistics

Standard deviation41030.656
Coefficient of variation (CV)2.448838
Kurtosis5.4375279
Mean16755.153
Median Absolute Deviation (MAD)80.4
Skewness2.5116444
Sum502654.6
Variance1.6835148 × 109
MonotonicityNot monotonic
2024-04-30T01:45:00.737161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11.0 2
 
6.7%
15.0 2
 
6.7%
34.0 2
 
6.7%
54450.0 1
 
3.3%
281.5 1
 
3.3%
106.8 1
 
3.3%
73.8 1
 
3.3%
462.1 1
 
3.3%
39.5 1
 
3.3%
53.2 1
 
3.3%
Other values (17) 17
56.7%
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%
42.0 1
3.3%
53.2 1
3.3%
73.8 1
3.3%
ValueCountFrequency (%)
152320.0 1
3.3%
136000.0 1
3.3%
96100.0 1
3.3%
57750.0 1
3.3%
54450.0 1
3.3%
3300.0 1
3.3%
462.1 1
3.3%
299.0 1
3.3%
281.5 1
3.3%
257.0 1
3.3%

5호선
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27470.74
Minimum14
Maximum193230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:00.855467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile27.55
Q152
median177.05
Q3469.7
95-th percentile169650
Maximum193230
Range193216
Interquartile range (IQR)417.7

Descriptive statistics

Standard deviation61587.504
Coefficient of variation (CV)2.241931
Kurtosis2.1981555
Mean27470.74
Median Absolute Deviation (MAD)137.5
Skewness1.9701822
Sum824122.2
Variance3.7930207 × 109
MonotonicityNot monotonic
2024-04-30T01:45:00.973473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
57.0 2
 
6.7%
19.0 1
 
3.3%
143660.0 1
 
3.3%
486.6 1
 
3.3%
191.1 1
 
3.3%
155.6 1
 
3.3%
833.3 1
 
3.3%
41.0 1
 
3.3%
115.3 1
 
3.3%
156.3 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
14.0 1
3.3%
19.0 1
3.3%
38.0 1
3.3%
41.0 1
3.3%
42.0 1
3.3%
43.0 1
3.3%
44.0 1
3.3%
51.0 1
3.3%
55.0 1
3.3%
57.0 2
6.7%
ValueCountFrequency (%)
193230.0 1
3.3%
171000.0 1
3.3%
168000.0 1
3.3%
143660.0 1
3.3%
130460.0 1
3.3%
13200.0 1
3.3%
833.3 1
3.3%
486.6 1
3.3%
419.0 1
3.3%
360.0 1
3.3%

6호선
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17755.64
Minimum12
Maximum118650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:01.116466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile15.285
Q135
median99
Q3313.5
95-th percentile105052.25
Maximum118650
Range118638
Interquartile range (IQR)278.5

Descriptive statistics

Standard deviation39760.747
Coefficient of variation (CV)2.2393306
Kurtosis1.8278739
Mean17755.64
Median Absolute Deviation (MAD)83.35
Skewness1.9102329
Sum532669.2
Variance1.580917 × 109
MonotonicityNot monotonic
2024-04-30T01:45:01.216098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
21.0 3
 
10.0%
12.0 2
 
6.7%
35.0 2
 
6.7%
99545.0 1
 
3.3%
332.0 1
 
3.3%
101.0 1
 
3.3%
51.9 1
 
3.3%
484.9 1
 
3.3%
19.3 1
 
3.3%
72.4 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
12.0 2
6.7%
19.3 1
 
3.3%
21.0 3
10.0%
24.0 1
 
3.3%
35.0 2
6.7%
38.0 1
 
3.3%
39.0 1
 
3.3%
51.9 1
 
3.3%
72.4 1
 
3.3%
91.7 1
 
3.3%
ValueCountFrequency (%)
118650.0 1
3.3%
105095.0 1
3.3%
105000.0 1
3.3%
99545.0 1
3.3%
96000.0 1
3.3%
5550.0 1
3.3%
484.9 1
3.3%
332.0 1
3.3%
258.0 1
3.3%
237.0 1
3.3%

7호선
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28445.113
Minimum17
Maximum193230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:01.323041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile29
Q153.7
median182.6
Q3487.4
95-th percentile167175
Maximum193230
Range193213
Interquartile range (IQR)433.7

