Overview

Dataset statistics

Number of variables18
Number of observations3864
Missing cells2131
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory573.7 KiB
Average record size in memory152.0 B

Variable types

Categorical6
Text4
Numeric8

Dataset

Description2017년 12월말 기준 경남도내 교량 현황입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15055128

Alerts

시도 has constant value ""Constant
교장 is highly overall correlated with 교고 and 2 other fieldsHigh correlation
총폭 is highly overall correlated with 유효폭 and 1 other fieldsHigh correlation
유효폭 is highly overall correlated with 총폭 and 1 other fieldsHigh correlation
교고 is highly overall correlated with 교장 and 1 other fieldsHigh correlation
경간수 is highly overall correlated with 교장High correlation
최대경간장 is highly overall correlated with 교장 and 2 other fieldsHigh correlation
교통량 is highly overall correlated with 총폭 and 1 other fieldsHigh correlation
상부구조 is highly overall correlated with 최대경간장High correlation
설계하중 is highly imbalanced (68.4%)Imbalance
노선명 has 1280 (33.1%) missing valuesMissing
has 415 (10.7%) missing valuesMissing
교통량 has 402 (10.4%) missing valuesMissing
교통량 has 201 (5.2%) zerosZeros

Reproduction

Analysis started2023-12-11 00:37:47.262668
Analysis finished2023-12-11 00:37:56.797291
Duration9.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로종류
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
고속국도
1088 
일반국도
871 
시도
855 
지방도
468 
군도
429 

Length

Max length7
Median length4
Mean length3.3330745
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고속국도 1088
28.2%
일반국도 871
22.5%
시도 855
22.1%
지방도 468
12.1%
군도 429
 
11.1%
국가지원지방도 153
 
4.0%

Length

2023-12-11T09:37:56.866768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:37:56.995335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고속국도 1088
28.2%
일반국도 871
22.5%
시도 855
22.1%
지방도 468
12.1%
군도 429
 
11.1%
국가지원지방도 153
 
4.0%

노선명
Text

MISSING 

Distinct76
Distinct (%)2.9%
Missing1280
Missing (%)33.1%
Memory size30.3 KiB
2023-12-11T09:37:57.212904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.2345201
Min length2

Characters and Unicode

Total characters21278
Distinct characters24
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

Unique3 ?
Unique (%)0.1%

Sample

1st row일반국도14호선
2nd row일반국도14호선
3rd row일반국도14호선
4th row일반국도14호선
5th row일반국도14호선
ValueCountFrequency (%)
고속국도10호선 328
 
12.7%
고속국도35호선 270
 
10.4%
일반국도2호선 153
 
5.9%
고속국도55호선 123
 
4.8%
일반국도3호선 115
 
4.5%
일반국도33호선 110
 
4.3%
일반국도14호선 100
 
3.9%
고속국도12호선 93
 
3.6%
일반국도5호선 81
 
3.1%
일반국도24호선 74
 
2.9%
Other values (66) 1137
44.0%
2023-12-11T09:37:57.552616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2584
12.1%
2580
12.1%
2580
12.1%
2112
9.9%
1 1337
 
6.3%
0 1192
 
5.6%
1088
 
5.1%
1088
 
5.1%
5 918
 
4.3%
871
 
4.1%
Other values (14) 4928
23.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15479
72.7%
Decimal Number 5799
 
27.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2584
16.7%
2580
16.7%
2580
16.7%
2112
13.6%
1088
7.0%
1088
7.0%
871
 
5.6%
871
 
5.6%
774
 
5.0%
621
 
4.0%
Other values (4) 310
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 1337
23.1%
0 1192
20.6%
5 918
15.8%
3 776
13.4%
2 567
9.8%
4 387
 
6.7%
7 195
 
3.4%
9 185
 
3.2%
8 139
 
2.4%
6 103
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15479
72.7%
Common 5799
 
27.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2584
16.7%
2580
16.7%
2580
16.7%
2112
13.6%
1088
7.0%
1088
7.0%
871
 
5.6%
871
 
5.6%
774
 
5.0%
621
 
4.0%
Other values (4) 310
 
2.0%
Common
ValueCountFrequency (%)
1 1337
23.1%
0 1192
20.6%
5 918
15.8%
3 776
13.4%
2 567
9.8%
4 387
 
6.7%
7 195
 
3.4%
9 185
 
3.2%
8 139
 
2.4%
6 103
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15479
72.7%
ASCII 5799
 
