Overview

Dataset statistics

Number of variables12
Number of observations21
Missing cells11
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory110.3 B

Variable types

Categorical2
Text2
Numeric8

Dataset

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

Alerts

1호선 is highly overall correlated with 2호선 and 6 other fieldsHigh correlation
2호선 is highly overall correlated with 1호선 and 6 other fieldsHigh correlation
3호선 is highly overall correlated with 1호선 and 6 other fieldsHigh correlation
4호선 is highly overall correlated with 1호선 and 6 other fieldsHigh correlation
5호선 is highly overall correlated with 1호선 and 6 other fieldsHigh correlation
6호선 is highly overall correlated with 1호선 and 6 other fieldsHigh correlation
7호선 is highly overall correlated with 1호선 and 6 other fieldsHigh correlation
8호선 is highly overall correlated with 1호선 and 7 other fieldsHigh correlation
대분류 is highly overall correlated with 8호선High correlation
소분류 has 3 (14.3%) missing valuesMissing
1호선 has 4 (19.0%) missing valuesMissing
2호선 has 1 (4.8%) missing valuesMissing
3호선 has 1 (4.8%) missing valuesMissing
4호선 has 1 (4.8%) missing valuesMissing
6호선 has 1 (4.8%) missing valuesMissing

Reproduction

Analysis started2024-04-29 16:45:30.830279
Analysis finished2024-04-29 16:45:36.846506
Duration6.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
변전설비
9 
역사전기설비
7 
송배선
3 
전차선
2 

Length

Max length6
Median length4
Mean length4.4285714
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
변전설비 9
42.9%
역사전기설비 7
33.3%
송배선 3
 
14.3%
전차선 2
 
9.5%

Length

2024-04-30T01:45:36.912837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:45:37.011023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
변전설비 9
42.9%
역사전기설비 7
33.3%
송배선 3
 
14.3%
전차선 2
 
9.5%

중분류
Text

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-04-30T01:45:37.142601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.2857143
Min length2

Characters and Unicode

Total characters69
Distinct characters33
Distinct categories3 ?
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 (%)47.6%

Sample

1st row변전소
2nd row변전소
3rd row정류기
4th row변압기
5th row변압기
ValueCountFrequency (%)
변압기 5
23.8%
차단기 4
19.0%
변전소 2
 
9.5%
정류기 1
 
4.8%
원제반 1
 
4.8%
담당역사 1
 
4.8%
역사전기실 1
 
4.8%
본선(터널)전기실 1
 
4.8%
강체 1
 
4.8%
카테나리 1
 
4.8%
Other values (3) 3
14.3%
2024-04-30T01:45:37.402371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 12
17.4%
변 7
 
10.1%
전 6
 
8.7%
압 5
 
7.2%
차 4
 
5.8%
단 4
 
5.8%
역 2
 
2.9%
사 2
 
2.9%
실 2
 
2.9%
소 2
 
2.9%
Other values (23) 23
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67
97.1%
Close Punctuation 1
 
1.4%
Open Punctuation 1
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 12
17.9%
변 7
 
10.4%
전 6
 
9.0%
압 5
 
7.5%
차 4
 
6.0%
단 4
 
6.0%
역 2
 
3.0%
사 2
 
3.0%
실 2
 
3.0%
소 2
 
3.0%
Other values (21) 21
31.3%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67
97.1%
Common 2
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 12
17.9%
변 7
 
10.4%
전 6
 
9.0%
압 5
 
7.5%
차 4
 
6.0%
단 4
 
6.0%
역 2
 
3.0%
사 2
 
3.0%
실 2
 
3.0%
소 2
 
3.0%
Other values (21) 21
31.3%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67
97.1%
ASCII 2
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 12
17.9%
변 7
 
10.4%
전 6
 
9.0%
압 5
 
7.5%
차 4
 
6.0%
단 4
 
6.0%
역 2
 
3.0%
사 2
 
3.0%
실 2
 
3.0%
소 2
 
3.0%
Other values (21) 21
31.3%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

소분류
Text

MISSING 

Distinct17
Distinct (%)94.4%
Missing3
Missing (%)14.3%
Memory size300.0 B
2024-04-30T01:45:37.562470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6
Min length3

