Overview

Dataset statistics

Number of variables10
Number of observations36
Missing cells34
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory87.7 B

Variable types

Numeric4
Text3
Categorical3

Dataset

Description인천광역시 지하차도(시설물명, 시설물 길이, 폭 , 준공년도, 형식, 설계하중, 관리주체 등 )에 관한 현황 자료입니다.
Author인천광역시
URLhttps://www.data.go.kr/data/15045183/fileData.do

Alerts

구분 is highly overall correlated with 규모(미터)_폭 and 3 other fieldsHigh correlation
규모(미터)_폭 is highly overall correlated with 구분High correlation
준공년도 is highly overall correlated with 구분High correlation
위치(군구) is highly overall correlated with 구분 and 1 other fieldsHigh correlation
관리주체 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
규모(미터)_길이 has 4 (11.1%) missing valuesMissing
규모(미터)_폭 has 2 (5.6%) missing valuesMissing
비고 has 28 (77.8%) missing valuesMissing
구분 has unique valuesUnique
시설물명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:06:40.271324
Analysis finished2023-12-11 23:06:42.815141
Duration2.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T08:06:42.883160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.75
Q19.75
median18.5
Q327.25
95-th percentile34.25
Maximum36
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.5694948
Kurtosis-1.2
Mean18.5
Median Absolute Deviation (MAD)9
Skewness0
Sum666
Variance111
MonotonicityStrictly increasing
2023-12-12T08:06:43.021320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 1
 
2.8%
20 1
 
2.8%
22 1
 
2.8%
23 1
 
2.8%
24 1
 
2.8%
25 1
 
2.8%
26 1
 
2.8%
27 1
 
2.8%
28 1
 
2.8%
29 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1 1
2.8%
2 1
2.8%
3 1
2.8%
4 1
2.8%
5 1
2.8%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
ValueCountFrequency (%)
36 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
32 1
2.8%
31 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%
27 1
2.8%

시설물명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T08:06:43.238706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.1388889
Min length3

Characters and Unicode

Total characters221
Distinct characters67
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

Unique36 ?
Unique (%)100.0%

Sample

1st row삼목지하차도
2nd row고속종점지하차도
3rd row석암지하차도
4th row용현지하차도
5th row문학지하차도
ValueCountFrequency (%)
삼목지하차도 1
 
2.6%
동춘지하차도 1
 
2.6%
호수공원1 1
 
2.6%
호수공원2 1
 
2.6%
석 1
 
2.6%
남 1
 
2.6%
왕 1
 
2.6%
길 1
 
2.6%
루원지하차도 1
 
2.6%
고잔지하차도 1
 
2.6%
Other values (29) 29
74.4%
2023-12-12T08:06:43.565836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 31
14.0%
지 30
 
13.6%
하 30
 
13.6%
차 30
 
13.6%
수 5
 
2.3%
원 4
 
1.8%
학 4
 
1.8%
나 4
 
1.8%
래 4
 
1.8%
3
 
1.4%
Other values (57) 76
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 216
97.7%
Space Separator 3
 
1.4%
Decimal Number 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 31
14.4%
지 30
13.9%
하 30
13.9%
차 30
13.9%
수 5
 
2.3%
원 4
 
1.9%
학 4
 
1.9%
나 4
 
1.9%
래 4
 
1.9%
석 3
 
1.4%
Other values (54) 71
32.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 216
97.7%
Common 5
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 31
14.4%
지 30
13.9%
하 30
13.9%
차 30
13.9%
수 5
 
2.3%
원 4
 
1.9%
학 4
 
1.9%
나 4
 
1.9%
래 4
 
1.9%
석 3
 
1.4%
Other values (54) 71
32.9%
Common
ValueCountFrequency (%)
3
60.0%
1 1
 
20.0%
2 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 216
97.7%
ASCII 5
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 31
14.4%
지 30
13.9%
하 30
13.9%
차 30
13.9%
수 5
 
