Overview

Dataset statistics

Number of variables11
Number of observations223
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.6 KiB
Average record size in memory94.6 B

Variable types

Categorical1
Numeric6
Text4

Dataset

Description완주군 도로 현황으로 군도, 면도, 리도, 농도별 노선명, 노선번호, 기점, 종점, 총연장, 정비율 등을 제공합니다.
Author전북특별자치도 완주군
URLhttps://www.data.go.kr/data/3076586/fileData.do

Alerts

정비도연장 (미터) is highly overall correlated with 정비율(백분율)High correlation
미정비도연장 (미터) is highly overall correlated with 정비율(백분율)High correlation
미개설도연장 (미터) is highly overall correlated with 정비율(백분율)High correlation
정비율(백분율) is highly overall correlated with 정비도연장 (미터) and 2 other fieldsHigh correlation
정비도연장 (미터) has 45 (20.2%) zerosZeros
미정비도연장 (미터) has 139 (62.3%) zerosZeros
미개설도연장 (미터) has 176 (78.9%) zerosZeros
정비율(백분율) has 44 (19.7%) zerosZeros

Reproduction

Analysis started2024-03-14 17:07:10.481883
Analysis finished2024-03-14 17:07:19.940981
Duration9.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
농도
102 
리도
76 
군도
26 
면도
19 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농도 102
45.7%
리도 76
34.1%
군도 26
 
11.7%
면도 19
 
8.5%

Length

2024-03-15T02:07:20.052101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:07:20.237165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농도 102
45.7%
리도 76
34.1%
군도 26
 
11.7%
면도 19
 
8.5%

관리번호
Real number (ℝ)

Distinct102
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.103139
Minimum1
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T02:07:20.469865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q114.5
median34
Q361.5
95-th percentile90.9
Maximum102
Range101
Interquartile range (IQR)47

Descriptive statistics

Standard deviation28.146386
Coefficient of variation (CV)0.71979863
Kurtosis-0.92248249
Mean39.103139
Median Absolute Deviation (MAD)22
Skewness0.47900752
Sum8720
Variance792.21904
MonotonicityNot monotonic
2024-03-15T02:07:20.737015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
1.8%
11 4
 
1.8%
2 4
 
1.8%
19 4
 
1.8%
18 4
 
1.8%
17 4
 
1.8%
16 4
 
1.8%
15 4
 
1.8%
14 4
 
1.8%
13 4
 
1.8%
Other values (92) 183
82.1%
ValueCountFrequency (%)
1 4
1.8%
2 4
1.8%
3 4
1.8%
4 4
1.8%
5 4
1.8%
6 4
1.8%
7 4
1.8%
8 4
1.8%
9 4
1.8%
10 4
1.8%
ValueCountFrequency (%)
102 1
0.4%
101 1
0.4%
100 1
0.4%
99 1
0.4%
98 1
0.4%
97 1
0.4%
96 1
0.4%
95 1
0.4%
94 1
0.4%
93 1
0.4%

노선명
Text

Distinct201
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-15T02:07:22.308167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2331839
Min length3

Characters and Unicode

Total characters721
Distinct characters139
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

Unique179 ?
Unique (%)80.3%

Sample

1st row덕천-백여
2nd row용진-봉동
3rd row은교-상개
4th row이서-반교
5th row이서-용지
ValueCountFrequency (%)
고비선 2
 
0.9%
신원선 2
 
0.9%
마치선 2
 
0.9%
내정선 2
 
0.9%
계월선 2
 
0.9%
설경선 2
 
0.9%
양전선 2
 
0.9%
목효선 2
 
0.9%
신촌선 2
 
0.9%
구제선 2
 
0.9%
Other values (191) 203
91.0%
2024-03-15T02:07:23.995428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
선 198
27.5%
- 26
 
3.6%
동 18
 
2.5%
신 18
 
2.5%
산 16
 
2.2%
봉 16
 
2.2%
용 15
 
2.1%
소 12
 
1.7%
덕 11
 
1.5%
구 9
 
1.2%
Other values (129) 382
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 695
96.4%
Dash Punctuation 26
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 198
28.5%
동 18
 
2.6%
신 18
 
2.6%
산 16
 
2.3%
봉 16
 
2.3%
용 15
 
2.2%
소 12
 
1.7%
덕 11
 
1.6%
구 9
 
1.3%
화 9
 
1.3%
Other values (128) 373
53.7%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 695
96.4%
Common 26
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 198
28.5%
동 18
 