27.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2584
16.7%
2580
16.7%
2580
16.7%
2112
13.6%
1088
7.0%
1088
7.0%
871
 
5.6%
871
 
5.6%
774
 
5.0%
621
 
4.0%
Other values (4) 310
 
2.0%
ASCII
ValueCountFrequency (%)
1 1337
23.1%
0 1192
20.6%
5 918
15.8%
3 776
13.4%
2 567
9.8%
4 387
 
6.7%
7 195
 
3.4%
9 185
 
3.2%
8 139
 
2.4%
6 103
 
1.8%
Distinct3434
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
2023-12-11T09:37:57.871468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length4.9477226
Min length2

Characters and Unicode

Total characters19118
Distinct characters351
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3141 ?
Unique (%)81.3%

Sample

1st row계룡산교
2nd row고현1교
3rd row고현2교
4th row고현3교
5th row고현천교
ValueCountFrequency (%)
신기교 9
 
0.2%
신촌교 8
 
0.2%
송정교 7
 
0.2%
오산교 6
 
0.2%
정동교 6
 
0.2%
대천교 6
 
0.2%
대곡교 6
 
0.2%
상평교 6
 
0.2%
운곡교 6
 
0.2%
두곡교 5
 
0.1%
Other values (3435) 3833
98.3%
2023-12-11T09:37:58.390501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3892
 
20.4%
( 1122
 
5.9%
) 1122
 
5.9%
655
 
3.4%
556
 
2.9%
1 490
 
2.6%
2 445
 
2.3%
C 340
 
1.8%
326
 
1.7%
292
 
1.5%
Other values (341) 9878
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14658
76.7%
Decimal Number 1204
 
6.3%
Open Punctuation 1122
 
5.9%
Close Punctuation 1122
 
5.9%
Uppercase Letter 919
 
4.8%
Dash Punctuation 40
 
0.2%
Space Separator 34
 
0.2%
Lowercase Letter 12
 
0.1%
Other Punctuation 5
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3892
26.6%
655
 
4.5%
556
 
3.8%
326
 
2.2%
292
 
2.0%
279
 
1.9%
274
 
1.9%
261
 
1.8%
243
 
1.7%
231
 
1.6%
Other values (304) 7649
52.2%
Uppercase Letter
ValueCountFrequency (%)
C 340
37.0%
I 257
28.0%
J 74
 
8.1%
T 74
 
8.1%
R 39
 
4.2%
A 38
 
4.1%
B 23
 
2.5%
P 17
 
1.8%
M 16
 
1.7%
D 13
 
1.4%
Other values (7) 28
 
3.0%
Decimal Number
ValueCountFrequency (%)
1 490
40.7%
2 445
37.0%
3 141
 
11.7%
4 52
 
4.3%
5 29
 
2.4%
6 19
 
1.6%
7 12
 
1.0%
8 7
 
0.6%
9 5
 
0.4%
0 4
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
p 4
33.3%
m 4
33.3%
a 4
33.3%
Other Punctuation
ValueCountFrequency (%)
. 4
80.0%
/ 1
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 1122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1122
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14658
76.7%
Common 3529
 
18.5%
Latin 931
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3892
26.6%
655
 
4.5%
556
 
3.8%
326
 
2.2%
292
 
2.0%
279
 
1.9%
274
 
1.9%
261
 
1.8%
243
 
1.7%
231
 
1.6%
Other values (304) 7649
52.2%
Latin
ValueCountFrequency (%)
C 340
36.5%
I 257
27.6%
J 74
 
7.9%
T 74
 
7.9%
R 39
 
4.2%
A 38
 
4.1%
B 23
 
2.5%
P 17
 
1.8%
M 16
 
1.7%
D 13
 
1.4%
Other values (10) 40
 
4.3%
Common
ValueCountFrequency (%)
( 1122
31.8%
) 1122
31.8%
1 490
13.9%
2 445
 
12.6%
3 141
 
4.0%
4 52
 
1.5%
- 40
 
1.1%
34
 
1.0%
5 29
 
0.8%
6 19
 
0.5%
Other values (7) 35
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14658
76.7%
ASCII 4460
 
23.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3892
26.6%
655
 
4.5%
556
 
3.8%
326
 
2.2%
292
 
2.0%
279
 
1.9%
274
 
1.9%
261
 
1.8%
243
 
1.7%
231
 
1.6%
Other values (304) 7649
52.2%
ASCII
ValueCountFrequency (%)
( 1122
25.2%
) 1122
25.2%
1 490
11.0%
2 445
 
