Overview

Dataset statistics

Number of variables12
Number of observations254
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.7 KiB
Average record size in memory103.5 B

Variable types

Categorical1
Text4
Numeric7

Dataset

Description충청남도 공주시 도로현황에 대한 데이터로 (종류, 노선명, 노선번호, 기점, 종점, 기존연장, 총연장) 등의 항목을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=441&beforeMenuCd=DOM_000000201001001000&publicdatapk=3084487

Alerts

기존연장 is highly overall correlated with 총연장 and 2 other fieldsHigh correlation
총연장 is highly overall correlated with 기존연장 and 2 other fieldsHigh correlation
소계 is highly overall correlated with 기존연장 and 2 other fieldsHigh correlation
포장 is highly overall correlated with 기존연장 and 2 other fieldsHigh correlation
미포장 is highly overall correlated with 포장율(%)High correlation
포장율(%) is highly overall correlated with 미포장High correlation
노선번호 has unique valuesUnique
증,감 has 243 (95.7%) zerosZeros
포장 has 10 (3.9%) zerosZeros
미포장 has 188 (74.0%) zerosZeros
포장율(%) has 10 (3.9%) zerosZeros

Reproduction

Analysis started2024-01-09 20:12:58.571850
Analysis finished2024-01-09 20:13:04.884417
Duration6.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

종류
Categorical

Distinct5
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
리도
107 
농도
95 
시도
29 
면도
17 
시의시도
 
6

Length

Max length4
Median length2
Mean length2.0472441
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
리도 107
42.1%
농도 95
37.4%
시도 29
 
11.4%
면도 17
 
6.7%
시의시도 6
 
2.4%

Length

2024-01-10T05:13:04.978117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:13:05.122394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
리도 107
42.1%
농도 95
37.4%
시도 29
 
11.4%
면도 17
 
6.7%
시의시도 6
 
2.4%

노선명
Text

Distinct250
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-10T05:13:05.478273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2913386
Min length3

Characters and Unicode

Total characters836
Distinct characters171
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

Unique246 ?
Unique (%)96.9%

Sample

1st row온천~학봉
2nd row월암~하신
3rd row경천~양화
4th row청룡~오인
5th row수촌~도신
ValueCountFrequency (%)
자동선 2
 
0.8%
대성선 2
 
0.8%
신명선 2
 
0.8%
장덕선 2
 
0.8%
조당선 1
 
0.4%
석녹선 1
 
0.4%
온천~학봉 1
 
0.4%
삼놋선 1
 
0.4%
만수선 1
 
0.4%
정산선 1
 
0.4%
Other values (240) 240
94.5%
2024-01-10T05:13:06.077743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
선 220
26.3%
~ 35
 
4.2%
신 21
 
2.5%
대 20
 
2.4%
동 17
 
2.0%
화 15
 
1.8%
구 14
 
1.7%
성 13
 
1.6%
산 12
 
1.4%
수 11
 
1.3%
Other values (161) 458
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 799
95.6%
Math Symbol 35
 
4.2%
Decimal Number 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 220
27.5%
신 21
 
2.6%
대 20
 
2.5%
동 17
 
2.1%
화 15
 
1.9%
구 14
 
1.8%
성 13
 
1.6%
산 12
 
1.5%
수 11
 
1.4%
가 11
 
1.4%
Other values (158) 445
55.7%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Math Symbol
ValueCountFrequency (%)
~ 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 799
95.6%
Common 37
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 220
27.5%
신 21
 
2.6%
대 20
 
2.5%
동 17
 
2.1%
화 15
 
1.9%
구 14
 
1.8%
성 13
 
1.6%
산 12
 
1.5%
수 11
 
1.4%
가 11
 
1.4%
Other values (158) 445
55.7%
Common
ValueCountFrequency (%)
~ 35
94.6%
1 1
 
2.7%
2 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 799
95.6%
ASCII 37
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
선 220
27.5%
신 21
 
2.6%
대 20
 
2.5%
동 17
 
2.1%
화 15
 
1.9%
구 14
 
1.8%
성 13
 
1.6%
산 12
 
1.5%
수 11
 
1.4%
가 11
 
1.4%
Other values (158) 445
55.7%
ASCII
ValueCountFrequency (%)
~ 35
94.6%
1 1
 
2.7%
2 1
 
2.7%

노선번호
Text

UNIQUE 

Distinct254
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-10T05:13:06.487361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters1270
Distinct characters39
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)100.0%