2.6%
신 18
 
2.6%
산 16
 
2.3%
봉 16
 
2.3%
용 15
 
2.2%
소 12
 
1.7%
덕 11
 
1.6%
구 9
 
1.3%
화 9
 
1.3%
Other values (128) 373
53.7%
Common
ValueCountFrequency (%)
- 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 695
96.4%
ASCII 26
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
선 198
28.5%
동 18
 
2.6%
신 18
 
2.6%
산 16
 
2.3%
봉 16
 
2.3%
용 15
 
2.2%
소 12
 
1.7%
덕 11
 
1.6%
구 9
 
1.3%
화 9
 
1.3%
Other values (128) 373
53.7%
ASCII
ValueCountFrequency (%)
- 26
100.0%

노선번호
Text

Distinct211
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-15T02:07:25.399493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6098655
Min length1

Characters and Unicode

Total characters1028
Distinct characters31
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

Unique199 ?
Unique (%)89.2%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5
ValueCountFrequency (%)
상관304 2
 
0.9%
용진306 2
 
0.9%
용진305 2
 
0.9%
용진309 2
 
0.9%
용진308 2
 
0.9%
용진307 2
 
0.9%
상관301 2
 
0.9%
상관302 2
 
0.9%
상관203 2
 
0.9%
상관202 2
 
0.9%
Other values (201) 203
91.0%
2024-03-15T02:07:27.231136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 190
18.5%
3 144
14.0%
2 111
 
10.8%
1 86
 
8.4%
이 37
 
3.6%
봉 36
 
3.5%
산 34
 
3.3%
동 30
 
2.9%
용 27
 
2.6%
진 27
 
2.6%
Other values (21) 306
29.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 634
61.7%
Other Letter 394
38.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 37
 
9.4%
봉 36
 
9.1%
산 34
 
8.6%
동 30
 
7.6%
용 27
 
6.9%
진 27
 
6.9%
구 22
 
5.6%
상 21
 
5.3%
화 19
 
4.8%
소 16
 
4.1%
Other values (11) 125
31.7%
Decimal Number
ValueCountFrequency (%)
0 190
30.0%
3 144
22.7%
2 111
17.5%
1 86
13.6%
4 26
 
4.1%
5 21
 
3.3%
6 18
 
2.8%
7 14
 
2.2%
9 12
 
1.9%
8 12
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 634
61.7%
Hangul 394
38.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 37
 
9.4%
봉 36
 
9.1%
산 34
 
8.6%
동 30
 
7.6%
용 27
 
6.9%
진 27
 
6.9%
구 22
 
5.6%
상 21
 
5.3%
화 19
 
4.8%
소 16
 
4.1%
Other values (11) 125
31.7%
Common
ValueCountFrequency (%)
0 190
30.0%
3 144
22.7%
2 111
17.5%
1 86
13.6%
4 26
 
4.1%
5 21
 
3.3%
6 18
 
2.8%
7 14
 
2.2%
9 12
 
1.9%
8 12
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 634
61.7%
Hangul 394
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 190
30.0%
3 144
22.7%
2 111
17.5%
1 86
13.6%
4 26
 
4.1%
5 21
 
3.3%
6 18
 
2.8%
7 14
 
2.2%
9 12
 
1.9%
8 12
 
1.9%
Hangul
ValueCountFrequency (%)
이 37
 
9.4%
봉 36
 
9.1%
산 34
 
8.6%
동 30
 
7.6%
용 27
 
6.9%
진 27
 
6.9%
구 22
 
5.6%
상 21
 
5.3%
화 19
 
4.8%
소 16
 
4.1%
Other values (11) 125
31.7%

기점
Text

Distinct208
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-15T02:07:28.959845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.300448
Min length8

Characters and Unicode

Total characters2520
Distinct characters112
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

Unique194 ?
Unique (%)87.0%

Sample

1st row구이면 덕천리 1453
2nd row용진읍 운곡리 987-2
3rd row이서면 은교리 산154-4
4th row이서면 반교리 694-12
5th row이서면 반교리 686-1
ValueCountFrequency (%)
용진 27
 
