Overview

Dataset statistics

Number of variables10
Number of observations92
Missing cells179
Missing cells (%)19.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory85.4 B

Variable types

Numeric4
Text4
Categorical1
DateTime1

Dataset

Description광주광역시 광산구에 설치된 자전거 도로 현황에 관한 데이터로 노선명, 기점, 종점, 총연장, 데이터기준일자 등의 항목을 제공합니다.
Author광주광역시 광산구
URLhttps://www.data.go.kr/data/15027974/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 분리형겸용도로(d)_연장(km)High correlation
총연장(km)(a_b_c_d_e) is highly overall correlated with 전용도로(a)_연장(km) and 1 other fieldsHigh correlation
전용도로(a)_연장(km) is highly overall correlated with 총연장(km)(a_b_c_d_e)High correlation
분리형겸용도로(d)_연장(km) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
전용도로(a)_연장(km) has 86 (93.5%) missing valuesMissing
전용도로(a)_폭원(m) has 86 (93.5%) missing valuesMissing
분리형겸용도로(d)_연장(km) has 7 (7.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:20:49.066893
Analysis finished2023-12-12 20:20:52.754542
Duration3.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.5
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T05:20:52.857173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.55
Q123.75
median46.5
Q369.25
95-th percentile87.45
Maximum92
Range91
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation26.70206
Coefficient of variation (CV)0.57423785
Kurtosis-1.2
Mean46.5
Median Absolute Deviation (MAD)23
Skewness0
Sum4278
Variance713
MonotonicityStrictly increasing
2023-12-13T05:20:53.048308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
60 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%

노선명
Text

Distinct72
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Memory size868.0 B
2023-12-13T05:20:53.404020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.1847826
Min length1

Characters and Unicode

Total characters477
Distinct characters83
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)62.0%

Sample

1st row무진대로
2nd row임방울대로
3rd row상무대로
4th row용아로
5th row어등대로
ValueCountFrequency (%)
목련로 5
 
5.3%
평동산단로 3
 
3.2%
손재로 3
 
3.2%
평동로 2
 
2.1%
평동산단 2
 
2.1%
왕버들로 2
 
2.1%
장신로 2
 
2.1%
풍영로 2
 
2.1%
평동산단6번로 2
 
2.1%
진곡산단중앙로 2
 
2.1%
Other values (64) 70
73.7%
2023-12-13T05:20:53.943918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 82
 
17.2%
번 28
 
5.9%
산 26
 
5.5%
단 25
 
5.2%
길 19
 
4.0%
동 17
 
3.6%
평 15
 
3.1%
대 10
 
2.1%
3 9
 
1.9%
1 9
 
1.9%
Other values (73) 237
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 414
86.8%
Decimal Number 60
 
12.6%
Space Separator 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 82
19.8%
번 28
 
6.8%
산 26
 
6.3%
단 25
 
6.0%
길 19
 
4.6%
동 17
 
4.1%
평 15
 
3.6%
대 10
 
2.4%
남 8
 
1.9%
안 8
 
1.9%
Other values (62) 176
42.5%
Decimal Number
ValueCountFrequency (%)
3 9
15.0%
1 9
15.0%
2 9
15.0%
6 7
11.7%
9 6
10.0%
0 6
10.0%
8 5
8.3%
7 3
 
5.0%
4 3
 
5.0%
5 3
 
5.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 414
86.8%
Common 63
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 82
19.8%
번 28
 
6.8%
산 26
 
6.3%
단 25
 
6.0%
길 19
 
4.6%
동 17
 
4.1%
평 15
 
3.6%
대 10
 
2.4%
남 8
 
1.9%
안 8
 
1.9%
Other values (62) 176
42.5%
Common
ValueCountFrequency (%)
3 9
14.3%
1 9
14.3%
2 9
14.3%
6 7
11.1%
9 6
9.5%
0 6
9.5%
8 5
7.9%
3
 