Sample

1st row시도1호
2nd row시도2호
3rd row시도4호
4th row시도7호
5th row시도8호
ValueCountFrequency (%)
시도1호 1
 
0.4%
이인306 1
 
0.4%
이인308 1
 
0.4%
유구303 1
 
0.4%
유구304 1
 
0.4%
유구305 1
 
0.4%
유구306 1
 
0.4%
유구307 1
 
0.4%
유구308 1
 
0.4%
유구309 1
 
0.4%
Other values (244) 244
96.1%
2024-01-10T05:13:07.101382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 187
 
14.7%
2 154
 
12.1%
3 134
 
10.6%
1 113
 
8.9%
시 41
 
3.2%
호 35
 
2.8%
도 35
 
2.8%
우 31
 
2.4%
성 31
 
2.4%
룡 26
 
2.0%
Other values (29) 483
38.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 715
56.3%
Other Letter 555
43.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 41
 
7.4%
호 35
 
6.3%
도 35
 
6.3%
우 31
 
5.6%
성 31
 
5.6%
룡 26
 
4.7%
의 26
 
4.7%
계 26
 
4.7%
정 25
 
4.5%
안 25
 
4.5%
Other values (19) 254
45.8%
Decimal Number
ValueCountFrequency (%)
0 187
26.2%
2 154
21.5%
3 134
18.7%
1 113
15.8%
4 26
 
3.6%
5 23
 
3.2%
8 22
 
3.1%
7 20
 
2.8%
6 20
 
2.8%
9 16
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 715
56.3%
Hangul 555
43.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 41
 
7.4%
호 35
 
6.3%
도 35
 
6.3%
우 31
 
5.6%
성 31
 
5.6%
룡 26
 
4.7%
의 26
 
4.7%
계 26
 
4.7%
정 25
 
4.5%
안 25
 
4.5%
Other values (19) 254
45.8%
Common
ValueCountFrequency (%)
0 187
26.2%
2 154
21.5%
3 134
18.7%
1 113
15.8%
4 26
 
3.6%
5 23
 
3.2%
8 22
 
3.1%
7 20
 
2.8%
6 20
 
2.8%
9 16
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 715
56.3%
Hangul 555
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 187
26.2%
2 154
21.5%
3 134
18.7%
1 113
15.8%
4 26
 
3.6%
5 23
 
3.2%
8 22
 
3.1%
7 20
 
2.8%
6 20
 
2.8%
9 16
 
2.2%
Hangul
ValueCountFrequency (%)
시 41
 
7.4%
호 35
 
6.3%
도 35
 
6.3%
우 31
 
5.6%
성 31
 
5.6%
룡 26
 
4.7%
의 26
 
4.7%
계 26
 
4.7%
정 25
 
4.5%
안 25
 
4.5%
Other values (19) 254
45.8%

기점
Text

Distinct253
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-10T05:13:07.541599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length21.224409
Min length16

Characters and Unicode

Total characters5391
Distinct characters127
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

Unique252 ?
Unique (%)99.2%

Sample

1st row충청남도 공주시 반포면 온천리 311-6
2nd row충청남도 공주시 계룡면 유평리 262-1
3rd row충청남도 공주시 계룡면 경천리 62-8
4th row충청남도 공주시 의당면 청룡리 665-2
5th row충청남도 공주시 의당면 수촌리 29-3
ValueCountFrequency (%)
충청남도 254
20.3%
공주시 254
20.3%
우성면 36
 
2.9%
정안면 29
 
2.3%
계룡면 28
 
2.2%
유구읍 26
 
2.1%
탄천면 25
 
2.0%
의당면 23
 
1.8%
이인면 22
 
1.8%
신풍면 18
 
1.4%
Other values (375) 538
42.9%
2024-01-10T05:13:08.262959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
999
18.5%
청 259
 
4.8%
주 258
 
4.8%
남 257
 
4.8%
공 257
 
4.8%
도 255
 
4.7%
시 254
 
4.7%
충 254
 
4.7%
리 241
 
4.5%
면 212
 
3.9%
Other values (117) 2145
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3255
60.4%
Space Separator 999
 
18.5%
Decimal Number 927
 
17.2%
Dash Punctuation 210
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 259
 