4.0%
봉동 23
 
3.4%
구이 22
 
3.3%
화산 17
 
2.5%
고산 16
 
2.4%
소양 16
 
2.4%
상관 14
 
2.1%
이서 14
 
2.1%
비봉 13
 
1.9%
삼례 11
 
1.6%
Other values (325) 499
74.3%
2024-03-15T02:07:30.757512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
449
17.8%
- 198
 
7.9%
1 152
 
6.0%
2 124
 
4.9%
3 107
 
4.2%
4 91
 
3.6%
7 84
 
3.3%
5 77
 
3.1%
6 77
 
3.1%
8 75
 
3.0%
Other values (102) 1086
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 968
38.4%
Decimal Number 905
35.9%
Space Separator 449
17.8%
Dash Punctuation 198
 
7.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 73
 
7.5%
용 44
 
4.5%
이 43
 
4.4%
봉 42
 
4.3%
구 40
 
4.1%
동 34
 
3.5%
상 33
 
3.4%
진 32
 
3.3%
리 31
 
3.2%
화 29
 
3.0%
Other values (90) 567
58.6%
Decimal Number
ValueCountFrequency (%)
1 152
16.8%
2 124
13.7%
3 107
11.8%
4 91
10.1%
7 84
9.3%
5 77
8.5%
6 77
8.5%
8 75
8.3%
9 60
 
6.6%
0 58
 
6.4%
Space Separator
ValueCountFrequency (%)
449
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1552
61.6%
Hangul 968
38.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 73
 
7.5%
용 44
 
4.5%
이 43
 
4.4%
봉 42
 
4.3%
구 40
 
4.1%
동 34
 
3.5%
상 33
 
3.4%
진 32
 
3.3%
리 31
 
3.2%
화 29
 
3.0%
Other values (90) 567
58.6%
Common
ValueCountFrequency (%)
449
28.9%
- 198
12.8%
1 152
 
9.8%
2 124
 
8.0%
3 107
 
6.9%
4 91
 
5.9%
7 84
 
5.4%
5 77
 
5.0%
6 77
 
5.0%
8 75
 
4.8%
Other values (2) 118
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1552
61.6%
Hangul 968
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
449
28.9%
- 198
12.8%
1 152
 
9.8%
2 124
 
8.0%
3 107
 
6.9%
4 91
 
5.9%
7 84
 
5.4%
5 77
 
5.0%
6 77
 
5.0%
8 75
 
4.8%
Other values (2) 118
 
7.6%
Hangul
ValueCountFrequency (%)
산 73
 
7.5%
용 44
 
4.5%
이 43
 
4.4%
봉 42
 
4.3%
구 40
 
4.1%
동 34
 
3.5%
상 33
 
3.4%
진 32
 
3.3%
리 31
 
3.2%
화 29
 
3.0%
Other values (90) 567
58.6%

종점
Text

Distinct210
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-15T02:07:32.501006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length10.865471
Min length8

Characters and Unicode

Total characters2423
Distinct characters118
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

Unique197 ?
Unique (%)88.3%

Sample

1st row구이면 백여리 624-16
2nd row봉동읍 성덕리 1329-3
3rd row이서면 상개리 580-21
4th row이서면 반교리 산21-3
5th row김제 용지 장신 103-2
ValueCountFrequency (%)
용진 29
 
4.3%
구이 22
 
3.2%
봉동 22
 
3.2%
화산 18
 
2.6%
소양 16
 
2.3%
이서 15
 
2.2%
고산 15
 
2.2%
상관 14
 
2.1%
비봉 13
 
1.9%
운곡 12
 
1.8%
Other values (336) 506
74.2%
2024-03-15T02:07:35.108641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
459
18.9%
- 158
 
6.5%
1 144
 
5.9%
2 117
 
4.8%
3 104
 
4.3%
산 87
 
3.6%
6 81
 
3.3%
4 75
 
3.1%
5 73
 
3.0%
7 68
 
2.8%
Other values (108) 1057
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 979
40.4%
Decimal Number 827
34.1%
Space Separator 459
18.9%
Dash Punctuation 158
 
6.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 87
 
8.9%
봉 44
 
4.5%
이 43
 
4.4%
용 40
 
4.1%
구 38
 
3.9%
동 35
 
3.6%
상 34
 
3.5%
진 32
 
3.3%
화 31
 
3.2%
운 29
 
3.0%
Other values (96) 566
57.8%
Decimal Number
ValueCountFrequency (%)
1 144
17.4%
2 117
14.1%
3 104
12.6%
6 81
9.8%
4 75
9.1%
5 73
8.8%
7 68
8.2%
9 65
7.9%
8 53
 
