Overview

Dataset statistics

Number of variables16
Number of observations230
Missing cells25
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.5 KiB
Average record size in memory135.6 B

Variable types

Categorical8
Text1
Numeric7

Dataset

Description이 데이터는 2021년 11월 2일 기준으로 남원시 국도 및 지방도에 대한 도로종류, 노선명, 시설명, 총길이, 총폭, 교통량, 준공년도 등에 대한 데이터 입니다.
Author전라북도 남원시
URLhttps://www.data.go.kr/data/3075515/fileData.do

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
데이터 기준일자 has constant value ""Constant
도로종류 is highly overall correlated with 교통량 and 3 other fieldsHigh correlation
노선명 is highly overall correlated with 교통량 and 3 other fieldsHigh correlation
총길이 is highly overall correlated with 경간수 and 1 other fieldsHigh correlation
총폭 is highly overall correlated with 유효폭High correlation
유효폭 is highly overall correlated with 총폭High correlation
경간수 is highly overall correlated with 총길이High correlation
최대경간장 is highly overall correlated with 총길이 and 1 other fieldsHigh correlation
교통량 is highly overall correlated with 도로종류 and 2 other fieldsHigh correlation
준공년도 is highly overall correlated with 도로종류 and 2 other fieldsHigh correlation
읍면동 is highly overall correlated with 교통량 and 2 other fieldsHigh correlation
상부구조 is highly overall correlated with 최대경간장High correlation
설계하중 is highly overall correlated with 준공년도High correlation
설계하중 is highly imbalanced (63.2%)Imbalance
총폭 has 4 (1.7%) missing valuesMissing
유효폭 has 4 (1.7%) missing valuesMissing
교통량 has 17 (7.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 08:53:04.682180
Analysis finished2023-12-12 08:53:11.813047
Duration7.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로종류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
고속국도
116 
일반국도
61 
지방도
32 
국가지원지방도
21 

Length

Max length7
Median length4
Mean length4.1347826
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고속국도 116
50.4%
일반국도 61
26.5%
지방도 32
 
13.9%
국가지원지방도 21
 
9.1%

Length

2023-12-12T17:53:11.882797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:11.987033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고속국도 116
50.4%
일반국도 61
26.5%
지방도 32
 
13.9%
국가지원지방도 21
 
9.1%

노선명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
고속국도12호선
82 
고속국도27호선
34 
일반국도17호선
29 
국가지원지방도60호선
18 
일반국도24호선
13 
Other values (8)
54 

Length

Max length11
Median length8
Mean length8.273913
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고속국도12호선
2nd row고속국도12호선
3rd row고속국도12호선
4th row고속국도12호선
5th row고속국도12호선

Common Values

ValueCountFrequency (%)
고속국도12호선 82
35.7%
고속국도27호선 34
14.8%
일반국도17호선 29
 
12.6%
국가지원지방도60호선 18
 
7.8%
일반국도24호선 13
 
5.7%
지방도730호선 12
 
5.2%
일반국도13호선 10
 
4.3%
일반국도19호선 9
 
3.9%
지방도721호선 8
 
3.5%
지방도861호선 5
 
2.2%
Other values (3) 10
 
4.3%

Length

2023-12-12T17:53:12.094914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고속국도12호선 82
35.7%
고속국도27호선 34
14.8%
일반국도17호선 29
 
12.6%
국가지원지방도60호선 18
 
7.8%
일반국도24호선 13
 
5.7%
지방도730호선 12
 
5.2%
일반국도13호선 10
 
4.3%
일반국도19호선 9
 
3.9%
지방도721호선 8
 
3.5%
지방도861호선 5
 
2.2%
Other values (3) 10
 
4.3%

시설명
Text

Distinct225
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T17:53:12.378159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.7478261
Min length3

Characters and Unicode

Total characters1322
Distinct characters111
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique220 ?
Unique (%)95.7%

Sample

1st row갈치천1교(광주)
2nd row갈치천1교(대구)
3rd row갈치천2교(광주)
4th row갈치천2교(대구)
5th row갈치천3교(광주)
ValueCountFrequency (%)
신기교 2
 