2.3%
원 4
 
1.9%
학 4
 
1.9%
나 4
 
1.9%
래 4
 
1.9%
석 3
 
1.4%
Other values (54) 71
32.9%
ASCII
ValueCountFrequency (%)
3
60.0%
1 1
 
20.0%
2 1
 
20.0%

위치(군구)
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size420.0 B
서구
8 
연수구
7 
중구
5 
남동구
5 
미추홀구
4 
Other values (2)
7 

Length

Max length4
Median length3
Mean length2.75
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중구
2nd row미추홀구
3rd row미추홀구
4th row미추홀구
5th row미추홀구

Common Values

ValueCountFrequency (%)
서구 8
22.2%
연수구 7
19.4%
중구 5
13.9%
남동구 5
13.9%
미추홀구 4
11.1%
부평구 4
11.1%
계양구 3
 
8.3%

Length

2023-12-12T08:06:43.718160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:06:43.832931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 8
22.2%
연수구 7
19.4%
중구 5
13.9%
남동구 5
13.9%
미추홀구 4
11.1%
부평구 4
11.1%
계양구 3
 
8.3%

도로명 주소 또는 지번
Text

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T08:06:44.031019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16.5
Mean length13.194444
Min length6

Characters and Unicode

Total characters475
Distinct characters83
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)88.9%

Sample

1st row운서동 2819
2nd row아암대로(용현동 575-1번지)
3rd row주안로(주안동 969-1번지)
4th row인주대로(용현동 150-1번지)
5th row매소홀로(학익동 665-1번지)
ValueCountFrequency (%)
일원 6
 
7.9%
하늘대로(운남동 4
 
5.3%
청라2동 2
 
2.6%
고잔동 2
 
2.6%
동춘동 2
 
2.6%
서구 2
 
2.6%
1780 2
 
2.6%
1793 2
 
2.6%
용종동 1
 
1.3%
977번지 1
 
1.3%
Other values (52) 52
68.4%
2023-12-12T08:06:44.354861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
8.4%
동 37
 
7.8%
1 35
 
7.4%
로 22
 
4.6%
) 20
 
4.2%
( 20
 
4.2%
- 17
 
3.6%
번 16
 
3.4%
5 16
 
3.4%
9 14
 
2.9%
Other values (73) 238
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 238
50.1%
Decimal Number 139
29.3%
Space Separator 40
 
8.4%
Close Punctuation 20
 
4.2%
Open Punctuation 20
 
4.2%
Dash Punctuation 17
 
3.6%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 37
 
15.5%
로 22
 
9.2%
번 16
 
6.7%
지 14
 
5.9%
대 12
 
5.0%
원 7
 
2.9%
일 7
 
2.9%
청 6
 
2.5%
학 5
 
2.1%
산 5
 
2.1%
Other values (58) 107
45.0%
Decimal Number
ValueCountFrequency (%)
1 35
25.2%
5 16
11.5%
9 14
 
10.1%
6 13
 
9.4%
3 13
 
9.4%
2 13
 
9.4%
8 11
 
7.9%
7 10
 
7.2%
0 8
 
5.8%
4 6
 
4.3%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 238
50.1%
Common 237
49.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 37
 
15.5%
로 22
 
9.2%
번 16
 
6.7%
지 14
 
5.9%
대 12
 
5.0%
원 7
 
2.9%
일 7
 
2.9%
청 6
 
2.5%
학 5
 
2.1%
산 5
 
2.1%
Other values (58) 107
45.0%
Common
ValueCountFrequency (%)
40
16.9%
1 35
14.8%
) 20
8.4%
( 20
8.4%
- 17
7.2%
5 16
 
6.8%
9 14
 
5.9%
6 13
 
5.5%
3 13
 
5.5%
2 13
 
5.5%
Other values (5) 36
15.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 238
50.1%
ASCII 237
49.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
16.9%
1 35
14.8%
) 20
8.4%
( 20
8.4%
- 17
7.2%
5 16
 