8.0%
주 258
 
7.9%
남 257
 
7.9%
공 257
 
7.9%
도 255
 
7.8%
시 254
 
7.8%
충 254
 
7.8%
리 241
 
7.4%
면 212
 
6.5%
성 47
 
1.4%
Other values (105) 961
29.5%
Decimal Number
ValueCountFrequency (%)
1 172
18.6%
2 151
16.3%
3 130
14.0%
4 96
10.4%
6 86
9.3%
5 77
8.3%
9 59
 
6.4%
8 58
 
6.3%
7 52
 
5.6%
0 46
 
5.0%
Space Separator
ValueCountFrequency (%)
999
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3255
60.4%
Common 2136
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 259
 
8.0%
주 258
 
7.9%
남 257
 
7.9%
공 257
 
7.9%
도 255
 
7.8%
시 254
 
7.8%
충 254
 
7.8%
리 241
 
7.4%
면 212
 
6.5%
성 47
 
1.4%
Other values (105) 961
29.5%
Common
ValueCountFrequency (%)
999
46.8%
- 210
 
9.8%
1 172
 
8.1%
2 151
 
7.1%
3 130
 
6.1%
4 96
 
4.5%
6 86
 
4.0%
5 77
 
3.6%
9 59
 
2.8%
8 58
 
2.7%
Other values (2) 98
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3255
60.4%
ASCII 2136
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
999
46.8%
- 210
 
9.8%
1 172
 
8.1%
2 151
 
7.1%
3 130
 
6.1%
4 96
 
4.5%
6 86
 
4.0%
5 77
 
3.6%
9 59
 
2.8%
8 58
 
2.7%
Other values (2) 98
 
4.6%
Hangul
ValueCountFrequency (%)
청 259
 
8.0%
주 258
 
7.9%
남 257
 
7.9%
공 257
 
7.9%
도 255
 
7.8%
시 254
 
7.8%
충 254
 
7.8%
리 241
 
7.4%
면 212
 
6.5%
성 47
 
1.4%
Other values (105) 961
29.5%

종점
Text

Distinct253
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-10T05:13:08.705528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length21.070866
Min length16

Characters and Unicode

Total characters5352
Distinct characters130
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

Unique252 ?
Unique (%)99.2%

Sample

1st row충청남도 공주시 반포면 학봉리 124-1
2nd row충청남도 공주시 반포면 온천리 156
3rd row충청남도 공주시 계룡면 양화리 146-1
4th row충청남도 공주시 정안면 화봉리 80
5th row충청남도 공주시 의당면 덕학리 361-6
ValueCountFrequency (%)
충청남도 254
20.3%
공주시 254
20.3%
우성면 35
 
2.8%
정안면 30
 
2.4%
계룡면 28
 
2.2%
유구읍 25
 
2.0%
의당면 24
 
1.9%
탄천면 23
 
1.8%
이인면 22
 
1.8%
신풍면 18
 
1.4%
Other values (386) 541
43.1%
2024-01-10T05:13:09.750955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1000
18.7%
청 258
 
4.8%
남 256
 
4.8%
도 256
 
4.8%
공 256
 
4.8%
주 256
 
4.8%
충 254
 
4.7%
시 254
 
4.7%
리 241
 
4.5%
면 214
 
4.0%
Other values (120) 2107
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3260
60.9%
Space Separator 1000
 
18.7%
Decimal Number 908
 
17.0%
Dash Punctuation 184
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 258
 
7.9%
남 256
 
7.9%
도 256
 
7.9%
공 256
 
7.9%
주 256
 
7.9%
충 254
 
7.8%
시 254
 
7.8%
리 241
 
7.4%
면 214
 
6.6%
성 43
 
1.3%
Other values (108) 972
29.8%
Decimal Number
ValueCountFrequency (%)
1 172
18.9%
2 124
13.7%
3 107
11.8%
4 106
11.7%
5 82
9.0%
6 74
8.1%
8 68
 
7.5%
7 65
 
7.2%
0 58
 
6.4%
9 52
 
5.7%
Space Separator
ValueCountFrequency (%)
1000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3260
60.9%
Common 2092
39.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 258
 