0.9%
송내교 2
 
0.9%
요천교 2
 
0.9%
화정교 2
 
0.9%
사석교 2
 
0.9%
신촌교(하 1
 
0.4%
안곡육교 1
 
0.4%
신평교 1
 
0.4%
신평ic교 1
 
0.4%
신평1교 1
 
0.4%
Other values (215) 215
93.5%
2023-12-12T17:53:12.778568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 232
17.5%
) 117
 
8.9%
( 117
 
8.9%
천 57
 
4.3%
주 55
 
4.2%
대 48
 
3.6%
광 43
 
3.3%
구 38
 
2.9%
C 30
 
2.3%
남 26
 
2.0%
Other values (101) 559
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 966
73.1%
Close Punctuation 117
 
8.9%
Open Punctuation 117
 
8.9%
Uppercase Letter 68
 
5.1%
Decimal Number 54
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 232
24.0%
천 57
 
5.9%
주 55
 
5.7%
대 48
 
5.0%
광 43
 
4.5%
구 38
 
3.9%
남 26
 
2.7%
동 25
 
2.6%
원 23
 
2.4%
산 20
 
2.1%
Other values (90) 399
41.3%
Decimal Number
ValueCountFrequency (%)
1 21
38.9%
2 20
37.0%
3 7
 
13.0%
4 5
 
9.3%
6 1
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
C 30
44.1%
I 22
32.4%
T 8
 
11.8%
J 8
 
11.8%
Close Punctuation
ValueCountFrequency (%)
) 117
100.0%
Open Punctuation
ValueCountFrequency (%)
( 117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 966
73.1%
Common 288
 
21.8%
Latin 68
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 232
24.0%
천 57
 
5.9%
주 55
 
5.7%
대 48
 
5.0%
광 43
 
4.5%
구 38
 
3.9%
남 26
 
2.7%
동 25
 
2.6%
원 23
 
2.4%
산 20
 
2.1%
Other values (90) 399
41.3%
Common
ValueCountFrequency (%)
) 117
40.6%
( 117
40.6%
1 21
 
7.3%
2 20
 
6.9%
3 7
 
2.4%
4 5
 
1.7%
6 1
 
0.3%
Latin
ValueCountFrequency (%)
C 30
44.1%
I 22
32.4%
T 8
 
11.8%
J 8
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 966
73.1%
ASCII 356
 
26.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 232
24.0%
천 57
 
5.9%
주 55
 
5.7%
대 48
 
5.0%
광 43
 
4.5%
구 38
 
3.9%
남 26
 
2.7%
동 25
 
2.6%
원 23
 
2.4%
산 20
 
2.1%
Other values (90) 399
41.3%
ASCII
ValueCountFrequency (%)
) 117
32.9%
( 117
32.9%
C 30
 
8.4%
I 22
 
6.2%
1 21
 
5.9%
2 20
 
5.6%
T 8
 
2.2%
J 8
 
2.2%
3 7
 
2.0%
4 5
 
1.4%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
전라북도
230 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 230
100.0%

Length

2023-12-12T17:53:12.894265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:12.967486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 230
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
남원시
230 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남원시 230
100.0%

Length

2023-12-12T17:53:13.041759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:13.124014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남원시 230
100.0%

읍면동
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
송동면
32 
대산면
23 
대강면
20 
아영면
16 
식정동
13 
Other values (23)
126 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)1.3%

Sample

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

Common Values

ValueCountFrequency (%)
송동면 32
13.9%
대산면 23
 
10.0%
대강면 20
 
8.7%
아영면 16
 
7.0%
식정동 13
 
5.7%
산동면 13
 
5.7%
주천면 11
 
4.8%
운봉읍 11
 
4.8%
주생면 11
 
4.8%
이백면 10
 
4.3%
Other values (18) 70
30.4%

Length

2023-12-12T17:53:13.210892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송동면 32
13.9%
대산면 23
 