10.0%
C 340
 
7.6%
I 257
 
5.8%
3 141
 
3.2%
J 74
 
1.7%
T 74
 
1.7%
4 52
 
1.2%
Other values (27) 343
 
7.7%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
경상남도
3864 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 3864
100.0%

Length

2023-12-11T09:37:58.510020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:37:58.594642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 3864
100.0%

시군구
Categorical

Distinct24
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
창원시
693 
김해시
502 
진주시
360 
양산시
243 
함양군
240 
Other values (19)
1826 

Length

Max length4
Median length3
Mean length3.0007764
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 693
17.9%
김해시 502
13.0%
진주시 360
9.3%
양산시 243
 
6.3%
함양군 240
 
6.2%
합천군 213
 
5.5%
함안군 211
 
5.5%
산청군 186
 
4.8%
밀양시 177
 
4.6%
고성군 161
 
4.2%
Other values (14) 878
22.7%

Length

2023-12-11T09:37:58.919942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 693
17.9%
김해시 502
13.0%
진주시 360
9.3%
양산시 243
 
6.3%
함양군 240
 
6.2%
합천군 213
 
5.5%
함안군 211
 
5.5%
산청군 186
 
4.8%
밀양시 177
 
4.6%
고성군 161
 
4.2%
Other values (14) 878
22.7%
Distinct287
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
2023-12-11T09:37:59.194047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1565735
Min length2

Characters and Unicode

Total characters12197
Distinct characters157
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.8%

Sample

1st row장평동
2nd row상동동
3rd row상동동
4th row상동동
5th row문동동
ValueCountFrequency (%)
의창구 179
 
4.6%
마산합포구 178
 
4.6%
마산회원구 127
 
3.3%
성산구 120
 
3.1%
대동면 87
 
2.3%
진해구 87
 
2.3%
상동면 53
 
1.4%
주촌면 51
 
1.3%
생림면 48
 
1.2%
진례면 46
 
1.2%
Other values (277) 2888
74.7%
2023-12-11T09:37:59.608116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2395
 
19.6%
774
 
6.3%
707
 
5.8%
651
 
5.3%
364
 
3.0%
339
 
2.8%
260
 
2.1%
238
 
2.0%
236
 
1.9%
232
 
1.9%
Other values (147) 6001
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12197
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2395
 
19.6%
774
 
6.3%
707
 
5.8%
651
 
5.3%
364
 
3.0%
339
 
2.8%
260
 
2.1%
238
 
2.0%
236
 
1.9%
232
 
1.9%
Other values (147) 6001
49.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12197
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2395
 
19.6%
774
 
6.3%
707
 
5.8%
651
 
5.3%
364
 
3.0%
339
 
2.8%
260
 
2.1%
238
 
2.0%
236
 
1.9%
232
 
1.9%
Other values (147) 6001
49.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12197
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2395
 
19.6%
774
 
6.3%
707
 
5.8%
651
 
5.3%
364
 
3.0%
339
 
2.8%
260
 
2.1%
238
 
2.0%
236
 
1.9%
232
 
1.9%
Other values (147) 6001
49.2%


Text

MISSING 

Distinct934
Distinct (%)27.1%
Missing415
Missing (%)10.7%
Memory size30.3 KiB
2023-12-11T09:37:59.880719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9449116
Min length2

Characters and Unicode

Total characters10157
Distinct characters248
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

Unique325 ?
Unique (%)9.4%

Sample

1st row사곡리
2nd row사곡리
3rd row사등리
4th row사등리
5th row사등리
ValueCountFrequency (%)
내서읍 50
 
1.4%
동읍 46
 
1.3%
북면 43
 
1.2%
진북면 40
 
1.2%
진전면 34
 
1.0%
대감리 29
 
0.8%
덕산리 26
 
0.8%
대산면 24
 
0.7%
석계리 19
 
0.6%
구암동 19
 
0.6%
Other values (924) 3119
90.4%
2023-12-11T09:38:00.272156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2758
27.2%
580
 
5.7%
355
 
3.5%
212
 
2.1%
199
 
2.0%
164
 
1.6%
158
 
1.6%
139
 
1.4%
132
 
1.3%
129
 
1.3%
Other values (238) 5331
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10142
99.9%
Decimal Number 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2758
27.2%
580
 