Characters and Unicode

Total characters108
Distinct characters47
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

Unique16 ?
Unique (%)88.9%

Sample

1st row수전용
2nd row연락용
3rd row실리콘
4th row정류용(전차선)
5th row배전용(역사)
ValueCountFrequency (%)
22.9kv)연장 2
 
10.5%
실리콘 1
 
5.3%
연락용 1
 
5.3%
지상부 1
 
5.3%
지하부 1
 
5.3%
터널용(vcb 1
 
5.3%
역사용(vcb 1
 
5.3%
터널전기실 1
 
5.3%
역사전기실 1
 
5.3%
수전용 1
 
5.3%
Other values (8) 8
42.1%
2024-04-30T01:45:37.842398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 8
 
7.4%
) 8
 
7.4%
용 7
 
6.5%
전 6
 
5.6%
V 5
 
4.6%
2 4
 
3.7%
연 4
 
3.7%
사 3
 
2.8%
역 3
 
2.8%
고 3
 
2.8%
Other values (37) 57
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64
59.3%
Uppercase Letter 12
 
11.1%
Open Punctuation 8
 
7.4%
Close Punctuation 8
 
7.4%
Decimal Number 8
 
7.4%
Other Punctuation 4
 
3.7%
Lowercase Letter 3
 
2.8%
Space Separator 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 7
 
10.9%
전 6
 
9.4%
연 4
 
6.2%
사 3
 
4.7%
역 3
 
4.7%
고 3
 
4.7%
실 3
 
4.7%
장 3
 
4.7%
지 2
 
3.1%
기 2
 
3.1%
Other values (22) 28
43.8%
Uppercase Letter
ValueCountFrequency (%)
V 5
41.7%
C 2
 
16.7%
B 2
 
16.7%
U 1
 
8.3%
T 1
 
8.3%
R 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
6 2
25.0%
9 2
25.0%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
, 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 3
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64
59.3%
Common 29
26.9%
Latin 15
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 7
 
10.9%
전 6
 
9.4%
연 4
 
6.2%
사 3
 
4.7%
역 3
 
4.7%
고 3
 
4.7%
실 3
 
4.7%
장 3
 
4.7%
지 2
 
3.1%
기 2
 
3.1%
Other values (22) 28
43.8%
Common
ValueCountFrequency (%)
( 8
27.6%
) 8
27.6%
2 4
13.8%
. 3
 
10.3%
6 2
 
6.9%
9 2
 
6.9%
1
 
3.4%
, 1
 
3.4%
Latin
ValueCountFrequency (%)
V 5
33.3%
k 3
20.0%
C 2
 
13.3%
B 2
 
13.3%
U 1
 
6.7%
T 1
 
6.7%
R 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64
59.3%
ASCII 44
40.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 8
18.2%
) 8
18.2%
V 5
11.4%
2 4
9.1%
k 3
 
6.8%
. 3
 
6.8%
6 2
 
4.5%
C 2
 
4.5%
B 2
 
4.5%
9 2
 
4.5%
Other values (5) 5
11.4%
Hangul
ValueCountFrequency (%)
용 7
 
10.9%
전 6
 
9.4%
연 4
 
6.2%
사 3
 
4.7%
역 3
 
4.7%
고 3
 
4.7%
실 3
 
4.7%
장 3
 
4.7%
지 2
 
3.1%
기 2
 
3.1%
Other values (22) 28
43.8%

단위
Categorical

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
대
10 
개소
5 
km
5 
면
 
1

Length

Max length2
Median length1
Mean length1.4761905
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

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

Common Values

ValueCountFrequency (%)
대 10
47.6%
개소 5
23.8%
km 5
23.8%
면 1
 
4.8%

Length

2024-04-30T01:45:37.964908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T01:45:38.080843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대 10
47.6%
개소 5
23.8%
km 5
23.8%
면 1
 
4.8%

1호선
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)76.5%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean26.447059
Minimum3
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-30T01:45:38.192005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q16
median12
Q333
95-th percentile88.88
Maximum120
Range117
Interquartile range (IQR)27