10.0%
대강면 20
 
8.7%
아영면 16
 
7.0%
식정동 13
 
5.7%
산동면 13
 
5.7%
주천면 11
 
4.8%
운봉읍 11
 
4.8%
주생면 11
 
4.8%
이백면 10
 
4.3%
Other values (18) 70
30.4%

총길이
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.151739
Minimum7
Maximum520.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:53:13.335503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9
Q116
median35
Q360
95-th percentile255.675
Maximum520.6
Range513.6
Interquartile range (IQR)44

Descriptive statistics

Standard deviation95.510734
Coefficient of variation (CV)1.4888253
Kurtosis11.171999
Mean64.151739
Median Absolute Deviation (MAD)20
Skewness3.2457247
Sum14754.9
Variance9122.3003
MonotonicityNot monotonic
2023-12-12T17:53:13.479790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.0 14
 
6.1%
40.0 13
 
5.7%
15.0 12
 
5.2%
30.0 12
 
5.2%
10.0 7
 
3.0%
60.0 6
 
2.6%
35.2 6
 
2.6%
35.0 6
 
2.6%
20.0 6
 
2.6%
30.2 5
 
2.2%
Other values (88) 143
62.2%
ValueCountFrequency (%)
7.0 3
 
1.3%
7.5 1
 
0.4%
8.0 5
 
2.2%
8.6 1
 
0.4%
9.0 4
 
1.7%
10.0 7
3.0%
11.2 1
 
0.4%
12.0 14
6.1%
13.0 1
 
0.4%
13.3 1
 
0.4%
ValueCountFrequency (%)
520.6 2
0.9%
520.4 2
0.9%
480.6 2
0.9%
333.2 2
0.9%
326.9 1
0.4%
326.3 1
0.4%
301.0 1
0.4%
264.0 1
0.4%
245.5 1
0.4%
225.6 2
0.9%

총폭
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)31.4%
Missing4
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean14.063274
Minimum2
Maximum69.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:53:13.623150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7.5
Q111
median12.2
Q315.4
95-th percentile24.225
Maximum69.5
Range67.5
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation6.6431268
Coefficient of variation (CV)0.47237412
Kurtosis23.355548
Mean14.063274
Median Absolute Deviation (MAD)1.7
Skewness3.5952214
Sum3178.3
Variance44.131134
MonotonicityNot monotonic
2023-12-12T17:53:13.792965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.8 22
 
9.6%
12.5 18
 
7.8%
11.0 16
 
7.0%
12.6 12
 
5.2%
10.0 10
 
4.3%
12.0 10
 
4.3%
21.0 10
 
4.3%
12.2 9
 
3.9%
8.5 8
 
3.5%
7.5 7
 
3.0%
Other values (61) 104
45.2%
ValueCountFrequency (%)
2.0 1
 
0.4%
5.0 1
 
0.4%
5.9 2
 
0.9%
6.0 1
 
0.4%
7.0 1
 
0.4%
7.5 7
3.0%
8.0 5
2.2%
8.5 8
3.5%
8.6 1
 
0.4%
9.0 3
 
1.3%
ValueCountFrequency (%)
69.5 1
0.4%
47.1 1
0.4%
35.3 1
0.4%
32.0 1
0.4%
27.6 1
0.4%
25.8 2
0.9%
25.4 1
0.4%
25.0 1
0.4%
24.9 1
0.4%
24.7 1
0.4%

유효폭
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct65
Distinct (%)28.8%
Missing4
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean12.556637
Minimum2
Maximum67.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:53:13.950930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.5
Q19
median11.4
Q313.925
95-th percentile21.9
Maximum67.8
Range65.8
Interquartile range (IQR)4.925

Descriptive statistics

Standard deviation6.4466408
Coefficient of variation (CV)0.51340504
Kurtosis26.355645
Mean12.556637
Median Absolute Deviation (MAD)2.4
Skewness3.8355051
Sum2837.8
Variance41.559178
MonotonicityNot monotonic
2023-12-12T17:53:14.093351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.4 69
30.0%
7.0 13
 