4.8%
7 3
 
4.8%
4 3
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 413
86.6%
ASCII 63
 
13.2%
Compat Jamo 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 82
19.9%
번 28
 
6.8%
산 26
 
6.3%
단 25
 
6.1%
길 19
 
4.6%
동 17
 
4.1%
평 15
 
3.6%
대 10
 
2.4%
남 8
 
1.9%
안 8
 
1.9%
Other values (61) 175
42.4%
ASCII
ValueCountFrequency (%)
3 9
14.3%
1 9
14.3%
2 9
14.3%
6 7
11.1%
9 6
9.5%
0 6
9.5%
8 5
7.9%
3
 
4.8%
7 3
 
4.8%
4 3
 
4.8%
Compat Jamo
ValueCountFrequency (%)
ㅡ 1
100.0%

기점
Text

Distinct78
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Memory size868.0 B
2023-12-13T05:20:54.250438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.7826087
Min length3

Characters and Unicode

Total characters716
Distinct characters54
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

Unique68 ?
Unique (%)73.9%

Sample

1st row우산동 1612-1
2nd row우산동 535-4
3rd row우산동 221-42
4th row하남동 739
5th row소촌동 529-2
ValueCountFrequency (%)
월전동 9
 
5.0%
하남동 8
 
4.4%
옥동 7
 
3.9%
신가동 6
 
3.3%
오선동 6
 
3.3%
월곡동 5
 
2.8%
1243 5
 
2.8%
송정동 4
 
2.2%
장덕동 4
 
2.2%
산정동 4
 
2.2%
Other values (94) 122
67.8%
2023-12-13T05:20:54.696540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
 
12.3%
동 85
 
11.9%
1 63
 
8.8%
2 39
 
5.4%
7 39
 
5.4%
3 38
 
5.3%
4 33
 
4.6%
- 31
 
4.3%
9 28
 
3.9%
5 24
 
3.4%
Other values (44) 248
34.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 322
45.0%
Other Letter 275
38.4%
Space Separator 88
 
12.3%
Dash Punctuation 31
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 85
30.9%
월 18
 
6.5%
산 13
 
4.7%
남 11
 
4.0%
신 10
 
3.6%
정 10
 
3.6%
전 10
 
3.6%
선 9
 
3.3%
송 8
 
2.9%
하 8
 
2.9%
Other values (32) 93
33.8%
Decimal Number
ValueCountFrequency (%)
1 63
19.6%
2 39
12.1%
7 39
12.1%
3 38
11.8%
4 33
10.2%
9 28
8.7%
5 24
 
7.5%
6 22
 
6.8%
0 18
 
5.6%
8 18
 
5.6%
Space Separator
ValueCountFrequency (%)
88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 441
61.6%
Hangul 275
38.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 85
30.9%
월 18
 
6.5%
산 13
 
4.7%
남 11
 
4.0%
신 10
 
3.6%
정 10
 
3.6%
전 10
 
3.6%
선 9
 
3.3%
송 8
 
2.9%
하 8
 
2.9%
Other values (32) 93
33.8%
Common
ValueCountFrequency (%)
88
20.0%
1 63
14.3%
2 39
8.8%
7 39
8.8%
3 38
8.6%
4 33
 
7.5%
- 31
 
7.0%
9 28
 
6.3%
5 24
 
5.4%
6 22
 
5.0%
Other values (2) 36
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441
61.6%
Hangul 275
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
88
20.0%
1 63
14.3%
2 39
8.8%
7 39
8.8%
3 38
8.6%
4 33
 
7.5%
- 31
 
7.0%
9 28
 
6.3%
5 24
 
5.4%
6 22
 
5.0%
Other values (2) 36
8.2%
Hangul
ValueCountFrequency (%)
동 85
30.9%
월 18
 
6.5%
산 13
 
4.7%
남 11
 
4.0%
신 10
 
3.6%
정 10
 
3.6%
전 10
 
3.6%
선 9
 
3.3%
송 8
 
2.9%
하 8
 
2.9%
Other values (32) 93
33.8%

종점
Text

Distinct81
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size868.0 B
2023-12-13T05:20:55.038739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.8804348
Min length3