5.7%
355
 
3.5%
212
 
2.1%
199
 
2.0%
164
 
1.6%
158
 
1.6%
139
 
1.4%
132
 
1.3%
129
 
1.3%
Other values (237) 5316
52.4%
Decimal Number
ValueCountFrequency (%)
1 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10142
99.9%
Common 15
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2758
27.2%
580
 
5.7%
355
 
3.5%
212
 
2.1%
199
 
2.0%
164
 
1.6%
158
 
1.6%
139
 
1.4%
132
 
1.3%
129
 
1.3%
Other values (237) 5316
52.4%
Common
ValueCountFrequency (%)
1 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10142
99.9%
ASCII 15
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2758
27.2%
580
 
5.7%
355
 
3.5%
212
 
2.1%
199
 
2.0%
164
 
1.6%
158
 
1.6%
139
 
1.4%
132
 
1.3%
129
 
1.3%
Other values (237) 5316
52.4%
ASCII
ValueCountFrequency (%)
1 15
100.0%

교장
Real number (ℝ)

HIGH CORRELATION 

Distinct736
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.288872
Minimum2.3
Maximum2145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-12-11T09:38:00.388480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile8.5
Q115
median31
Q380
95-th percentile340
Maximum2145
Range2142.7
Interquartile range (IQR)65

Descriptive statistics

Standard deviation150.21854
Coefficient of variation (CV)1.8035847
Kurtosis38.32461
Mean83.288872
Median Absolute Deviation (MAD)19
Skewness5.0695402
Sum321828.2
Variance22565.609
MonotonicityNot monotonic
2023-12-11T09:38:00.495159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 176
 
4.6%
30.0 123
 
3.2%
12.0 123
 
3.2%
10.0 94
 
2.4%
50.0 79
 
2.0%
40.0 78
 
2.0%
20.0 65
 
1.7%
8.0 65
 
1.7%
24.0 59
 
1.5%
45.0 56
 
1.4%
Other values (726) 2946
76.2%
ValueCountFrequency (%)
2.3 1
 
< 0.1%
2.4 1
 
< 0.1%
3.0 3
0.1%
3.4 1
 
< 0.1%
3.7 1
 
< 0.1%
4.0 3
0.1%
4.1 1
 
< 0.1%
4.5 1
 
< 0.1%
4.7 1
 
< 0.1%
4.8 2
0.1%
ValueCountFrequency (%)
2145.0 1
< 0.1%
2018.0 1
< 0.1%
1700.0 1
< 0.1%
1479.7 1
< 0.1%
1422.4 2
0.1%
1320.0 2
0.1%
1290.0 1
< 0.1%
1240.0 1
< 0.1%
1160.0 2
0.1%
1148.0 1
< 0.1%

총폭
Real number (ℝ)

HIGH CORRELATION 

Distinct320
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.179684
Minimum2
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-12-11T09:38:00.596463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median12
Q319.4
95-th percentile28.685
Maximum80
Range78
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation7.6674118
Coefficient of variation (CV)0.5407322
Kurtosis8.7947153
Mean14.179684
Median Absolute Deviation (MAD)3.85
Skewness2.097611
Sum54790.3
Variance58.789204
MonotonicityNot monotonic
2023-12-11T09:38:00.713109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5 249
 
6.4%
9.0 218
 
5.6%
8.0 189
 
4.9%
12.1 177
 
4.6%
10.0 155
 
4.0%
19.5 125
 
3.2%
21.0 109
 
2.8%
12.6 98
 
2.5%
12.2 96
 
2.5%
12.0 90
 
2.3%
Other values (310) 2358
61.0%
ValueCountFrequency (%)
2.0 1
 
< 0.1%
2.5 5
0.1%
2.6 1
 
< 0.1%
3.0 6
0.2%
3.1 1
 
< 0.1%
3.3 2
 
0.1%
3.4 1
 
< 0.1%
3.5 10
0.3%
3.6 1
 
< 0.1%
3.8 2
 
0.1%
ValueCountFrequency (%)
80.0 1
< 0.1%
77.8 1
< 0.1%
76.5 1
< 0.1%
75.0 1
< 0.1%
73.0 1
< 0.1%
66.0 2
0.1%
61.3 1
< 0.1%
60.0 2
0.1%
51.4 1
< 0.1%
50.7 1
< 0.1%

유효폭
Real number (ℝ)