5.7%
20.0 11
 
4.8%
11.7 10
 
4.3%
9.0 10
 
4.3%
7.5 8
 
3.5%
10.0 8
 
3.5%
11.0 7
 
3.0%
8.0 5
 
2.2%
6.5 5
 
2.2%
Other values (55) 80
34.8%
ValueCountFrequency (%)
2.0 1
 
0.4%
3.0 2
 
0.9%
4.1 1
 
0.4%
5.1 1
 
0.4%
6.0 3
 
1.3%
6.1 1
 
0.4%
6.5 5
 
2.2%
7.0 13
5.7%
7.1 1
 
0.4%
7.5 8
3.5%
ValueCountFrequency (%)
67.8 1
0.4%
46.5 1
0.4%
31.1 1
0.4%
28.8 1
0.4%
25.8 1
0.4%
25.3 1
0.4%
24.8 1
0.4%
24.0 1
0.4%
23.4 1
0.4%
23.0 1
0.4%

경간수
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2826087
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:53:14.227402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile7
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2141175
Coefficient of variation (CV)0.96999433
Kurtosis7.938538
Mean2.2826087
Median Absolute Deviation (MAD)0
Skewness2.6428395
Sum525
Variance4.9023163
MonotonicityNot monotonic
2023-12-12T17:53:14.364539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 126
54.8%
2 40
 
17.4%
3 23
 
10.0%
4 19
 
8.3%
7 6
 
2.6%
5 6
 
2.6%
8 3
 
1.3%
13 2
 
0.9%
12 2
 
0.9%
9 2
 
0.9%
ValueCountFrequency (%)
1 126
54.8%
2 40
 
17.4%
3 23
 
10.0%
4 19
 
8.3%
5 6
 
2.6%
7 6
 
2.6%
8 3
 
1.3%
9 2
 
0.9%
10 1
 
0.4%
12 2
 
0.9%
ValueCountFrequency (%)
13 2
 
0.9%
12 2
 
0.9%
10 1
 
0.4%
9 2
 
0.9%
8 3
 
1.3%
7 6
 
2.6%
5 6
 
2.6%
4 19
8.3%
3 23
10.0%
2 40
17.4%

최대경간장
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.091304
Minimum5
Maximum73.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:53:14.512969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q112
median25.35
Q335
95-th percentile45
Maximum73.2
Range68.2
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.684215
Coefficient of variation (CV)0.54537679
Kurtosis-0.20589409
Mean25.091304
Median Absolute Deviation (MAD)11.65
Skewness0.50627044
Sum5771
Variance187.25774
MonotonicityNot monotonic
2023-12-12T17:53:14.657156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.0 24
 
10.4%
40.0 20
 
8.7%
15.0 20
 
8.7%
30.0 18
 
7.8%
12.0 17
 
7.4%
10.0 14
 
6.1%
8.0 10
 
4.3%
30.2 8
 
3.5%
9.0 6
 
2.6%
45.0 6
 
2.6%
Other values (47) 87
37.8%
ValueCountFrequency (%)
5.0 1
 
0.4%
6.0 1
 
0.4%
7.0 4
 
1.7%
7.5 1
 
0.4%
8.0 10
4.3%
9.0 6
2.6%
9.4 1
 
0.4%
10.0 14
6.1%
11.0 3
 
1.3%
11.2 1
 
0.4%
ValueCountFrequency (%)
73.2 2
 
0.9%
60.0 2
 
0.9%
47.7 2
 
0.9%
45.7 2
 
0.9%
45.5 2
 
0.9%
45.1 1
 
0.4%
45.0 6
2.6%
44.0 2
 
0.9%
43.0 1
 
0.4%
40.9 2
 
0.9%

상부구조
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
PSCI거더교
99 
라멘교
60 
RC슬래브교
35 
강박스거더교
14 
RCT거더교
11 
Other values (4)
11 

Length

Max length8
Median length7
Mean length5.6782609
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row라멘교
2nd row라멘교
3rd rowPSCI거더교
4th rowPSCI거더교
5th rowPSCI거더교