6.4%
0 47
 
5.7%
Space Separator
ValueCountFrequency (%)
459
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1444
59.6%
Hangul 979
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 87
 
8.9%
봉 44
 
4.5%
이 43
 
4.4%
용 40
 
4.1%
구 38
 
3.9%
동 35
 
3.6%
상 34
 
3.5%
진 32
 
3.3%
화 31
 
3.2%
운 29
 
3.0%
Other values (96) 566
57.8%
Common
ValueCountFrequency (%)
459
31.8%
- 158
 
10.9%
1 144
 
10.0%
2 117
 
8.1%
3 104
 
7.2%
6 81
 
5.6%
4 75
 
5.2%
5 73
 
5.1%
7 68
 
4.7%
9 65
 
4.5%
Other values (2) 100
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1444
59.6%
Hangul 979
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
459
31.8%
- 158
 
10.9%
1 144
 
10.0%
2 117
 
8.1%
3 104
 
7.2%
6 81
 
5.6%
4 75
 
5.2%
5 73
 
5.1%
7 68
 
4.7%
9 65
 
4.5%
Other values (2) 100
 
6.9%
Hangul
ValueCountFrequency (%)
산 87
 
8.9%
봉 44
 
4.5%
이 43
 
4.4%
용 40
 
4.1%
구 38
 
3.9%
동 35
 
3.6%
상 34
 
3.5%
진 32
 
3.3%
화 31
 
3.2%
운 29
 
3.0%
Other values (96) 566
57.8%

총연장 (미터)
Real number (ℝ)

Distinct62
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2422.4215
Minimum200
Maximum23800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T02:07:35.685235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile600
Q11000
median1500
Q32850
95-th percentile7470
Maximum23800
Range23600
Interquartile range (IQR)1850

Descriptive statistics

Standard deviation2634.7196
Coefficient of variation (CV)1.0876388
Kurtosis22.995098
Mean2422.4215
Median Absolute Deviation (MAD)600
Skewness3.9573792
Sum540200
Variance6941747.3
MonotonicityNot monotonic
2024-03-15T02:07:36.089560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 21
 
9.4%
1100 17
 
7.6%
1400 12
 
5.4%
800 11
 
4.9%
1200 9
 
4.0%
900 9
 
4.0%
1500 8
 
3.6%
1300 8
 
3.6%
2200 7
 
3.1%
3100 7
 
3.1%
Other values (52) 114
51.1%
ValueCountFrequency (%)
200 1
 
0.4%
400 3
 
1.3%
500 4
 
1.8%
600 5
 
2.2%
700 5
 
2.2%
800 11
4.9%
900 9
4.0%
1000 21
9.4%
1100 17
7.6%
1200 9
4.0%
ValueCountFrequency (%)
23800 1
0.4%
16000 1
0.4%
12100 1
0.4%
10300 1
0.4%
9600 1
0.4%
9500 1
0.4%
9200 1
0.4%
8800 1
0.4%
8600 1
0.4%
7700 1
0.4%

정비도연장 (미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1540.1345
Minimum0
Maximum16000
Zeros45
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T02:07:36.439861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1500
median1100
Q31700
95-th percentile5190
Maximum16000
Range16000
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation1925.5961
Coefficient of variation (CV)1.2502778
Kurtosis16.5183
Mean1540.1345
Median Absolute Deviation (MAD)600
Skewness3.299349
Sum343450
Variance3707920.3
MonotonicityNot monotonic
2024-03-15T02:07:37.134014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45
20.2%
1100 17
 
7.6%
1000 17
 
7.6%
800 12
 
5.4%
1400 10
 
4.5%
900 9
 
4.0%
1500 8
 
3.6%
1200 7
 
3.1%
1300 7
 
3.1%
400 6
 
2.7%
Other values (49) 85
38.1%
ValueCountFrequency (%)
0 45
20.2%
100 1
 
0.4%
200 1
 
0.4%
300 1
 
0.4%
400 6
 
2.7%
500 5
 
2.2%
600 4
 
1.8%
650 1
 
0.4%
700 6
 
2.7%
750 1
 
0.4%
ValueCountFrequency (%)
16000 1
0.4%
9600 1
0.4%
8600 1
0.4%
7650 1
0.4%
7500 2
0.9%
7200 1
0.4%
6600 1
0.4%
6400 1
0.4%
5400 1
0.4%
5300 1
0.4%