Descriptive statistics

Standard deviation32.552345
Coefficient of variation (CV)1.2308493
Kurtosis3.5032748
Mean26.447059
Median Absolute Deviation (MAD)6.1
Skewness1.9450718
Sum449.6
Variance1059.6551
MonotonicityNot monotonic
2024-04-30T01:45:38.290351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3.0 2
9.5%
12.0 2
9.5%
6.0 2
9.5%
10.0 2
9.5%
44.0 1
 
4.8%
33.0 1
 
4.8%
59.0 1
 
4.8%
120.0 1
 
4.8%
18.1 1
 
4.8%
3.1 1
 
4.8%
Other values (3) 3
14.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
3.0 2
9.5%
3.1 1
4.8%
6.0 2
9.5%
10.0 2
9.5%
11.7 1
4.8%
12.0 2
9.5%
17.6 1
4.8%
18.1 1
4.8%
33.0 1
4.8%
44.0 1
4.8%
ValueCountFrequency (%)
120.0 1
4.8%
81.1 1
4.8%
59.0 1
4.8%
44.0 1
4.8%
33.0 1
4.8%
18.1 1
4.8%
17.6 1
4.8%
12.0 2
9.5%
11.7 1
4.8%
10.0 2
9.5%

2호선
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)80.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean135.035
Minimum15
Maximum593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-30T01:45:38.410860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q125.25
median59
Q3165.35
95-th percentile558.325
Maximum593
Range578
Interquartile range (IQR)140.1

Descriptive statistics

Standard deviation172.43301
Coefficient of variation (CV)1.2769505
Kurtosis2.9096662
Mean135.035
Median Absolute Deviation (MAD)39
Skewness1.9185128
Sum2700.7
Variance29733.141
MonotonicityNot monotonic
2024-04-30T01:45:38.507585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20.0 3
14.3%
59.0 2
 
9.5%
15.0 2
 
9.5%
30.0 1
 
4.8%
221.0 1
 
4.8%
158.0 1
 
4.8%
50.0 1
 
4.8%
56.0 1
 
4.8%
27.0 1
 
4.8%
345.0 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
15.0 2
9.5%
20.0 3
14.3%
27.0 1
 
4.8%
30.0 1
 
4.8%
50.0 1
 
4.8%
56.0 1
 
4.8%
59.0 2
9.5%
76.0 1
 
4.8%
89.7 1
 
4.8%
103.1 1
 
4.8%
ValueCountFrequency (%)
593.0 1
4.8%
556.5 1
4.8%
345.0 1
4.8%
221.0 1
4.8%
187.4 1
4.8%
158.0 1
4.8%
103.1 1
4.8%
89.7 1
4.8%
76.0 1
4.8%
59.0 2
9.5%

3호선
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)90.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean100.715
Minimum13
Maximum397.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-30T01:45:38.619446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile13.95
Q129.75
median46.45
Q3120.6
95-th percentile384.655
Maximum397.1
Range384.1
Interquartile range (IQR)90.85

Descriptive statistics

Standard deviation113.66877
Coefficient of variation (CV)1.1286181
Kurtosis2.8178931
Mean100.715
Median Absolute Deviation (MAD)30.5
Skewness1.8720456
Sum2014.3
Variance12920.59
MonotonicityNot monotonic
2024-04-30T01:45:38.735246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
26.0 2
 
9.5%
41.0 2
 
9.5%
13.0 1
 
4.8%
384.0 1
 
4.8%
397.1 1
 
4.8%
122.4 1
 
4.8%
76.8 1
 
4.8%
51.9 1
 
4.8%
77.1 1
 
4.8%
88.0 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
13.0 1
4.8%
14.0 1
4.8%
26.0 2
9.5%
29.0 1
4.8%
30.0 1
4.8%
34.0 1
4.8%
39.0 1
4.8%
41.0 2
9.5%
51.9 1
4.8%
76.8 1
4.8%
ValueCountFrequency (%)
397.1 1
4.8%
384.0 1
4.8%
217.0 1
4.8%
187.0 1
4.8%
122.4 1
4.8%
120.0 1
4.8%
88.0 1
4.8%
77.1 1
4.8%
76.8 1
4.8%
51.9 1
4.8%