Common Values

ValueCountFrequency (%)
PSCI거더교 99
43.0%
라멘교 60
26.1%
RC슬래브교 35
 
15.2%
강박스거더교 14
 
6.1%
RCT거더교 11
 
4.8%
프리플렉스거더교 4
 
1.7%
아치교 3
 
1.3%
PSC박스거더교 2
 
0.9%
강플레이트거더교 2
 
0.9%

Length

2023-12-12T17:53:14.837282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:14.987392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
psci거더교 99
43.0%
라멘교 60
26.1%
rc슬래브교 35
 
15.2%
강박스거더교 14
 
6.1%
rct거더교 11
 
4.8%
프리플렉스거더교 4
 
1.7%
아치교 3
 
1.3%
psc박스거더교 2
 
0.9%
강플레이트거더교 2
 
0.9%

설계하중
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
DB-24
191 
DB-18
28 
DB-13.5
 
8
DB-24(성능개선)
 
2
기타
 
1

Length

Max length11
Median length5
Mean length5.1086957
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowDB-24
2nd rowDB-24
3rd rowDB-24
4th rowDB-24
5th rowDB-24

Common Values

ValueCountFrequency (%)
DB-24 191
83.0%
DB-18 28
 
12.2%
DB-13.5 8
 
3.5%
DB-24(성능개선) 2
 
0.9%
기타 1
 
0.4%

Length

2023-12-12T17:53:15.129174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:15.239466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
db-24 191
83.0%
db-18 28
 
12.2%
db-13.5 8
 
3.5%
db-24(성능개선 2
 
0.9%
기타 1
 
0.4%

교통량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)17.8%
Missing17
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean5280.9484
Minimum133
Maximum12372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:53:15.389388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum133
5-th percentile514
Q11804
median6125
Q36705
95-th percentile12044
Maximum12372
Range12239
Interquartile range (IQR)4901

Descriptive statistics

Standard deviation3660.6685
Coefficient of variation (CV)0.69318392
Kurtosis-0.63866037
Mean5280.9484
Median Absolute Deviation (MAD)3038
Skewness0.5275019
Sum1124842
Variance13400494
MonotonicityNot monotonic
2023-12-12T17:53:15.541091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
6213 32
13.9%
6705 25
 
10.9%
12044 22
 
9.6%
5655 11
 
4.8%
3254 11
 
4.8%
302 9
 
3.9%
1180 9
 
3.9%
12372 8
 
3.5%
6125 7
 
3.0%
7830 7
 
3.0%
Other values (28) 72
31.3%
(Missing) 17
 
7.4%
ValueCountFrequency (%)
133 1
 
0.4%
302 9
3.9%
514 2
 
0.9%
551 3
 
1.3%
771 1
 
0.4%
935 3
 
1.3%
1033 2
 
0.9%
1047 2
 
0.9%
1068 2
 
0.9%
1180 9
3.9%
ValueCountFrequency (%)
12372 8
 
3.5%
12044 22
9.6%
9961 4
 
1.7%
7830 7
 
3.0%
6705 25
10.9%
6476 3
 
1.3%
6213 32
13.9%
6125 7
 
3.0%
5655 11
 
4.8%
5052 1
 
0.4%

준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.3174
Minimum1970
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T17:53:15.990357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1984
Q11998
median2011
Q32015
95-th percentile2017
Maximum2017
Range47
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.639758
Coefficient of variation (CV)0.0058015539
Kurtosis0.47960342
Mean2006.3174
Median Absolute Deviation (MAD)4
Skewness-1.2409307
Sum461453
Variance135.48398
MonotonicityNot monotonic
2023-12-12T17:53:16.136510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2015 75
32.6%
2011 26
 