HIGH CORRELATION 

Distinct284
Distinct (%)7.4%
Missing15
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean12.228605
Minimum0
Maximum80
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-12-11T09:38:00.833533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q315.7
95-th percentile23.24
Maximum80
Range80
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation6.5595026
Coefficient of variation (CV)0.53640646
Kurtosis11.044127
Mean12.228605
Median Absolute Deviation (MAD)3.5
Skewness2.1170727
Sum47067.9
Variance43.027074
MonotonicityNot monotonic
2023-12-11T09:38:00.947999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.4 286
 
7.4%
8.0 282
 
7.3%
7.0 216
 
5.6%
9.0 180
 
4.7%
14.0 151
 
3.9%
11.7 151
 
3.9%
9.5 109
 
2.8%
18.5 107
 
2.8%
10.0 97
 
2.5%
20.0 88
 
2.3%
Other values (274) 2182
56.5%
ValueCountFrequency (%)
0.0 3
 
0.1%
2.0 2
 
0.1%
2.4 1
 
< 0.1%
2.5 9
 
0.2%
2.6 1
 
< 0.1%
3.0 11
 
0.3%
3.1 4
 
0.1%
3.2 3
 
0.1%
3.4 2
 
0.1%
3.5 31
0.8%
ValueCountFrequency (%)
80.0 1
< 0.1%
76.8 1
< 0.1%
75.0 1
< 0.1%
73.0 1
< 0.1%
48.6 1
< 0.1%
46.7 1
< 0.1%
45.3 1
< 0.1%
44.7 1
< 0.1%
44.2 1
< 0.1%
43.6 1
< 0.1%

교고
Real number (ℝ)

HIGH CORRELATION 

Distinct275
Distinct (%)7.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.5890758
Minimum0
Maximum109.7
Zeros11
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-12-11T09:38:01.059597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13.5
median5.5
Q38.5
95-th percentile21.2
Maximum109.7
Range109.7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.1493
Coefficient of variation (CV)0.94205146
Kurtosis25.107025
Mean7.5890758
Median Absolute Deviation (MAD)2.4
Skewness3.759915
Sum29316.6
Variance51.112491
MonotonicityNot monotonic
2023-12-11T09:38:01.166333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 294
 
7.6%
3.0 234
 
6.1%
2.0 184
 
4.8%
4.0 138
 
3.6%
6.0 135
 
3.5%
2.5 118
 
3.1%
3.5 103
 
2.7%
7.0 103
 
2.7%
4.5 85
 
2.2%
10.0 71
 
1.8%
Other values (265) 2398
62.1%
ValueCountFrequency (%)
0.0 11
0.3%
0.8 1
 
< 0.1%
1.0 5
0.1%
1.1 2
 
0.1%
1.2 4
 
0.1%
1.3 3
 
0.1%
1.4 2
 
0.1%
1.5 11
0.3%
1.6 3
 
0.1%
1.7 6
0.2%
ValueCountFrequency (%)
109.7 1
< 0.1%
92.0 1
< 0.1%
64.0 1
< 0.1%
56.5 1
< 0.1%
54.2 1
< 0.1%
54.0 2
0.1%
52.0 2
0.1%
51.5 2
0.1%
48.7 1
< 0.1%
47.1 1
< 0.1%

경간수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)0.7%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.049469
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-12-11T09:38:01.265350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum76
Range75
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.25296
Coefficient of variation (CV)1.06673
Kurtosis82.883113
Mean3.049469
Median Absolute Deviation (MAD)1
Skewness5.8997594
Sum11774
Variance10.581749
MonotonicityNot monotonic
2023-12-11T09:38:01.361479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1379
35.7%
2 819
21.2%
3 683
17.7%
4 318
 
8.2%
5 191
 
4.9%
6 120
 
3.1%
7 75
 
1.9%
8 61
 
1.6%
9 58
 
1.5%
10 38
 
1.0%
Other values (18) 119
 
3.1%
ValueCountFrequency (%)
1 1379
35.7%
2 819
21.2%
3 683
17.7%
4 318
 
8.2%
5 191
 
4.9%
6 120
 
3.1%
7 75
 
1.9%
8 61
 
1.6%
9 58
 
1.5%
10 38
 
1.0%
ValueCountFrequency (%)
76 1
 
< 0.1%
45 1
 
< 0.1%
34 1
 
< 0.1%
29 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 9
0.2%
23 1
 
< 0.1%
21 2
 
0.1%
20 2
 
0.1%

최대경간장
Real number (ℝ)