7.9%
남 256
 
7.9%
도 256
 
7.9%
공 256
 
7.9%
주 256
 
7.9%
충 254
 
7.8%
시 254
 
7.8%
리 241
 
7.4%
면 214
 
6.6%
성 43
 
1.3%
Other values (108) 972
29.8%
Common
ValueCountFrequency (%)
1000
47.8%
- 184
 
8.8%
1 172
 
8.2%
2 124
 
5.9%
3 107
 
5.1%
4 106
 
5.1%
5 82
 
3.9%
6 74
 
3.5%
8 68
 
3.3%
7 65
 
3.1%
Other values (2) 110
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3260
60.9%
ASCII 2092
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1000
47.8%
- 184
 
8.8%
1 172
 
8.2%
2 124
 
5.9%
3 107
 
5.1%
4 106
 
5.1%
5 82
 
3.9%
6 74
 
3.5%
8 68
 
3.3%
7 65
 
3.1%
Other values (2) 110
 
5.3%
Hangul
ValueCountFrequency (%)
청 258
 
7.9%
남 256
 
7.9%
도 256
 
7.9%
공 256
 
7.9%
주 256
 
7.9%
충 254
 
7.8%
시 254
 
7.8%
리 241
 
7.4%
면 214
 
6.6%
성 43
 
1.3%
Other values (108) 972
29.8%

증,감
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1134252
Minimum0
Maximum5.6
Zeros243
Zeros (%)95.7%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:13:09.903324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5.6
Range5.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61751997
Coefficient of variation (CV)5.4442927
Kurtosis41.552984
Mean0.1134252
Median Absolute Deviation (MAD)0
Skewness6.1967421
Sum28.81
Variance0.38133091
MonotonicityNot monotonic
2024-01-10T05:13:10.034590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 243
95.7%
2.01 2
 
0.8%
3.75 1
 
0.4%
5.6 1
 
0.4%
2.1 1
 
0.4%
0.85 1
 
0.4%
4.4 1
 
0.4%
3.12 1
 
0.4%
2.5 1
 
0.4%
0.07 1
 
0.4%
ValueCountFrequency (%)
0.0 243
95.7%
0.07 1
 
0.4%
0.85 1
 
0.4%
2.01 2
 
0.8%
2.1 1
 
0.4%
2.4 1
 
0.4%
2.5 1
 
0.4%
3.12 1
 
0.4%
3.75 1
 
0.4%
4.4 1
 
0.4%
ValueCountFrequency (%)
5.6 1
0.4%
4.4 1
0.4%
3.75 1
0.4%
3.12 1
0.4%
2.5 1
0.4%
2.4 1
0.4%
2.1 1
0.4%
2.01 2
0.8%
0.85 1
0.4%
0.07 1
0.4%

기존연장
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8500394
Minimum0.2
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:13:10.207521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.9
Q11.4
median2.2
Q33.5
95-th percentile7.4935
Maximum23.5
Range23.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation2.5101915
Coefficient of variation (CV)0.88075677
Kurtosis21.325268
Mean2.8500394
Median Absolute Deviation (MAD)1
Skewness3.6758675
Sum723.91
Variance6.3010613
MonotonicityNot monotonic
2024-01-10T05:13:10.374214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 18
 
7.1%
1.5 15
 
5.9%
2.5 10
 
3.9%
2.0 10
 
3.9%
1.8 10
 
3.9%
1.0 10
 
3.9%
1.3 9
 
3.5%
1.1 8
 
3.1%
1.4 8
 
3.1%
1.6 7
 
2.8%
Other values (58) 149
58.7%
ValueCountFrequency (%)
0.2 1
 
0.4%
0.25 1
 
0.4%
0.3 1
 
0.4%
0.5 1
 
0.4%
0.7 3
 
1.2%
0.8 4
 
1.6%
0.9 6
 
2.4%
1.0 10
3.9%
1.1 8
3.1%
1.2 18
7.1%
ValueCountFrequency (%)
23.5 1
0.4%
15.5 1
0.4%
12.3 1
0.4%
12.2 1
0.4%
11.0 1
0.4%
9.6 1
0.4%
9.5 1
0.4%
9.0 1
0.4%
8.0 2
0.8%
7.6 1
0.4%

총연장
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9129921
Minimum0.2
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:13:10.556763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.9
Q11.4
median2.2
Q33.5
95-th percentile8
Maximum23.5
Range23.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation2.5795922
Coefficient of variation (CV)0.88554728
Kurtosis18.673201
Mean2.9129921
Median Absolute Deviation (MAD)1
Skewness3.4502232
Sum739.9
Variance6.6542962
MonotonicityNot monotonic
2024-01-10T05:13:10.745317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 18
 