6.8%
9 14
 
5.9%
6 13
 
5.5%
3 13
 
5.5%
2 13
 
5.5%
Other values (5) 36
15.2%
Hangul
ValueCountFrequency (%)
동 37
 
15.5%
로 22
 
9.2%
번 16
 
6.7%
지 14
 
5.9%
대 12
 
5.0%
원 7
 
2.9%
일 7
 
2.9%
청 6
 
2.5%
학 5
 
2.1%
산 5
 
2.1%
Other values (58) 107
45.0%

규모(미터)_길이
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)90.6%
Missing4
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean529.32812
Minimum49
Maximum1932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T08:06:44.504623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile77.75
Q1315.5
median463.5
Q3622.5
95-th percentile1260.75
Maximum1932
Range1883
Interquartile range (IQR)307

Descriptive statistics

Standard deviation387.52287
Coefficient of variation (CV)0.73210331
Kurtosis4.7870748
Mean529.32812
Median Absolute Deviation (MAD)157
Skewness1.860078
Sum16938.5
Variance150173.98
MonotonicityNot monotonic
2023-12-12T08:06:44.609849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
200.0 3
 
8.3%
610.0 2
 
5.6%
500.0 1
 
2.8%
1245.0 1
 
2.8%
530.0 1
 
2.8%
765.0 1
 
2.8%
1932.0 1
 
2.8%
98.0 1
 
2.8%
210.0 1
 
2.8%
1280.0 1
 
2.8%
Other values (19) 19
52.8%
(Missing) 4
 
11.1%
ValueCountFrequency (%)
49.0 1
 
2.8%
53.0 1
 
2.8%
98.0 1
 
2.8%
200.0 3
8.3%
210.0 1
 
2.8%
296.0 1
 
2.8%
322.0 1
 
2.8%
340.0 1
 
2.8%
351.0 1
 
2.8%
380.0 1
 
2.8%
ValueCountFrequency (%)
1932.0 1
2.8%
1280.0 1
2.8%
1245.0 1
2.8%
846.5 1
2.8%
824.5 1
2.8%
765.0 1
2.8%
690.0 1
2.8%
660.0 1
2.8%
610.0 2
5.6%
580.0 1
2.8%

규모(미터)_폭
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)70.6%
Missing2
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean21.661765
Minimum8.6
Maximum39.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T08:06:44.722271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.6
5-th percentile13.25
Q116.5
median20.65
Q325.6
95-th percentile32.555
Maximum39.3
Range30.7
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.7512519
Coefficient of variation (CV)0.31166676
Kurtosis0.25824598
Mean21.661765
Median Absolute Deviation (MAD)4.65
Skewness0.50146885
Sum736.5
Variance45.579403
MonotonicityNot monotonic
2023-12-12T08:06:44.826674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
16.0 4
 
11.1%
26.9 2
 
5.6%
24.0 2
 
5.6%
17.0 2
 
5.6%
32.1 2
 
5.6%
19.6 2
 
5.6%
16.5 2
 
5.6%
25.6 2
 
5.6%
39.3 1
 
2.8%
33.4 1
 
2.8%
Other values (14) 14
38.9%
(Missing) 2
 
5.6%
ValueCountFrequency (%)
8.6 1
 
2.8%
10.0 1
 
2.8%
15.0 1
 
2.8%
15.5 1
 
2.8%
16.0 4
11.1%
16.5 2
5.6%
17.0 2
5.6%
18.5 1
 
2.8%
19.2 1
 
2.8%
19.6 2
5.6%
ValueCountFrequency (%)
39.3 1
2.8%
33.4 1
2.8%
32.1 2
5.6%
28.5 1
2.8%
27.8 1
2.8%
26.9 2
5.6%
25.6 2
5.6%
25.0 1
2.8%
24.0 2
5.6%
23.5 1
2.8%

준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.5556
Minimum1979
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T08:06:44.931194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1979
5-th percentile1988.5
Q11996.75
median2004.5
Q32012.25
95-th percentile2014.25
Maximum2017
Range38
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.9468428
Coefficient of variation (CV)0.0049645955
Kurtosis-0.31895189
Mean2003.5556
Median Absolute Deviation (MAD)8
Skewness-0.64472406
Sum72128
Variance98.939683
MonotonicityNot monotonic
2023-12-12T08:06:45.057429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2013 4
 