4호선
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)85.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean75.885
Minimum11
Maximum301.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-30T01:45:38.880400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q124.25
median36.4
Q398.825
95-th percentile274.405
Maximum301.1
Range290.1
Interquartile range (IQR)74.575

Descriptive statistics

Standard deviation84.634323
Coefficient of variation (CV)1.1152971
Kurtosis2.4607003
Mean75.885
Median Absolute Deviation (MAD)21.4
Skewness1.7696556
Sum1517.7
Variance7162.9687
MonotonicityNot monotonic
2024-04-30T01:45:38.988936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11.0 2
 
9.5%
15.0 2
 
9.5%
34.0 2
 
9.5%
273.0 1
 
4.8%
301.1 1
 
4.8%
107.3 1
 
4.8%
73.8 1
 
4.8%
38.8 1
 
4.8%
53.7 1
 
4.8%
44.0 1
 
4.8%
Other values (7) 7
33.3%
ValueCountFrequency (%)
11.0 2
9.5%
15.0 2
9.5%
22.0 1
4.8%
25.0 1
4.8%
26.0 1
4.8%
27.0 1
4.8%
34.0 2
9.5%
38.8 1
4.8%
44.0 1
4.8%
53.7 1
4.8%
ValueCountFrequency (%)
301.1 1
4.8%
273.0 1
4.8%
157.0 1
4.8%
153.0 1
4.8%
107.3 1
4.8%
96.0 1
4.8%
73.8 1
4.8%
53.7 1
4.8%
44.0 1
4.8%
38.8 1
4.8%

5호선
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.12381
Minimum5
Maximum554.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-30T01:45:39.093782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile16
Q144
median61
Q3184
95-th percentile419
Maximum554.5
Range549.5
Interquartile range (IQR)140

Descriptive statistics

Standard deviation149.32953
Coefficient of variation (CV)1.0890124
Kurtosis1.9748343
Mean137.12381
Median Absolute Deviation (MAD)39
Skewness1.6016531
Sum2879.6
Variance22299.308
MonotonicityNot monotonic
2024-04-30T01:45:39.209323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
63.0 2
 
9.5%
16.0 1
 
4.8%
348.0 1
 
4.8%
554.5 1
 
4.8%
214.3 1
 
4.8%
171.5 1
 
4.8%
43.3 1
 
4.8%
132.0 1
 
4.8%
60.0 1
 
4.8%
419.0 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
5.0 1
4.8%
16.0 1
4.8%
22.0 1
4.8%
42.0 1
4.8%
43.3 1
4.8%
44.0 1
4.8%
48.0 1
4.8%
49.0 1
4.8%
56.0 1
4.8%
60.0 1
4.8%
ValueCountFrequency (%)
554.5 1
4.8%
419.0 1
4.8%
348.0 1
4.8%
284.0 1
4.8%
214.3 1
4.8%
184.0 1
4.8%
171.5 1
4.8%
132.0 1
4.8%
63.0 2
9.5%
61.0 1
4.8%

6호선
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)75.0%
Missing1
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean77.575
Minimum12
Maximum332.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-30T01:45:39.308070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12.95
Q121
median37
Q398.775
95-th percentile221.81
Maximum332.2
Range320.2
Interquartile range (IQR)77.775

Descriptive statistics

Standard deviation87.433685
Coefficient of variation (CV)1.1270859
Kurtosis2.7425363
Mean77.575
Median Absolute Deviation (MAD)16.6
Skewness1.7915854
Sum1551.5
Variance7644.6493
MonotonicityNot monotonic
2024-04-30T01:45:39.433893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
21.0 3
14.3%
35.0 2
 
9.5%
24.0 2
 
9.5%
39.0 2
 
9.5%
12.0 1
 
4.8%
161.0 1
 
4.8%
98.0 1
 
4.8%
13.0 1
 
4.8%
215.0 1
 
4.8%
216.0 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
12.0 1
 
4.8%
13.0 1
 
4.8%
19.8 1
 
4.8%
21.0 3
14.3%
24.0 2
9.5%
35.0 2
9.5%
39.0 2
9.5%
51.9 1
 
4.8%
72.5 1
 
4.8%
98.0 1
 
4.8%
ValueCountFrequency (%)
332.2 1
4.8%
216.0 1
4.8%
215.0 1
4.8%
161.0 1
4.8%
101.1 1
4.8%
98.0 1
4.8%
72.5 1
4.8%
51.9 1
4.8%
39.0 2
9.5%
35.0 2
9.5%