Characters and Unicode

Total characters725
Distinct characters71
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)79.3%

Sample

1st row운수동 233-18
2nd row쌍암동 696
3rd row송정리 1003-9
4th row소촌동 175-3
5th row지평동 137
ValueCountFrequency (%)
월전동 8
 
4.4%
옥동 7
 
3.8%
산정동 7
 
3.8%
오선동 7
 
3.8%
하남동 5
 
2.7%
신가동 5
 
2.7%
수완동 4
 
2.2%
1243 4
 
2.2%
소촌동 3
 
1.6%
송정동 3
 
1.6%
Other values (102) 130
71.0%
2023-12-13T05:20:55.487596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91
 
12.6%
동 86
 
11.9%
1 78
 
10.8%
7 41
 
5.7%
- 31
 
4.3%
2 31
 
4.3%
9 31
 
4.3%
0 27
 
3.7%
3 25
 
3.4%
5 25
 
3.4%
Other values (61) 259
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320
44.1%
Other Letter 282
38.9%
Space Separator 92
 
12.7%
Dash Punctuation 31
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 86
30.5%
산 16
 
5.7%
월 14
 
5.0%
정 11
 
3.9%
선 10
 
3.5%
신 10
 
3.5%
전 8
 
2.8%
오 8
 
2.8%
촌 7
 
2.5%
남 7
 
2.5%
Other values (48) 105
37.2%
Decimal Number
ValueCountFrequency (%)
1 78
24.4%
7 41
12.8%
2 31
 
9.7%
9 31
 
9.7%
0 27
 
8.4%
3 25
 
7.8%
5 25
 
7.8%
4 24
 
7.5%
6 22
 
6.9%
8 16
 
5.0%
Space Separator
ValueCountFrequency (%)
91
98.9%
　 1
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 443
61.1%
Hangul 282
38.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 86
30.5%
산 16
 
5.7%
월 14
 
5.0%
정 11
 
3.9%
선 10
 
3.5%
신 10
 
3.5%
전 8
 
2.8%
오 8
 
2.8%
촌 7
 
2.5%
남 7
 
2.5%
Other values (48) 105
37.2%
Common
ValueCountFrequency (%)
91
20.5%
1 78
17.6%
7 41
9.3%
- 31
 
7.0%
2 31
 
7.0%
9 31
 
7.0%
0 27
 
6.1%
3 25
 
5.6%
5 25
 
5.6%
4 24
 
5.4%
Other values (3) 39
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 442
61.0%
Hangul 282
38.9%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
91
20.6%
1 78
17.6%
7 41
9.3%
- 31
 
7.0%
2 31
 
7.0%
9 31
 
7.0%
0 27
 
6.1%
3 25
 
5.7%
5 25
 
5.7%
4 24
 
5.4%
Other values (2) 38
8.6%
Hangul
ValueCountFrequency (%)
동 86
30.5%
산 16
 
5.7%
월 14
 
5.0%
정 11
 
3.9%
선 10
 
3.5%
신 10
 
3.5%
전 8
 
2.8%
오 8
 
2.8%
촌 7
 
2.5%
남 7
 
2.5%
Other values (48) 105
37.2%
None
ValueCountFrequency (%)
　 1
100.0%

총연장(km)(a_b_c_d_e)
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4334783
Minimum0.2
Maximum18.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T05:20:55.639726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.27
Q10.8075
median1.31
Q33.335
95-th percentile7.1105
Maximum18.01
Range17.81
Interquartile range (IQR)2.5275