11.3%
2014 15
 
6.5%
2010 13
 
5.7%
2017 13
 
5.7%
2007 11
 
4.8%
1998 9
 
3.9%
1988 7
 
3.0%
1992 6
 
2.6%
1984 6
 
2.6%
Other values (25) 49
21.3%
ValueCountFrequency (%)
1970 1
 
0.4%
1971 1
 
0.4%
1972 1
 
0.4%
1973 1
 
0.4%
1976 1
 
0.4%
1978 2
 
0.9%
1979 1
 
0.4%
1980 2
 
0.9%
1981 1
 
0.4%
1984 6
2.6%
ValueCountFrequency (%)
2017 13
 
5.7%
2016 1
 
0.4%
2015 75
32.6%
2014 15
 
6.5%
2013 1
 
0.4%
2012 1
 
0.4%
2011 26
 
11.3%
2010 13
 
5.7%
2007 11
 
4.8%
2005 5
 
2.2%

데이터 기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2021-11-02
230 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-11-02
2nd row2021-11-02
3rd row2021-11-02
4th row2021-11-02
5th row2021-11-02

Common Values

ValueCountFrequency (%)
2021-11-02 230
100.0%

Length

2023-12-12T17:53:16.277124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:53:16.366027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-11-02 230
100.0%

Interactions

2023-12-12T17:53:10.584738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:05.542887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.306944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.063376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.815453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:08.500252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:09.723886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.700856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:05.645699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.411543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.197780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.898863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:08.624672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:09.820814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.815745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:05.764684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.506741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.303760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.990036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:08.737223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:09.952203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.913517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:05.868924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.619274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.403491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:08.091197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:09.215498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.086585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.998640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:05.986577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.718848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.499053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:08.183246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:09.325449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.215292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:11.104408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.086631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.831507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.611485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:08.285447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:09.461595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.332740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:11.204449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.195191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:06.963496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:07.726407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:08.386102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:09.580066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:53:10.470381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:53:16.434963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류노선명읍면동총길이총폭유효폭경간수최대경간장상부구조설계하중교통량준공년도
도로종류1.0001.0000.8790.2650.4960.4920.2350.4800.6180.4320.7970.761
노선명1.0001.0000.9120.2660.5410.5470.4450.4580.6320.7270.8570.833
읍면동0.8790.9121.0000.2360.5190.4530.4480.4180.5920.5010.8730.807
총길이0.2650.2660.2361.0000.0000.0000.9490.6370.3480.0000.4380.142
총폭0.4960.5410.5190.0001.0000.9950.0000.3020.6320.4520.3860.501
유효폭0.4920.5470.4530.0000.9951.0000.0000.2720.5740.4150.4420.428
경간수0.2350.4450.4480.9490.0000.0001.0000.6820.3690.1530.5820.306
최대경간장0.4800.4580.4180.6370.3020.2720.6821.0000.9070.3790.7790.355
상부구조0.6180.6320.5920.3480.6320.5740.3690.9071.0000.5640.6810.676
설계하중0.4320.7270.5010.0000.4520.4150.1530.3790.5641.0000.4430.884
교통량0.7970.8570.8730.4380.3860.4420.5820.7790.6810.4431.0000.691
준공년도0.7610.8330.8070.1420.5010.4280.3060.3550.6760.8840.6911.000
2023-12-12T17:53:16.577316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류설계하중상부구조읍면동노선명
도로종류1.0000.3650.4430.5830.980
설계하중0.3651.0000.3660.2530.496
상부구조0.4430.3661.0000.2530.330
읍면동0.5830.2530.2531.0000.570
노선명0.9800.4960.3300.5701.000
2023-12-12T17:53:16.711313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총길이총폭유효폭경간수최대경간장교통량준공년도도로종류노선명읍면동상부구조설계하중
총길이1.000-0.042-0.0260.6530.7650.196-0.0050.1200.1210.0860.1780.000
총폭-0.0421.0000.950-0.3360.1980.3710.3570.3610.2830.2100.3980.309
유효폭-0.0260.9501.000-0.3330.2100.4330.3150.3580.2870.1770.3470.280
경간수0.653-0.336-0.3331.0000.081-0.090-0.3640.1100.2020.1240.1720.080
최대경간장0.7650.1980.2100.0811.0000.3910.3260.3240.2140.1600.5260.228
교통량0.1960.3710.433-0.0900.3911.0000.3440.6440.5850.5370.2800.289
준공년도-0.0050.3570.315-0.3640.3260.3441.0000.5660.5380.4260.3900.563
도로종류0.1200.3610.3580.1100.3240.6440.5661.0000.9800.5830.4430.365
노선명0.1210.2830.2870.2020.2140.5850.5380.9801.0000.5700.3300.496
읍면동0.0860.2100.1770.1240.1600.5370.4260.5830.5701.0000.2530.253
상부구조0.1780.3980.3470.1720.5260.2800.3900.4430.3300.2531.0000.366
설계하중0.0000.3090.2800.0800.2280.2890.5630.3650.4960.2530.3661.000