HIGH CORRELATION 

Distinct326
Distinct (%)8.5%
Missing15
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean23.597038
Minimum1
Maximum475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-12-11T09:38:01.472449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.34
Q111
median15
Q330.1
95-th percentile55
Maximum475
Range474
Interquartile range (IQR)19.1

Descriptive statistics

Standard deviation22.371172
Coefficient of variation (CV)0.94804998
Kurtosis98.423378
Mean23.597038
Median Absolute Deviation (MAD)7
Skewness6.7933225
Sum90825
Variance500.46932
MonotonicityNot monotonic
2023-12-11T09:38:01.595027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 342
 
8.9%
15.0 277
 
7.2%
12.0 228
 
5.9%
50.0 212
 
5.5%
10.0 195
 
5.0%
8.0 126
 
3.3%
11.0 119
 
3.1%
35.0 96
 
2.5%
14.0 94
 
2.4%
13.0 89
 
2.3%
Other values (316) 2071
53.6%
ValueCountFrequency (%)
1.0 1
 
< 0.1%
2.3 1
 
< 0.1%
2.4 2
 
0.1%
2.9 1
 
< 0.1%
3.0 18
0.5%
3.1 1
 
< 0.1%
3.2 4
 
0.1%
3.3 4
 
0.1%
3.4 19
0.5%
3.5 6
 
0.2%
ValueCountFrequency (%)
475.0 1
< 0.1%
404.0 1
< 0.1%
400.0 1
< 0.1%
280.0 1
< 0.1%
230.0 2
0.1%
202.0 1
< 0.1%
200.0 1
< 0.1%
190.0 1
< 0.1%
182.0 1
< 0.1%
180.0 1
< 0.1%

상부구조
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
라멘교
1353 
RC슬래브교
794 
PSC I형교
743 
강상자형교
478 
RC T형교
 
112
Other values (13)
384 

Length

Max length9
Median length8
Mean length4.9748965
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
라멘교 1353
35.0%
RC슬래브교 794
20.5%
PSC I형교 743
19.2%
강상자형교 478
 
12.4%
RC T형교 112
 
2.9%
프리플렉스형교 86
 
2.2%
PSC상자형교 81
 
2.1%
RC상자형교 48
 
1.2%
기타 47
 
1.2%
PSC슬래브교 29
 
0.8%
Other values (8) 93
 
2.4%

Length

2023-12-11T09:38:01.746446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
라멘교 1353
28.7%
rc슬래브교 794
16.8%
psc 743
15.7%
i형교 743
15.7%
강상자형교 478
 
10.1%
rc 112
 
2.4%
t형교 112
 
2.4%
프리플렉스형교 86
 
1.8%
psc상자형교 81
 
1.7%
rc상자형교 48
 
1.0%
Other values (10) 169
 
3.6%

하부구조
Categorical

Distinct18
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
기타
1161 
T형 교각식
636 
역 T형식교대
570 
라멘식
416 
벽식
322 
Other values (13)
759 

Length

Max length7
Median length6
Mean length3.9011387
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row기타
2nd rowT형 교각식
3rd rowT형 교각식
4th row역 T형식교대
5th row기타

Common Values

ValueCountFrequency (%)
기타 1161
30.0%
T형 교각식 636
16.5%
역 T형식교대 570
14.8%
라멘식 416
 
10.8%
벽식 322
 
8.3%
중력식 284
 
7.3%
중력식교대 124
 
3.2%
반중력식 83
 
2.1%
구주식 67
 
1.7%
반중력식교대 60
 
1.6%
Other values (8) 141
 
3.6%

Length

2023-12-11T09:38:01.877577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 1161
22.8%
교각식 642
12.6%
t형 636
12.5%
570
11.2%
t형식교대 570
11.2%
라멘식 416
 
8.2%
벽식 322
 
6.3%
중력식 284
 
5.6%
중력식교대 124
 
2.4%
반중력식 83
 
1.6%
Other values (11) 275
 
5.4%

설계하중
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
DB-24
3075 
DB-18
490 
DB-13.5
 
249
미상
 
23
DB-24(성능개선)
 
13
Other values (4)
 
14

Length

Max length11
Median length5
Mean length5.1270704
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
DB-24 3075
79.6%
DB-18 490
 