Descriptive statistics

Standard deviation63441.702
Coefficient of variation (CV)2.2303199
Kurtosis1.905064
Mean28445.113
Median Absolute Deviation (MAD)142.6
Skewness1.9208686
Sum853353.4
Variance4.0248496 × 109
MonotonicityNot monotonic
2024-04-30T01:45:01.427713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
57.0 3
 
10.0%
40.0 2
 
6.7%
20.0 1
 
3.3%
148050.0 1
 
3.3%
506.2 1
 
3.3%
197.5 1
 
3.3%
99.8 1
 
3.3%
803.5 1
 
3.3%
52.6 1
 
3.3%
115.1 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
17.0 1
 
3.3%
20.0 1
 
3.3%
40.0 2
6.7%
42.0 1
 
3.3%
50.0 1
 
3.3%
51.0 1
 
3.3%
52.6 1
 
3.3%
57.0 3
10.0%
99.8 1
 
3.3%
115.1 1
 
3.3%
ValueCountFrequency (%)
193230.0 1
3.3%
171000.0 1
3.3%
162500.0 1
3.3%
161000.0 1
3.3%
148050.0 1
3.3%
12950.0 1
3.3%
803.5 1
3.3%
506.2 1
3.3%
431.0 1
3.3%
395.0 1
3.3%

8호선
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6132.4867
Minimum3
Maximum43000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-30T01:45:01.547714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7.25
Q116.175
median64.35
Q3171.975
95-th percentile36368.75
Maximum43000
Range42997
Interquartile range (IQR)155.8

Descriptive statistics

Standard deviation13655.297
Coefficient of variation (CV)2.2267145
Kurtosis2.1075501
Mean6132.4867
Median Absolute Deviation (MAD)48.35
Skewness1.9529697
Sum183974.6
Variance1.8646713 × 108
MonotonicityNot monotonic
2024-04-30T01:45:01.669932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
16.0 3
 
10.0%
15.0 2
 
6.7%
5.0 1
 
3.3%
37775.0 1
 
3.3%
191.3 1
 
3.3%
71.3 1
 
3.3%
63.7 1
 
3.3%
326.3 1
 
3.3%
16.7 1
 
3.3%
39.8 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
3.0 1
 
3.3%
5.0 1
 
3.3%
10.0 1
 
3.3%
15.0 2
6.7%
16.0 3
10.0%
16.7 1
 
3.3%
17.0 1
 
3.3%
18.0 1
 
3.3%
39.8 1
 
3.3%
42.0 1
 
3.3%
ValueCountFrequency (%)
43000.0 1
3.3%
37775.0 1
3.3%
34650.0 1
3.3%
33900.0 1
3.3%
30000.0 1
3.3%
3125.0 1
3.3%
326.3 1
3.3%
191.3 1
3.3%
114.0 1
3.3%
109.0 1
3.3%