Descriptive statistics

Standard deviation2.9828979
Coefficient of variation (CV)1.2257755
Kurtosis14.790702
Mean2.4334783
Median Absolute Deviation (MAD)0.8
Skewness3.3833689
Sum223.88
Variance8.8976801
MonotonicityNot monotonic
2023-12-13T05:20:55.784505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22 2
 
2.2%
0.99 2
 
2.2%
1.13 2
 
2.2%
0.27 2
 
2.2%
1.06 2
 
2.2%
3.35 1
 
1.1%
1.12 1
 
1.1%
0.95 1
 
1.1%
0.3 1
 
1.1%
0.82 1
 
1.1%
Other values (77) 77
83.7%
ValueCountFrequency (%)
0.2 1
1.1%
0.22 2
2.2%
0.23 1
1.1%
0.27 2
2.2%
0.28 1
1.1%
0.3 1
1.1%
0.36 1
1.1%
0.41 1
1.1%
0.43 1
1.1%
0.48 1
1.1%
ValueCountFrequency (%)
18.01 1
1.1%
17.97 1
1.1%
7.64 1
1.1%
7.45 1
1.1%
7.27 1
1.1%
6.98 1
1.1%
6.39 1
1.1%
5.76 1
1.1%
5.7 1
1.1%
5.32 1
1.1%

전용도로(a)_연장(km)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing86
Missing (%)93.5%
Infinite0
Infinite (%)0.0%
Mean9.6266667
Minimum3.99
Maximum18.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T05:20:55.915395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.99
5-th percentile4.2825
Q15.165
median6.315
Q315.34
95-th percentile18
Maximum18.01
Range14.02
Interquartile range (IQR)10.175

Descriptive statistics

Standard deviation6.5745773
Coefficient of variation (CV)0.68295471
Kurtosis-1.901818
Mean9.6266667
Median Absolute Deviation (MAD)1.74
Skewness0.84824563
Sum57.76
Variance43.225067
MonotonicityNot monotonic
2023-12-13T05:20:56.012742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
17.97 1
 
1.1%
5.16 1
 
1.1%
18.01 1
 
1.1%
5.18 1
 
1.1%
3.99 1
 
1.1%
7.45 1
 
1.1%
(Missing) 86
93.5%
ValueCountFrequency (%)
3.99 1
1.1%
5.16 1
1.1%
5.18 1
1.1%
7.45 1
1.1%
17.97 1
1.1%
18.01 1
1.1%
ValueCountFrequency (%)
18.01 1
1.1%
17.97 1
1.1%
7.45 1
1.1%
5.18 1
1.1%
5.16 1
1.1%
3.99 1
1.1%

전용도로(a)_폭원(m)
Text

MISSING 

Distinct5
Distinct (%)83.3%
Missing86
Missing (%)93.5%
Memory size868.0 B
2023-12-13T05:20:56.125842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.3333333
Min length1

Characters and Unicode

Total characters20
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)66.7%

Sample

1st row3.0~6.0
2nd row1.5
3rd row3.2
4th row3.2
5th row2.2
ValueCountFrequency (%)
3.2 2
33.3%
3.0~6.0 1
16.7%
1.5 1
16.7%
2.2 1
16.7%
3 1
16.7%
2023-12-13T05:20:56.363759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 6
30.0%
3 4
20.0%
2 4
20.0%
0 2
 
10.0%
~ 1
 
5.0%
6 1
 
5.0%
1 1
 
5.0%
5 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
65.0%
Other Punctuation 6
30.0%
Math Symbol 1
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4
30.8%
2 4
30.8%
0 2
15.4%
6 1
 
7.7%
1 1
 
7.7%
5 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6
30.0%
3 4
20.0%
2 4
20.0%
0 2
 
10.0%
~ 1
 
5.0%
6 1
 
5.0%
1 1
 
5.0%
5 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 6
30.0%
3 4
20.0%
2 4
20.0%
0 2
 