11.1%
2010 4
 
11.1%
2012 3
 
8.3%
1998 3
 
8.3%
2014 3
 
8.3%
1997 2
 
5.6%
1993 2
 
5.6%
1995 2
 
5.6%
1979 1
 
2.8%
2011 1
 
2.8%
Other values (11) 11
30.6%
ValueCountFrequency (%)
1979 1
 
2.8%
1981 1
 
2.8%
1991 1
 
2.8%
1992 1
 
2.8%
1993 2
5.6%
1995 2
5.6%
1996 1
 
2.8%
1997 2
5.6%
1998 3
8.3%
2000 1
 
2.8%
ValueCountFrequency (%)
2017 1
 
2.8%
2015 1
 
2.8%
2014 3
8.3%
2013 4
11.1%
2012 3
8.3%
2011 1
 
2.8%
2010 4
11.1%
2005 1
 
2.8%
2004 1
 
2.8%
2002 1
 
2.8%

관리주체
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
종합건설본부
6 
인천시설공단
6 
연수구청
5 
미추홀구 건설과
4 
부평구 도로과
4 
Other values (5)
11 

Length

Max length8
Median length7
Mean length5.9722222
Min length3

Unique

Unique2 ?
Unique (%)5.6%

Sample

1st row중구 기반시설과
2nd row미추홀구 건설과
3rd row미추홀구 건설과
4th row미추홀구 건설과
5th row미추홀구 건설과

Common Values

ValueCountFrequency (%)
종합건설본부 6
16.7%
인천시설공단 6
16.7%
연수구청 5
13.9%
미추홀구 건설과 4
11.1%
부평구 도로과 4
11.1%
서구 도로과 4
11.1%
남동구 3
8.3%
계양구 건설과 2
 
5.6%
중구 기반시설과 1
 
2.8%
계양구청 건설과 1
 
2.8%

Length

2023-12-12T08:06:45.198125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:06:45.567564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로과 8
15.4%
건설과 7
13.5%
종합건설본부 6
11.5%
인천시설공단 6
11.5%
연수구청 5
9.6%
미추홀구 4
7.7%
부평구 4
7.7%
서구 4
7.7%
남동구 3
 
5.8%
계양구 2
 
3.8%
Other values (3) 3
 
5.8%

1_2종 시설물
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
2종
24 
3종
5 
1종
4 
일반
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반
2nd row2종
3rd row2종
4th row3종
5th row3종

Common Values

ValueCountFrequency (%)
2종 24
66.7%
3종 5
 
13.9%
1종 4
 
11.1%
일반 3
 
8.3%

Length

2023-12-12T08:06:45.709159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:06:45.805145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2종 24
66.7%
3종 5
 
13.9%
1종 4
 
11.1%
일반 3
 
8.3%

비고
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing28
Missing (%)77.8%
Memory size420.0 B
2023-12-12T08:06:45.964607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length86
Median length54
Mean length37.25
Min length18

Characters and Unicode

Total characters298
Distinct characters38
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st row규모(미터_폭) : 24(12/12)
2nd row규모(미터_폭) : 16(8/8)
3rd row규모(미터_폭) : 17(8.5/8.5)
4th row규모(미터_폭) : 8.6(4.3/4.3)
5th row규모(미터_길이) : U type 235.5BOX 140 /U type 235.5BOX 140 , 규모(미터_폭) : 17통과H 4.1 /17통과H 4.1
ValueCountFrequency (%)
11
21.6%
규모(미터_폭 6
11.8%
규모(미터_길이 4
 
7.8%
u 4
 
7.8%
type 4
 
7.8%
140 4
 
7.8%
17통과h 2
 
3.9%
4.5 2
 
3.9%
29.4통과h 2
 
3.9%
420box 2
 
3.9%
Other values (8) 10
19.6%
2023-12-12T08:06:46.262350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43
 