7호선
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.91429
Minimum3
Maximum415.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-30T01:45:39.583259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile14
Q135
median51
Q3145
95-th percentile270
Maximum415.7
Range412.7
Interquartile range (IQR)110

Descriptive statistics

Standard deviation108.2598
Coefficient of variation (CV)1.0727896
Kurtosis2.3351881
Mean100.91429
Median Absolute Deviation (MAD)32.8
Skewness1.6625402
Sum2119.2
Variance11720.184
MonotonicityNot monotonic
2024-04-30T01:45:40.028595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
51.0 2
 
9.5%
35.0 2
 
9.5%
37.0 2
 
9.5%
14.0 1
 
4.8%
261.0 1
 
4.8%
415.7 1
 
4.8%
166.1 1
 
4.8%
83.8 1
 
4.8%
52.9 1
 
4.8%
93.7 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
3.0 1
4.8%
14.0 1
4.8%
18.0 1
4.8%
34.0 1
4.8%
35.0 2
9.5%
37.0 2
9.5%
42.0 1
4.8%
48.0 1
4.8%
51.0 2
9.5%
52.9 1
4.8%
ValueCountFrequency (%)
415.7 1
4.8%
270.0 1
4.8%
261.0 1
4.8%
226.0 1
4.8%
166.1 1
4.8%
145.0 1
4.8%
93.7 1
4.8%
83.8 1
4.8%
52.9 1
4.8%
51.0 2
9.5%

8호선
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.119048
Minimum2
Maximum192.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-30T01:45:40.149565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q115
median16.2
Q363.7
95-th percentile106
Maximum192.4
Range190.4
Interquartile range (IQR)48.7

Descriptive statistics

Standard deviation46.694
Coefficient of variation (CV)1.163886
Kurtosis4.7426573
Mean40.119048
Median Absolute Deviation (MAD)11.2
Skewness2.0526747
Sum842.5
Variance2180.3296
MonotonicityNot monotonic
2024-04-30T01:45:40.280657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
16.0 3
 
14.3%
15.0 2
 
9.5%
3.0 1
 
4.8%
192.4 1
 
4.8%
71.4 1
 
4.8%
63.7 1
 
4.8%
16.2 1
 
4.8%
39.8 1
 
4.8%
106.0 1
 
4.8%
100.0 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
2.0 1
 
4.8%
3.0 1
 
4.8%
5.0 1
 
4.8%
10.0 1
 
4.8%
11.0 1
 
4.8%
15.0 2
9.5%
16.0 3
14.3%
16.2 1
 
4.8%
18.0 1
 
4.8%
19.0 1
 
4.8%
ValueCountFrequency (%)
192.4 1
4.8%
106.0 1
4.8%
100.0 1
4.8%
71.4 1
4.8%
65.0 1
4.8%
63.7 1
4.8%
42.0 1
4.8%
39.8 1
4.8%
19.0 1
4.8%
18.0 1
4.8%