7.1%
1.5 15
 
5.9%
1.8 10
 
3.9%
1.0 10
 
3.9%
2.5 10
 
3.9%
2.0 10
 
3.9%
1.3 9
 
3.5%
1.1 8
 
3.1%
1.4 8
 
3.1%
1.6 7
 
2.8%
Other values (55) 149
58.7%
ValueCountFrequency (%)
0.2 1
 
0.4%
0.3 1
 
0.4%
0.5 1
 
0.4%
0.7 3
 
1.2%
0.8 4
 
1.6%
0.9 6
 
2.4%
1.0 10
3.9%
1.1 8
3.1%
1.2 18
7.1%
1.3 9
3.5%
ValueCountFrequency (%)
23.5 1
 
0.4%
15.5 1
 
0.4%
12.2 1
 
0.4%
11.0 1
 
0.4%
10.0 3
1.2%
9.6 1
 
0.4%
9.5 2
0.8%
9.0 2
0.8%
8.0 2
0.8%
7.9 1
 
0.4%

소계
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9129921
Minimum0.2
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:13:10.924430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.9
Q11.4
median2.2
Q33.5
95-th percentile8
Maximum23.5
Range23.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation2.5795922
Coefficient of variation (CV)0.88554728
Kurtosis18.673201
Mean2.9129921
Median Absolute Deviation (MAD)1
Skewness3.4502232
Sum739.9
Variance6.6542962
MonotonicityNot monotonic
2024-01-10T05:13:11.112031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 18
 
7.1%
1.5 15
 
5.9%
1.8 10
 
3.9%
1.0 10
 
3.9%
2.5 10
 
3.9%
2.0 10
 
3.9%
1.3 9
 
3.5%
1.1 8
 
3.1%
1.4 8
 
3.1%
1.6 7
 
2.8%
Other values (55) 149
58.7%
ValueCountFrequency (%)
0.2 1
 
0.4%
0.3 1
 
0.4%
0.5 1
 
0.4%
0.7 3
 
1.2%
0.8 4
 
1.6%
0.9 6
 
2.4%
1.0 10
3.9%
1.1 8
3.1%
1.2 18
7.1%
1.3 9
3.5%
ValueCountFrequency (%)
23.5 1
 
0.4%
15.5 1
 
0.4%
12.2 1
 
0.4%
11.0 1
 
0.4%
10.0 3
1.2%
9.6 1
 
0.4%
9.5 2
0.8%
9.0 2
0.8%
8.0 2
0.8%
7.9 1
 
0.4%

포장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2751969
Minimum0
Maximum12.7
Zeros10
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:13:11.286835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.2
median1.7
Q33
95-th percentile5.375
Maximum12.7
Range12.7
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.8054526
Coefficient of variation (CV)0.79353687
Kurtosis6.8354051
Mean2.2751969
Median Absolute Deviation (MAD)0.8
Skewness2.1045456
Sum577.9
Variance3.2596591
MonotonicityNot monotonic
2024-01-10T05:13:11.495768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 19
 
7.5%
1.5 17
 
6.7%
1.0 14
 
5.5%
0.0 10
 
3.9%
0.8 9
 
3.5%
1.3 9
 
3.5%
1.6 9
 
3.5%
1.8 8
 
3.1%
2.5 8
 
3.1%
0.9 7
 
2.8%
Other values (49) 144
56.7%
ValueCountFrequency (%)
0.0 10
3.9%
0.2 2
 
0.8%
0.3 2
 
0.8%
0.4 3
 
1.2%
0.5 3
 
1.2%
0.6 3
 
1.2%
0.7 3
 
1.2%
0.8 9
3.5%
0.9 7
2.8%
1.0 14
5.5%
ValueCountFrequency (%)
12.7 1
0.4%
10.0 1
0.4%
9.6 1
0.4%
9.0 1
0.4%
8.0 2
0.8%
7.9 1
0.4%
6.4 1
0.4%
6.3 1
0.4%
6.2 1
0.4%
5.9 1
0.4%

미포장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63779528
Minimum0
Maximum21.3
Zeros188
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:13:11.684035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.375
95-th percentile3
Maximum21.3
Range21.3
Interquartile range (IQR)0.375