12.7%
DB-13.5 249
 
6.4%
미상 23
 
0.6%
DB-24(성능개선) 13
 
0.3%
기타 9
 
0.2%
T12 또는 D12 2
 
0.1%
DB-9 2
 
0.1%
T9 또는 D9 1
 
< 0.1%

Length

2023-12-11T09:38:01.983716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:02.083160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
db-24 3075
79.5%
db-18 490
 
12.7%
db-13.5 249
 
6.4%
미상 23
 
0.6%
db-24(성능개선 13
 
0.3%
기타 9
 
0.2%
또는 3
 
0.1%
t12 2
 
0.1%
d12 2
 
0.1%
db-9 2
 
0.1%
Other values (2) 2
 
0.1%

교통량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct989
Distinct (%)28.6%
Missing402
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean18427.281
Minimum0
Maximum72984
Zeros201
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-12-11T09:38:02.205812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11335
median7192
Q323679
95-th percentile72984
Maximum72984
Range72984
Interquartile range (IQR)22344

Descriptive statistics

Standard deviation22937.563
Coefficient of variation (CV)1.2447611
Kurtosis0.43920351
Mean18427.281
Median Absolute Deviation (MAD)6824
Skewness1.3103663
Sum63795247
Variance5.2613181 × 108
MonotonicityNot monotonic
2023-12-11T09:38:02.340000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 201
 
5.2%
72984 185
 
4.8%
23679 181
 
4.7%
70581 164
 
4.2%
50510 160
 
4.1%
19917 89
 
2.3%
43113 71
 
1.8%
42192 29
 
0.8%
21868 20
 
0.5%
9467 19
 
0.5%
Other values (979) 2343
60.6%
(Missing) 402
 
10.4%
ValueCountFrequency (%)
0 201
5.2%
1 1
 
< 0.1%
10 3
 
0.1%
15 3
 
0.1%
20 5
 
0.1%
30 2
 
0.1%
31 1
 
< 0.1%
50 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
ValueCountFrequency (%)
72984 185
4.8%
70964 3
 
0.1%
70581 164
4.2%
62042 1
 
< 0.1%
56700 8
 
0.2%
56544 6
 
0.2%
56204 6
 
0.2%
54000 1
 
< 0.1%
52567 1
 
< 0.1%
52189 1
 
< 0.1%

준공년도
Real number (ℝ)

Distinct65
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2039.8476
Minimum0
Maximum8888
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.1 KiB
2023-12-11T09:38:02.467505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1979
Q11994
median2001
Q32007
95-th percentile2015
Maximum8888
Range8888
Interquartile range (IQR)13

Descriptive statistics

Standard deviation531.08616
Coefficient of variation (CV)0.26035581
Kurtosis161.91267
Mean2039.8476
Median Absolute Deviation (MAD)6
Skewness12.756608
Sum7881971
Variance282052.51
MonotonicityNot monotonic
2023-12-11T09:38:02.579218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 242
 
6.3%
1998 222
 
5.7%
1996 222
 
5.7%
2001 214
 
5.5%
2006 186
 
4.8%
2015 181
 
4.7%
2011 144
 
3.7%
1992 144
 
3.7%
1995 131
 
3.4%
2004 131
 
3.4%
Other values (55) 2047
53.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
1927 1
 
< 0.1%
1934 3
0.1%
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1942 1
 
< 0.1%
1943 3
0.1%
1956 1
 
< 0.1%
1961 1
 
< 0.1%
1963 2
0.1%
ValueCountFrequency (%)
8888 23
 
0.6%
2017 41
 
1.1%
2016 23
 
0.6%
2015 181
4.7%
2014 95
2.5%
2013 74
1.9%
2012 74
1.9%
2011 144
3.7%
2010 120
3.1%
2009 81
2.1%