미정비도연장 (미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean729.93274
Minimum0
Maximum12500
Zeros139
Zeros (%)62.3%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T02:07:37.676949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3912.5
95-th percentile3245
Maximum12500
Range12500
Interquartile range (IQR)912.5

Descriptive statistics

Standard deviation1470.7406
Coefficient of variation (CV)2.0148989
Kurtosis20.491572
Mean729.93274
Median Absolute Deviation (MAD)0
Skewness3.6889242
Sum162775
Variance2163078
MonotonicityNot monotonic
2024-03-15T02:07:38.188226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139
62.3%
700 5
 
2.2%
1900 5
 
2.2%
1800 4
 
1.8%
900 4
 
1.8%
1000 3
 
1.3%
3000 3
 
1.3%
500 3
 
1.3%
1100 3
 
1.3%
300 3
 
1.3%
Other values (41) 51
 
22.9%
ValueCountFrequency (%)
0 139
62.3%
100 2
 
0.9%
150 1
 
0.4%
200 1
 
0.4%
300 3
 
1.3%
350 1
 
0.4%
400 2
 
0.9%
500 3
 
1.3%
600 2
 
0.9%
700 5
 
2.2%
ValueCountFrequency (%)
12500 1
0.4%
6850 1
0.4%
6400 1
0.4%
5700 1
0.4%
5000 1
0.4%
4900 1
0.4%
4700 1
0.4%
4200 1
0.4%
3700 1
0.4%
3500 1
0.4%

미개설도연장 (미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.52466
Minimum0
Maximum6100
Zeros176
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T02:07:38.583936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1100
Maximum6100
Range6100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation699.23279
Coefficient of variation (CV)3.1564557
Kurtosis31.798567
Mean221.52466
Median Absolute Deviation (MAD)0
Skewness5.134613
Sum49400
Variance488926.49
MonotonicityNot monotonic
2024-03-15T02:07:38.988686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 176
78.9%
300 6
 
2.7%
900 3
 
1.3%
500 3
 
1.3%
700 3
 
1.3%
400 3
 
1.3%
200 3
 
1.3%
600 3
 
1.3%
100 2
 
0.9%
1000 2
 
0.9%
Other values (18) 19
 
8.5%
ValueCountFrequency (%)
0 176
78.9%
50 1
 
0.4%
100 2
 
0.9%
200 3
 
1.3%
250 1
 
0.4%
300 6
 
2.7%
350 1
 
0.4%
400 3
 
1.3%
500 3
 
1.3%
600 3
 
1.3%
ValueCountFrequency (%)
6100 1
0.4%
4200 1
0.4%
3600 1
0.4%
3500 1
0.4%
3200 1
0.4%
2100 1
0.4%
1800 1
0.4%
1600 1
0.4%
1400 1
0.4%
1300 1
0.4%

정비율(백분율)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.64574
Minimum0
Maximum100
Zeros44
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T02:07:39.363749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)65

Descriptive statistics

Standard deviation40.816498
Coefficient of variation (CV)0.5860588
Kurtosis-1.0192167
Mean69.64574
Median Absolute Deviation (MAD)0
Skewness-0.85146489
Sum15531
Variance1665.9865
MonotonicityNot monotonic
2024-03-15T02:07:39.609861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
100 126
56.5%
0 44
 