10.0%
~ 1
 
5.0%
6 1
 
5.0%
1 1
 
5.0%
5 1
 
5.0%

분리형겸용도로(d)_연장(km)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)94.1%
Missing7
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean1.9272941
Minimum0.2
Maximum7.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T05:20:56.524770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.27
Q10.76
median1.21
Q32.65
95-th percentile5.748
Maximum7.64
Range7.44
Interquartile range (IQR)1.89

Descriptive statistics

Standard deviation1.7637726
Coefficient of variation (CV)0.91515487
Kurtosis1.9139579
Mean1.9272941
Median Absolute Deviation (MAD)0.63
Skewness1.5580739
Sum163.82
Variance3.1108938
MonotonicityNot monotonic
2023-12-13T05:20:56.670743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.22 2
 
2.2%
0.99 2
 
2.2%
1.13 2
 
2.2%
0.27 2
 
2.2%
1.06 2
 
2.2%
0.82 1
 
1.1%
0.83 1
 
1.1%
2.1 1
 
1.1%
1.4 1
 
1.1%
0.62 1
 
1.1%
Other values (70) 70
76.1%
(Missing) 7
 
7.6%
ValueCountFrequency (%)
0.2 1
1.1%
0.22 2
2.2%
0.23 1
1.1%
0.27 2
2.2%
0.28 1
1.1%
0.3 1
1.1%
0.36 1
1.1%
0.41 1
1.1%
0.43 1
1.1%
0.48 1
1.1%
ValueCountFrequency (%)
7.64 1
1.1%
7.27 1
1.1%
6.98 1
1.1%
6.39 1
1.1%
5.76 1
1.1%
5.7 1
1.1%
5.32 1
1.1%
4.86 1
1.1%
4.43 1
1.1%
4.05 1
1.1%

분리형겸용도로(d)_자전거도로폭원(m)
Categorical

Distinct22
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Memory size868.0 B
3
25 
1.5
25 
2
7 
<NA>
7 
1.4
5 
Other values (17)
23 

Length

Max length7
Median length3
Mean length2.5108696
Min length1

Unique

Unique13 ?
Unique (%)14.1%

Sample

1st row3.5
2nd row<NA>
3rd row1.1~1.4
4th row1.0~1.3
5th row1.1

Common Values

ValueCountFrequency (%)
3 25
27.2%
1.5 25
27.2%
2 7
 
7.6%
<NA> 7
 
7.6%
1.4 5
 
5.4%
1.3 4
 
4.3%
3.2 2
 
2.2%
1.8 2
 
2.2%
3.5 2
 
2.2%
1.1 1
 
1.1%
Other values (12) 12
13.0%

Length

2023-12-13T05:20:56.829023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 25
27.2%
1.5 25
27.2%
2 7
 