Descriptive statistics

Standard deviation1.9046157
Coefficient of variation (CV)2.9862494
Kurtosis59.003793
Mean0.63779528
Median Absolute Deviation (MAD)0
Skewness6.5345058
Sum162
Variance3.6275611
MonotonicityNot monotonic
2024-01-10T05:13:11.849241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 188
74.0%
1.0 5
 
2.0%
1.5 4
 
1.6%
0.6 4
 
1.6%
2.0 4
 
1.6%
1.2 4
 
1.6%
2.5 4
 
1.6%
3.0 3
 
1.2%
0.8 3
 
1.2%
0.4 2
 
0.8%
Other values (25) 33
 
13.0%
ValueCountFrequency (%)
0.0 188
74.0%
0.3 2
 
0.8%
0.4 2
 
0.8%
0.5 1
 
0.4%
0.6 4
 
1.6%
0.7 2
 
0.8%
0.73 1
 
0.4%
0.77 1
 
0.4%
0.8 3
 
1.2%
0.9 2
 
0.8%
ValueCountFrequency (%)
21.3 1
0.4%
11.0 1
0.4%
7.2 1
0.4%
7.0 1
0.4%
6.6 1
0.4%
6.5 1
0.4%
6.0 1
0.4%
5.5 1
0.4%
3.8 2
0.8%
3.6 1
0.4%

포장율(%)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.960197
Minimum0
Maximum100
Zeros10
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-01-10T05:13:12.029617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.9515
Q185.32
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)14.68

Descriptive statistics

Standard deviation28.72497
Coefficient of variation (CV)0.33809915
Kurtosis1.8659697
Mean84.960197
Median Absolute Deviation (MAD)0
Skewness-1.7709822
Sum21579.89
Variance825.12393
MonotonicityNot monotonic
2024-01-10T05:13:12.208039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
100.0 188
74.0%
0.0 10
 
3.9%
50.0 3
 
1.2%
40.0 3
 
1.2%
66.67 3
 
1.2%
30.0 2
 
0.8%
27.27 2
 
0.8%
33.33 2
 
0.8%
54.55 2
 
0.8%
16.67 1
 
0.4%
Other values (38) 38
 
15.0%
ValueCountFrequency (%)
0.0 10
3.9%
6.25 1
 
0.4%
9.36 1
 
0.4%
11.76 1
 
0.4%
16.67 1
 
0.4%
20.0 1
 
0.4%
21.05 1
 
0.4%
25.0 1
 
0.4%
27.27 2
 
0.8%
27.78 1
 
0.4%
ValueCountFrequency (%)
100.0 188
74.0%
88.24 1
 
0.4%
85.71 1
 
0.4%
85.19 1
 
0.4%
83.33 1
 
0.4%
82.86 1
 
0.4%
81.94 1
 
0.4%
79.55 1
 
0.4%
78.67 1
 
0.4%
72.73 1
 
0.4%

Interactions

2024-01-10T05:13:03.796833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:59.241367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.106157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.865723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.590229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.325180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.083779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.896048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:59.374935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.239690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.975509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.687784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.441089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.193384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.997998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:59.493220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.333338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.093169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.784838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.549295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.292191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:04.111517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:59.608585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.448788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.193198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.877445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.654801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.400450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:04.208498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:59.737710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.557100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.300981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.992350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.770511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.509727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:04.314204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:12:59.884713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.667577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.406093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.094244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.878993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.621078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:04.407359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.000940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:00.764066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:01.495888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.197820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:02.975400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:13:03.699600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:13:12.341542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류증,감기존연장총연장소계포장미포장포장율(%)
종류1.0000.3410.5230.5440.5440.4710.3720.385
증,감0.3411.0000.7140.7190.7190.6580.4580.218
기존연장0.5230.7141.0000.9940.9940.8230.8130.426
총연장0.5440.7190.9941.0001.0000.8430.7960.457
소계0.5440.7190.9941.0001.0000.8430.7960.457
포장0.4710.6580.8230.8430.8431.0000.2370.421
미포장0.3720.4580.8130.7960.7960.2371.0000.561
포장율(%)0.3850.2180.4260.4570.4570.4210.5611.000
2024-01-10T05:13:12.517127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
증,감기존연장총연장소계포장미포장포장율(%)종류
증,감1.0000.1900.2640.2640.1700.173-0.1370.215
기존연장0.1901.0000.9810.9810.7430.312-0.2340.353
총연장0.2640.9811.0001.0000.7580.317-0.2380.371
소계0.2640.9811.0001.0000.7580.317-0.2380.371
포장0.1700.7430.7580.7581.000-0.2480.3390.293
미포장0.1730.3120.3170.317-0.2481.000-0.9840.262
포장율(%)-0.137-0.234-0.238-0.2380.339-0.9841.0000.172
종류0.2150.3530.3710.3710.2930.2620.1721.000