14.4%
( 16
 
5.4%
4 16
 
5.4%
) 16
 
5.4%
1 15
 
5.0%
. 13
 
4.4%
모 10
 
3.4%
2 10
 
3.4%
규 10
 
3.4%
_ 10
 
3.4%
Other values (28) 139
46.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78
26.2%
Other Letter 66
22.1%
Space Separator 43
14.4%
Other Punctuation 33
11.1%
Uppercase Letter 20
 
6.7%
Open Punctuation 16
 
5.4%
Close Punctuation 16
 
5.4%
Lowercase Letter 16
 
5.4%
Connector Punctuation 10
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
모 10
15.2%
규 10
15.2%
터 10
15.2%
미 10
15.2%
폭 6
9.1%
과 4
 
6.1%
이 4
 
6.1%
길 4
 
6.1%
통 4
 
6.1%
박 2
 
3.0%
Decimal Number
ValueCountFrequency (%)
4 16
20.5%
1 15
19.2%
2 10
12.8%
5 9
11.5%
0 8
10.3%
8 7
9.0%
3 5
 
6.4%
9 3
 
3.8%
7 3
 
3.8%
6 2
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
O 4
20.0%
U 4
20.0%
B 4
20.0%
X 4
20.0%
H 4
20.0%
Other Punctuation
ValueCountFrequency (%)
. 13
39.4%
: 10
30.3%
/ 8
24.2%
, 2
 
6.1%
Lowercase Letter
ValueCountFrequency (%)
t 4
25.0%
y 4
25.0%
p 4
25.0%
e 4
25.0%
Space Separator
ValueCountFrequency (%)
43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 196
65.8%
Hangul 66
 
22.1%
Latin 36
 
12.1%

Most frequent character per script

Common
ValueCountFrequency (%)
43
21.9%
( 16
 
8.2%
4 16
 
8.2%
) 16
 
8.2%
1 15
 
7.7%
. 13
 
6.6%
2 10
 
5.1%
_ 10
 
5.1%
: 10
 
5.1%
5 9
 
4.6%
Other values (8) 38
19.4%
Hangul
ValueCountFrequency (%)
모 10
15.2%
규 10
15.2%
터 10
15.2%
미 10
15.2%
폭 6
9.1%
과 4
 
6.1%
이 4
 
6.1%
길 4
 
6.1%
통 4
 
6.1%
박 2
 
3.0%
Latin
ValueCountFrequency (%)
O 4
11.1%
U 4
11.1%
t 4
11.1%
y 4
11.1%
p 4
11.1%
e 4
11.1%
B 4
11.1%
X 4
11.1%
H 4
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 232
77.9%
Hangul 66
 
22.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43
18.5%
( 16
 
6.9%
4 16
 
6.9%
) 16
 
6.9%
1 15
 
6.5%
. 13
 
5.6%
2 10
 
4.3%
_ 10
 
4.3%
: 10
 
4.3%
5 9
 
3.9%
Other values (17) 74
31.9%
Hangul
ValueCountFrequency (%)
모 10
15.2%
규 10
15.2%
터 10
15.2%
미 10
15.2%
폭 6
9.1%
과 4
 