7.6%
na 7
 
7.6%
1.4 5
 
5.4%
1.3 4
 
4.3%
3.2 2
 
2.2%
1.8 2
 
2.2%
3.5 2
 
2.2%
2.3 1
 
1.1%
Other values (12) 12
13.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
Minimum2022-06-12 00:00:00
Maximum2022-06-12 00:00:00
2023-12-13T05:20:56.921396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:57.046637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T05:20:51.375333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:49.894221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.409760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.901041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.484379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.009136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.529884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.014285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.614535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.146990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.665093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.139667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.740642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.282840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:50.784870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:20:51.264612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:20:57.121873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번노선명기점종점총연장(km)(a_b_c_d_e)전용도로(a)_연장(km)전용도로(a)_폭원(m)분리형겸용도로(d)_연장(km)분리형겸용도로(d)_자전거도로폭원(m)
연번1.0000.8190.9350.8910.4640.0001.0000.5060.388
노선명0.8191.0000.9670.9840.4621.0001.0000.0000.000
기점0.9350.9671.0000.9980.9471.0001.0000.9440.954
종점0.8910.9840.9981.0000.9741.0001.0000.9890.974
총연장(km)(a_b_c_d_e)0.4640.4620.9470.9741.0001.0000.6470.9900.463
전용도로(a)_연장(km)0.0001.0001.0001.0001.0001.0000.647NaNNaN
전용도로(a)_폭원(m)1.0001.0001.0001.0000.6470.6471.000NaNNaN
분리형겸용도로(d)_연장(km)0.5060.0000.9440.9890.990NaNNaN1.0000.713
분리형겸용도로(d)_자전거도로폭원(m)0.3880.0000.9540.9740.463NaNNaN0.7131.000
2023-12-13T05:20:57.238873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번총연장(km)(a_b_c_d_e)전용도로(a)_연장(km)분리형겸용도로(d)_연장(km)분리형겸용도로(d)_자전거도로폭원(m)
연번1.000-0.378-0.314-0.5490.131
총연장(km)(a_b_c_d_e)-0.3781.0001.0001.0000.215
전용도로(a)_연장(km)-0.3141.0001.000NaN0.000
분리형겸용도로(d)_연장(km)-0.5491.000NaN1.0000.325
분리형겸용도로(d)_자전거도로폭원(m)0.1310.2150.0000.3251.000

Missing values

2023-12-13T05:20:52.270950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:20:52.481411image/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.
2023-12-13T05:20:52.650976image/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

연번노선명기점종점총연장(km)(a_b_c_d_e)전용도로(a)_연장(km)전용도로(a)_폭원(m)분리형겸용도로(d)_연장(km)분리형겸용도로(d)_자전거도로폭원(m)데이터기준일자
01무진대로우산동 1612-1운수동 233-183.35<NA><NA>3.353.52022-06-12
12임방울대로우산동 535-4쌍암동 69617.9717.973.0~6.0<NA><NA>2022-06-12
23상무대로우산동 221-42송정리 1003-93.95<NA><NA>3.951.1~1.42022-06-12
34용아로하남동 739소촌동 175-36.39<NA><NA>6.391.0~1.32022-06-12
45어등대로소촌동 529-2지평동 1376.98<NA><NA>6.981.12022-06-12
56상무대로송정리 1003-12도산동 1218-572.26<NA><NA>2.262.32022-06-12
67하남대로신가동 95-5하남동 7914.05<NA><NA>4.052.22022-06-12
78동곡로운수동 233-12장록동 752-15.76<NA><NA>5.761.52022-06-12
89하남대로하남동 740하남동 5291.7<NA><NA>1.722022-06-12
910공항로신촌동 687-1송정동 280-11.1<NA><NA>1.12.8~3.02022-06-12
연번노선명기점종점총연장(km)(a_b_c_d_e)전용도로(a)_연장(km)전용도로(a)_폭원(m)분리형겸용도로(d)_연장(km)분리형겸용도로(d)_자전거도로폭원(m)데이터기준일자
8283평동산단803번안길옥동 1243옥동 12430.76<NA><NA>0.761.52022-06-12
8384용아로297번길산정동 1115산정동 11150.5<NA><NA>0.532022-06-12
8485목련로22번길산정동 1115산정동 11150.43<NA><NA>0.4332022-06-12
8586광산로송정동 1148-13신촌동 12050.66<NA><NA>0.661.52022-06-12
8687영산강변우안산월동 442-4승촌보18.0118.013.2<NA><NA>2022-06-12
8788황룡강변안송정2교영산강 합류부5.185.183.2<NA><NA>2022-06-12
8889풍영정천변 좌안도천동 533-8우산동 5293.993.992.2<NA><NA>2022-06-12
8990황룡강변우안송산유원지용진교7.457.453<NA><NA>2022-06-12
9091황룡강변좌안송산유원지박호동 257번지2.3<NA><NA><NA><NA>2022-06-12
9192황룡강변우안용진교오룡교5.32<NA><NA>5.3232022-06-12