Missing values

2024-01-10T05:13:04.564346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:13:04.792711image/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

종류노선명노선번호기점종점증,감기존연장총연장소계포장미포장포장율(%)
0시도온천~학봉시도1호충청남도 공주시 반포면 온천리 311-6충청남도 공주시 반포면 학봉리 124-13.750.254.04.04.00.0100.0
1시도월암~하신시도2호충청남도 공주시 계룡면 유평리 262-1충청남도 공주시 반포면 온천리 1560.012.212.212.25.07.240.98
2시도경천~양화시도4호충청남도 공주시 계룡면 경천리 62-8충청남도 공주시 계룡면 양화리 146-10.03.13.13.13.10.0100.0
3시도청룡~오인시도7호충청남도 공주시 의당면 청룡리 665-2충청남도 공주시 정안면 화봉리 800.03.23.23.23.20.0100.0
4시도수촌~도신시도8호충청남도 공주시 의당면 수촌리 29-3충청남도 공주시 의당면 덕학리 361-60.09.09.09.09.00.0100.0
5시도달산~추봉시도9호충청남도 공주시 탄천면 성리 103-11충청남도 공주시 이인면 주봉리 502-50.08.08.08.08.00.0100.0
6시도대성~신웅시도10호충청남도 공주시 우성면 대성리 266-12충청남도 공주시 우성면 신웅리 2190.07.27.27.23.43.847.22
7시도옥성~목천시도11호충청남도 공주시 우성면 평목리 76충청남도 공주시 우성면 목천리 14-22.017.499.59.56.23.365.26
8시도목천~신관시도12호충청남도 공주시 우성면 목천리 65-28충청남도 공주시 신관동 495-1662.015.213.23.23.20.0100.0
9시도구계~문금시도13호충청남도 공주시 유구읍 구계리 438충청남도 공주시 유구읍 문금리 235-20.09.59.59.55.73.860.0
종류노선명노선번호기점종점증,감기존연장총연장소계포장미포장포장율(%)
244농도호들선사곡305충청남도 공주시 사곡면 호계리 36-1충청남도 공주시 사곡면 호계리 149-60.01.51.51.51.50.0100.0
245농도계실선사곡306충청남도 공주시 사곡면 화월리 208-3충청남도 공주시 사곡면 계실리 242-30.01.51.51.51.50.0100.0
246농도무대선신풍301충청남도 공주시 신풍면 용수리 398-2충청남도 공주시 신풍면 대룡리 8770.01.51.51.51.50.0100.0
247농도죽로선신풍302충청남도 공주시 신풍면 쌍대리 200-1충청남도 공주시 신풍면 쌍대리 2960.02.92.92.92.90.0100.0
248농도말멍선신풍303충청남도 공주시 신풍면 입동리 386-2충청남도 공주시 신풍면 입동리 87-50.02.52.52.52.50.0100.0
249농도쇠고선신풍304충청남도 공주시 신풍면 산정리 495충청남도 공주시 신풍면 산정리 8220.01.21.21.21.20.0100.0
250농도정골선신풍305충청남도 공주시 신풍면 백룡리 284-3충청남도 공주시 신풍면 백룡리 203-10.01.01.01.01.00.0100.0
251농도동화선신풍306충청남도 공주시 신풍면 동원리 240충청남도 공주시 신풍면 동원리 553-330.01.11.11.11.10.0100.0
252농도오화선신풍307충청남도 공주시 신풍면 백룡리 512충청남도 공주시 신풍면 동원리 553-330.01.21.21.21.20.0100.0
253농도동원선신풍308충청남도 공주시 신풍면 동원리 498-1충청남도 공주시 신풍면 동원리 223-90.02.02.02.02.00.0100.0