Interactions

2024-04-30T01:44:57.024005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:50.705853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.524453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.336094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.025100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.799720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.615949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.612223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.300130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:57.121478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:50.775679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.613575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.425730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.100682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.892476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.725616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.691251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.376525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:57.200548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:50.850290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.689989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.501632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.173032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.971292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.816020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.754506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.465089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:57.279548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:50.932776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.774074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.572517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.258605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.041555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.884993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.822803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.538274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:57.362848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.016732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.848519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.652742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.350983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.117126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.970300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.908615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.615304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:57.447835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.113179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.930422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.727746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.427589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.215348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.044655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.989063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.693768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:57.539631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.207170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.017792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.809323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.525624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.333828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.129697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.064021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.769703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:57.630039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.309715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.126816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.884941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.623904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.427461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.255151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.143603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.847345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:57.721169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:51.437133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.249655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:52.960024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:53.719226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:54.521853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:55.544480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.230668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:44:56.936526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:45:01.765592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분설비명설비명.1단위합계1호선2호선3호선4호선5호선6호선7호선8호선
구 분1.0000.9620.9670.5770.0000.0000.0000.0000.0000.0000.0000.0000.000
설비명0.9621.0000.9170.8950.0000.0000.0000.0000.0000.0000.0000.0000.000
설비명.10.9670.9171.0000.8580.0000.0000.0000.0000.0000.0000.0000.0000.000
단위0.5770.8950.8581.0000.7090.7740.7740.7090.9140.8040.6290.5400.914
합계0.0000.0000.0000.7091.0001.0001.0001.0001.0001.0001.0001.0001.000
1호선0.0000.0000.0000.7741.0001.0001.0001.0001.0000.8731.0000.9821.000
2호선0.0000.0000.0000.7741.0001.0001.0001.0001.0000.8731.0000.9821.000
3호선0.0000.0000.0000.7091.0001.0001.0001.0001.0001.0001.0001.0001.000
4호선0.0000.0000.0000.9141.0001.0001.0001.0001.0000.9881.0000.8731.000
5호선0.0000.0000.0000.8041.0000.8730.8731.0000.9881.0001.0001.0000.988
6호선0.0000.0000.0000.6291.0001.0001.0001.0001.0001.0001.0001.0001.000
7호선0.0000.0000.0000.5401.0000.9820.9821.0000.8731.0001.0001.0000.873
8호선0.0000.0000.0000.9141.0001.0001.0001.0001.0000.9881.0000.8731.000
2024-04-30T01:45:01.889326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단위구 분
단위1.0000.490
구 분0.4901.000
2024-04-30T01:45:01.966112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계1호선2호선3호선4호선5호선6호선7호선8호선구 분단위
합계1.0000.8540.9920.9870.9920.9830.9740.9940.9670.0000.559
1호선0.8541.0000.8510.8310.8380.8470.8510.8530.8100.0000.710
2호선0.9920.8511.0000.9750.9780.9660.9650.9840.9550.0000.710
3호선0.9870.8310.9751.0000.9950.9770.9600.9840.9620.0000.559
4호선0.9920.8380.9780.9951.0000.9780.9660.9860.9630.0000.594
5호선0.9830.8470.9660.9770.9781.0000.9890.9930.9650.0000.423
6호선0.9740.8510.9650.9600.9660.9891.0000.9850.9670.0000.564
7호선0.9940.8530.9840.9840.9860.9930.9851.0000.9680.0000.454
8호선0.9670.8100.9550.9620.9630.9650.9670.9681.0000.0000.594
구 분0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.490
단위0.5590.7100.7100.5590.5940.4230.5640.4540.5940.4901.000

Missing values

2024-04-30T01:44:57.825136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:44:57.980448image/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.1단위합계1호선2호선3호선4호선5호선6호선7호선8호선
0변전설비변전소수변전소수개소98.03.015.013.011.019.012.020.05.0
1변전설비변전소수 전개소88.03.015.013.011.014.012.017.03.0
2변전설비정류기실리콘정류기대308.011.058.041.034.057.035.057.015.0
3변전설비정류기용 량kW1053000.044000.0232000.0164000.0136000.0171000.0105000.0171000.030000.0
4변전설비정류용변압기수 량대308.011.058.041.034.057.035.057.015.0
5변전설비정류용변압기용 량kVA1186890.049720.0262160.0183680.0152320.0193230.0118650.0193230.033900.0
6변전설비배전용변압기수 량대195.04.028.026.025.038.024.040.010.0
7변전설비배전용변압기용 량kVA850000.023000.0123400.0138000.096100.0168000.096000.0162500.043000.0
8변전설비차단기소계대2219.067.0377.0307.0253.0419.0258.0431.0107.0
9변전설비차단기고압.특고압대1347.035.0220.0187.0159.0256.0161.0264.065.0
구 분설비명설비명.1단위합계1호선2호선3호선4호선5호선6호선7호선8호선
20전력설비차 단 기소계대2558.0105.0604.0444.0299.0360.0237.0395.0114.0
21전력설비차 단 기역사용대2289.0105.0585.0367.0257.0316.0216.0345.098.0
22전력설비차 단 기터널용대269.00.019.077.042.044.021.050.016.0
23전차선소계km907.921.1192.8129.192.7156.391.7167.756.5
24전차선강 체지하부km580.718.189.777.153.2115.372.4115.139.8
25전차선카테나리지상부km327.23.0103.152.039.541.019.352.616.7
26송배전소계km4432.0110.1818.5593.3462.1833.3484.9803.5326.3
27송배전수전선로(22.9kv) 연장km609.111.775.876.873.8155.651.999.863.7
28송배전연락송전(22.9kv) 연장km995.017.6187.4122.3106.8191.1101.0197.571.3
29송배전고압배전(6.6kv) 연장km2827.980.8555.3394.2281.5486.6332.0506.2191.3