Interactions

2024-04-30T01:45:35.805213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.182929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.786386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.432240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.091051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.692920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.522273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.205762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.881637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.251868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.858175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.518729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.174122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.762597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.628817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.293697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.957915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.325443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.935754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.606849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.244922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.836052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.716595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.374155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:36.049233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.405892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.018367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.686765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.320847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.923135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.800092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.458962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:36.124902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.477329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.097677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.765223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.394250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.992747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.871615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.534388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:36.197613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.545763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.171252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.835925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.460458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.056805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.943401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.602013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:36.276348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.613739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.253363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.925310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.530977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.135218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.017382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.679201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:36.361546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:31.698991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:32.342032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.003461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:33.608771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:34.432527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.110057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T01:45:35.736206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T01:45:40.404879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류중분류소분류단위1호선2호선3호선4호선5호선6호선7호선8호선
대분류1.0000.8861.0000.8510.2810.6410.5120.3790.8180.5620.5950.730
중분류0.8861.0000.0001.0000.0000.0000.0000.0000.0000.0000.6090.589
소분류1.0000.0001.0001.0000.9790.7950.8370.0001.0000.6560.8831.000
단위0.8511.0001.0001.0000.0000.3640.0000.0000.0000.1870.0000.202
1호선0.2810.0000.9790.0001.0000.9030.9030.9790.9350.9130.9720.911
2호선0.6410.0000.7950.3640.9031.0000.9990.9230.9350.9880.9020.926
3호선0.5120.0000.8370.0000.9030.9991.0000.9470.9220.9830.8800.912
4호선0.3790.0000.0000.0000.9790.9230.9471.0000.9170.8730.9430.820
5호선0.8180.0001.0000.0000.9350.9350.9220.9171.0000.9570.9490.940
6호선0.5620.0000.6560.1870.9130.9880.9830.8730.9571.0000.9620.983
7호선0.5950.6090.8830.0000.9720.9020.8800.9430.9490.9621.0000.926
8호선0.7300.5891.0000.2020.9110.9260.9120.8200.9400.9830.9261.000
2024-04-30T01:45:40.535342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단위대분류
단위1.0000.499
대분류0.4991.000
2024-04-30T01:45:40.644933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1호선2호선3호선4호선5호선6호선7호선8호선대분류단위
1호선1.0000.8930.9250.9130.9670.9530.9240.8830.0580.000
2호선0.8931.0000.8950.9320.8790.8860.9720.8760.4250.191
3호선0.9250.8951.0000.9790.9280.8400.9480.9050.3100.000
4호선0.9130.9320.9791.0000.9240.8770.9560.8940.2040.000
5호선0.9670.8790.9280.9241.0000.9450.9470.9140.4070.000
6호선0.9530.8860.8400.8770.9451.0000.8980.8870.3530.000
7호선0.9240.9720.9480.9560.9470.8981.0000.9370.3960.000
8호선0.8830.8760.9050.8940.9140.8870.9371.0000.5220.039
대분류0.0580.4250.3100.2040.4070.3530.3960.5221.0000.499
단위0.0000.1910.0000.0000.0000.0000.0000.0390.4991.000

Missing values

2024-04-30T01:45:36.492739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T01:45:36.630388image/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.
2024-04-30T01:45:36.772913image/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

대분류중분류소분류단위1호선2호선3호선4호선5호선6호선7호선8호선
0변전설비변전소수전용개소3.015.013.011.016.012.014.03.0
1변전설비변전소연락용개소<NA><NA><NA><NA>5.0<NA>3.02.0
2변전설비정류기실리콘대12.059.041.034.063.035.051.015.0
3변전설비변압기정류용(전차선)대12.059.041.034.063.035.051.015.0
4변전설비변압기배전용(역사)대6.027.026.025.042.024.034.010.0
5변전설비변압기소내용대6.030.026.022.044.024.035.011.0
6변전설비차단기고압, 특고압대44.0221.0187.0157.0284.0161.0226.065.0
7변전설비차단기직류고속도대33.0158.0120.096.0184.098.0145.042.0
8변전설비원제반RTU(변전소)면3.015.014.011.022.013.018.05.0
9역사전기설비담당역사<NA>개소10.050.034.026.056.039.042.018.0
대분류중분류소분류단위1호선2호선3호선4호선5호선6호선7호선8호선
11역사전기설비본선(터널)전기실<NA>개소<NA>20.029.015.048.021.037.016.0
12역사전기설비변압기역사전기실대59.0345.0217.0153.0348.0215.0261.0100.0
13역사전기설비변압기터널전기실대<NA>20.030.015.049.021.035.016.0
14역사전기설비차단기역사용(VCB)대120.0593.0384.0273.0419.0216.0270.0106.0
15역사전기설비차단기터널용(VCB)대<NA>20.088.044.060.021.037.016.0
16전차선강체지하부km18.189.777.153.7132.072.593.739.8
17전차선카테나리지상부km3.1103.151.938.843.319.852.916.2
18송배선수전(22.9kV)연장km11.776.076.873.8171.551.983.863.7
19송배선연락(22.9kV)연장km17.6187.4122.4107.3214.3101.1166.171.4
20송배선배전(6.6kV)연장km81.1556.5397.1301.1554.5332.2415.7192.4