19.7%
90 4
 
1.8%
50 3
 
1.3%
48 2
 
0.9%
92 2
 
0.9%
64 2
 
0.9%
59 2
 
0.9%
27 2
 
0.9%
58 2
 
0.9%
Other values (33) 34
 
15.2%
ValueCountFrequency (%)
0 44
19.7%
8 1
 
0.4%
18 1
 
0.4%
20 1
 
0.4%
21 1
 
0.4%
22 1
 
0.4%
24 2
 
0.9%
25 1
 
0.4%
27 2
 
0.9%
31 1
 
0.4%
ValueCountFrequency (%)
100 126
56.5%
99 1
 
0.4%
92 2
 
0.9%
91 1
 
0.4%
90 4
 
1.8%
88 1
 
0.4%
79 1
 
0.4%
78 1
 
0.4%
76 1
 
0.4%
74 1
 
0.4%

Interactions

2024-03-15T02:07:18.358329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:11.212471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:12.474603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:13.962526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:15.672399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:17.080721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:18.503059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:11.544769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:12.690205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:14.194718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:15.822822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:17.392080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:18.734897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:11.781321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:12.927622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:14.679953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:16.055955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:17.605205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:18.926242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:11.997289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:13.191866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:14.884567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:16.329712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:17.847073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:19.079946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:12.160790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:13.440032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:15.152528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:16.580102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:18.036470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:19.232037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:12.315191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:13.693627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:15.416087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:16.833624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:18.191895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:07:39.824598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류관리번호총연장 (미터)정비도연장 (미터)미정비도연장 (미터)미개설도연장 (미터)정비율(백분율)
분류1.0000.5180.7910.4780.4380.2390.383
관리번호0.5181.0000.0000.0000.1060.0000.283
총연장 (미터)0.7910.0001.0000.8160.7880.6960.408
정비도연장 (미터)0.4780.0000.8161.0000.4430.4450.145
미정비도연장 (미터)0.4380.1060.7880.4431.0000.8480.566
미개설도연장 (미터)0.2390.0000.6960.4450.8481.0000.527
정비율(백분율)0.3830.2830.4080.1450.5660.5271.000
2024-03-15T02:07:40.119871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호총연장 (미터)정비도연장 (미터)미정비도연장 (미터)미개설도연장 (미터)정비율(백분율)분류
관리번호1.000-0.272-0.097-0.1290.0070.1390.331
총연장 (미터)-0.2721.0000.4890.3890.143-0.2450.454
정비도연장 (미터)-0.0970.4891.000-0.418-0.2110.5950.346
미정비도연장 (미터)-0.1290.389-0.4181.0000.432-0.8860.314
미개설도연장 (미터)0.0070.143-0.2110.4321.000-0.5280.164
정비율(백분율)0.139-0.2450.595-0.886-0.5281.0000.238
분류0.3310.4540.3460.3140.1640.2381.000

Missing values

2024-03-15T02:07:19.434761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:07:19.728287image/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

분류관리번호노선명노선번호기점종점총연장 (미터)정비도연장 (미터)미정비도연장 (미터)미개설도연장 (미터)정비율(백분율)
0군도1덕천-백여1구이면 덕천리 1453구이면 백여리 624-16160001600000100
1군도2용진-봉동2용진읍 운곡리 987-2봉동읍 성덕리 1329-336000036000
2군도3은교-상개3이서면 은교리 산154-4이서면 상개리 580-212800280000100
3군도4이서-반교4이서면 반교리 694-12이서면 반교리 산21-31000100000100
4군도5이서-용지5이서면 반교리 686-1김제 용지 장신 103-233000330000
5군도6상관-용암6상관면 용암리 산227-17상관면 용암리 457-63300800190060024
6군도7삼례-봉동7삼례읍 하리 234-4봉동읍 구암리 188-17700765005099
7군도8봉동-화산8봉동읍 둔산리 142-14화산면 와룡리 456-1950051001200320053
8군도9덕천-광곡9구이면 덕천리 1415-54구이면 광곡리 499-13600360000100
9군도10용진-소양10용진읍 구억리 42-3소양면 황운리 873-287500750000100
분류관리번호노선명노선번호기점종점총연장 (미터)정비도연장 (미터)미정비도연장 (미터)미개설도연장 (미터)정비율(백분율)
213농도93신월선동상301동상 신월 233-6동상 신월 1301400140000100
214농도94구수선동상302동상 신월 산90-2동상 신월 791400140000100
215농도95검태선동상303동상 신월 산56-24동상 신월 8911400140000100
216농도96밤티선동상304동상 사봉 293-4동상 사봉 225130090040040069
217농도97화심선동상305동상 수만 282-1동상 수만 31320020000100
218농도98묵인선동상306동상 사봉 산 11-2동상 신월 산87-133100310000100
219농도99죽림선경천301경천 경천 203-1경천 경천 674-880080000100
220농도100오복선경천302경천 경천 145-2경천 경천 5-11200120000100
221농도101구룡선경천303경천 용복 193-8경천 용복 572-23300330000100
222농도102요동선경천304경천 가천 227경천 가천 9021400140000100