Missing values

2023-12-12T17:53:11.357444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:53:11.578552image/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.
2023-12-12T17:53:11.744549image/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

도로종류노선명시설명시도시군구읍면동총길이총폭유효폭경간수최대경간장상부구조설계하중교통량준공년도데이터 기준일자
0고속국도고속국도12호선갈치천1교(광주)전라북도남원시식정동12.017.316.4112.0라멘교DB-24621320152021-11-02
1고속국도고속국도12호선갈치천1교(대구)전라북도남원시식정동12.015.311.4112.0라멘교DB-24621320152021-11-02
2고속국도고속국도12호선갈치천2교(광주)전라북도남원시식정동45.512.011.4145.5PSCI거더교DB-24621320152021-11-02
3고속국도고속국도12호선갈치천2교(대구)전라북도남원시식정동45.512.211.4145.5PSCI거더교DB-24621320152021-11-02
4고속국도고속국도12호선갈치천3교(광주)전라북도남원시식정동30.412.611.7130.4PSCI거더교DB-24621320152021-11-02
5고속국도고속국도12호선갈치천3교(대구)전라북도남원시식정동30.411.811.4130.4PSCI거더교DB-24621320152021-11-02
6고속국도고속국도12호선감성천교(광주)전라북도남원시대산면40.012.011.4140.0PSCI거더교DB-24670520152021-11-02
7고속국도고속국도12호선감성천교(대구)전라북도남원시대산면40.012.211.4140.0PSCI거더교DB-24670520152021-11-02
8고속국도고속국도12호선강기천교(광주)전라북도남원시이백면70.212.511.4235.0PSCI거더교DB-24621320152021-11-02
9고속국도고속국도12호선강기천교(대구)전라북도남원시이백면70.211.811.4235.0PSCI거더교DB-24621320152021-11-02
도로종류노선명시설명시도시군구읍면동총길이총폭유효폭경간수최대경간장상부구조설계하중교통량준공년도데이터 기준일자
220지방도지방도721호선성남교전라북도남원시보절면20.08.57.1210.0라멘교DB-13.5<NA>19882021-11-02
221지방도지방도730호선송동교전라북도남원시송동면24.08.08.038.0RC슬래브교DB-18125619922021-11-02
222지방도지방도743호선신기교전라북도남원시운봉읍70.0<NA><NA>514.0RC슬래브교DB-1851419922021-11-02
223지방도지방도721호선신파교전라북도남원시보절면23.012.211.0211.5RC슬래브교DB-24<NA>19962021-11-02
224지방도지방도730호선연산교전라북도남원시송동면12.07.06.026.0RC슬래브교DB-18118019852021-11-02
225지방도지방도730호선요천대교전라북도남원시송동면264.010.07.8833.0PSCI거더교DB-18118019922021-11-02
226지방도지방도721호선의황교전라북도남원시보절면18.011.010.029.0라멘교DB-24177719952021-11-02
227지방도지방도745호선입암대교전라북도남원시금지면40.58.57.0313.5RC슬래브교DB-18<NA>19922021-11-02
228지방도지방도861호선학천교전라북도남원시산내면60.0<NA><NA>415.0RC슬래브교DB-24<NA>20032021-11-02
229지방도지방도721호선황벌교전라북도남원시보절면28.011.09.039.4RC슬래브교DB-24<NA>19952021-11-02