6.1%
이 4
 
6.1%
길 4
 
6.1%
통 4
 
6.1%
박 2
 
3.0%

Interactions

2023-12-12T08:06:42.085073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:40.710020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.142911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.590922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:42.173660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:40.796094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.237465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.708447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:42.257495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:40.928906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.336585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.824011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:42.347719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.041732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.454156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:06:41.958968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:06:46.370858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시설물명위치(군구)도로명 주소 또는 지번규모(미터)_길이규모(미터)_폭준공년도관리주체1_2종 시설물비고
구분1.0001.0000.8691.0000.3530.0000.6470.9390.0001.000
시설물명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치(군구)0.8691.0001.0001.0000.5620.5430.5910.9510.3301.000
도로명 주소 또는 지번1.0001.0001.0001.0000.5220.7881.0001.0000.8661.000
규모(미터)_길이0.3531.0000.5620.5221.0000.7030.0000.5590.6671.000
규모(미터)_폭0.0001.0000.5430.7880.7031.0000.0000.3280.4991.000
준공년도0.6471.0000.5911.0000.0000.0001.0000.7160.2531.000
관리주체0.9391.0000.9511.0000.5590.3280.7161.0000.6701.000
1_2종 시설물0.0001.0000.3300.8660.6670.4990.2530.6701.0001.000
비고1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T08:06:46.497983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리주체위치(군구)1_2종 시설물
관리주체1.0000.8270.417
위치(군구)0.8271.0000.210
1_2종 시설물0.4170.2101.000
2023-12-12T08:06:46.582293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분규모(미터)_길이규모(미터)_폭준공년도위치(군구)관리주체1_2종 시설물
구분1.0000.2350.6070.6910.6270.5600.000
규모(미터)_길이0.2351.0000.1060.4470.2080.2880.496
규모(미터)_폭0.6070.1061.0000.4260.3000.1130.301
준공년도0.6910.4470.4261.0000.2320.4240.193
위치(군구)0.6270.2080.3000.2321.0000.8270.210
관리주체0.5600.2880.1130.4240.8271.0000.417
1_2종 시설물0.0000.4960.3010.1930.2100.4171.000

Missing values

2023-12-12T08:06:42.471206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:06:42.636701image/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.
2023-12-12T08:06:42.745803image/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

구분시설물명위치(군구)도로명 주소 또는 지번규모(미터)_길이규모(미터)_폭준공년도관리주체1_2종 시설물비고
01삼목지하차도중구운서동 2819401.018.52000중구 기반시설과일반<NA>
12고속종점지하차도미추홀구아암대로(용현동 575-1번지)500.016.51996미추홀구 건설과2종<NA>
23석암지하차도미추홀구주안로(주안동 969-1번지)424.515.01991미추홀구 건설과2종<NA>
34용현지하차도미추홀구인주대로(용현동 150-1번지)340.022.01992미추홀구 건설과3종<NA>
45문학지하차도미추홀구매소홀로(학익동 665-1번지)420.015.51997미추홀구 건설과3종<NA>
56선학지하차도연수구경원대로(선학동 384)690.024.01998연수구청2종규모(미터_폭) : 24(12/12)
67옥련터널연수구독배로(옥련동 산 21-11번지)351.016.02001연수구청2종규모(미터_폭) : 16(8/8)
78청학지하차도연수구비류대로(청학동241)474.017.01993연수구청2종규모(미터_폭) : 17(8.5/8.5)
89용담지하차도연수구함박뫼로(청학동 466)296.08.61993연수구청2종규모(미터_폭) : 8.6(4.3/4.3)
910옹암지하차도연수구비류대로(옥련동산2-35번지 일원)660.019.22017연수구청2종<NA>
구분시설물명위치(군구)도로명 주소 또는 지번규모(미터)_길이규모(미터)_폭준공년도관리주체1_2종 시설물비고
2627송도지하차도연수구동춘동 916번지 일원200.021.52010종합건설본부2종<NA>
2728고잔지하차도남동구고잔동 977번지 일원200.032.12010종합건설본부2종<NA>
2829해안지하차도남동구고잔동 795번지 일원200.032.12010종합건설본부2종<NA>
2930봉수지하차도서구가정동 631번지 일원98.023.52014종합건설본부일반<NA>
3031중봉지하차도서구중봉대로(청라동115-8,9)1932.020.02011인천시설공단1종<NA>
3132청라지하차도서구봉오대로(청라동 101-11)610.024.02014인천시설공단2종<NA>
3233해찬나래지하차도중구하늘대로(운남동 1793)765.025.02013인천시설공단2종<NA>
3334푸른나래지하차도중구하늘대로(운남동 1780)530.026.92013인천시설공단2종<NA>
3435두빛나래지하차도중구하늘대로(운남동 1780)1245.033.42013인천시설공단1종<NA>
3536그린나래지하차도중구하늘대로(운남동 1793)610.026.92013인천시설공단2종<NA>