Interactions

2023-12-11T09:37:55.395200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:49.301252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.134159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:51.020617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.202312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.976900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.773227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.543923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:55.499119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:49.418879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.228802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:51.117711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.286103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.063174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.857768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.627872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:55.616285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:49.534526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.396919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:51.254412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.377611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.186827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.955673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.751075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:55.717757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:49.635588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.497119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:51.374722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.483997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.287949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.070234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.874176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:55.826220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:49.716376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.587305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:51.491685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.571971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.381266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.167302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.965087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:55.961441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:49.838242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.697463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:51.599752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.664055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.473980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.278247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:55.062562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:56.100951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:49.933173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.794917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:51.704425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.754104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.564678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.366952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:55.151796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:56.197137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.045031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:50.916360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:51.825698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:52.878569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:53.674016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:54.464546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:37:55.274066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:38:02.662636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류노선명시군구교장총폭유효폭교고경간수최대경간장상부구조하부구조설계하중교통량준공년도
도로종류1.0001.0000.6980.1660.5030.3820.2490.0820.2060.5700.5440.4170.6680.199
노선명1.0001.0000.9440.2240.5500.5470.3280.2260.4560.7960.7200.6660.9320.241
시군구0.6980.9441.0000.1690.2410.2750.3290.0350.2950.2990.3570.2050.6570.100
교장0.1660.2240.1691.0000.0870.0490.6280.7560.6410.6380.4350.0370.1840.000
총폭0.5030.5500.2410.0871.0000.8750.1420.0660.0620.3050.3680.3210.5650.000
유효폭0.3820.5470.2750.0490.8751.0000.1330.0430.0000.2760.3350.3380.3880.036
교고0.2490.3280.3290.6280.1420.1331.0000.2680.7300.5200.4070.1010.2830.000
경간수0.0820.2260.0350.7560.0660.0430.2681.0000.2060.3120.2130.0000.1630.000
최대경간장0.2060.4560.2950.6410.0620.0000.7300.2061.0000.8430.5380.0920.1760.000
상부구조0.5700.7960.2990.6380.3050.2760.5200.3120.8431.0000.8200.4750.3880.041
하부구조0.5440.7200.3570.4350.3680.3350.4070.2130.5380.8201.0000.5020.4080.057
설계하중0.4170.6660.2050.0370.3210.3380.1010.0000.0920.4750.5021.0000.2800.679
교통량0.6680.9320.6570.1840.5650.3880.2830.1630.1760.3880.4080.2801.0000.070
준공년도0.1990.2410.1000.0000.0000.0360.0000.0000.0000.0410.0570.6790.0701.000
2023-12-11T09:38:02.792460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류상부구조하부구조설계하중시군구
도로종류1.0000.2640.2480.2220.360
상부구조0.2641.0000.3170.1760.088
하부구조0.2480.3171.0000.1890.107
설계하중0.2220.1760.1891.0000.079
시군구0.3600.0880.1070.0791.000
2023-12-11T09:38:02.895033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교장총폭유효폭교고경간수최대경간장교통량준공년도도로종류시군구상부구조하부구조설계하중
교장1.0000.1330.1290.6200.7310.7910.1930.2270.0830.0650.2650.1580.012
총폭0.1331.0000.9480.344-0.1060.3070.5550.3480.2930.0920.1200.1490.152
유효폭0.1290.9481.0000.353-0.1180.3130.5720.3460.2720.1130.1340.1540.156
교고0.6200.3440.3531.0000.2640.7080.4520.4010.1410.1380.2480.1830.049
경간수0.731-0.106-0.1180.2641.0000.219-0.094-0.1210.0460.0000.1430.0960.000
최대경간장0.7910.3070.3130.7080.2191.0000.3980.4630.1160.1260.5620.2600.045
교통량0.1930.5550.5720.452-0.0940.3981.0000.2020.4310.3090.1590.1690.131
준공년도0.2270.3480.3460.401-0.1210.4630.2021.0000.0840.0450.0180.0260.389
도로종류0.0830.2930.2720.1410.0460.1160.4310.0841.0000.3600.2640.2480.222
시군구0.0650.0920.1130.1380.0000.1260.3090.0450.3601.0000.0880.1070.079
상부구조0.2650.1200.1340.2480.1430.5620.1590.0180.2640.0881.0000.3170.176
하부구조0.1580.1490.1540.1830.0960.2600.1690.0260.2480.1070.3171.0000.189
설계하중0.0120.1520.1560.0490.0000.0450.1310.3890.2220.0790.1760.1891.000

Missing values

2023-12-11T09:37:56.322873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:37:56.564245image/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.
2023-12-11T09:37:56.715679image/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

도로종류노선명시설명시도시군구읍면동교장총폭유효폭교고경간수최대경간장상부구조하부구조설계하중교통량준공년도
0일반국도일반국도14호선계룡산교경상남도거제시장평동<NA>13.520.020.05.0113.5라멘교기타DB-24154242015
1일반국도일반국도14호선고현1교경상남도거제시상동동<NA>230.020.920.05.0550.0강상자형교T형 교각식DB-24154242015
2일반국도일반국도14호선고현2교경상남도거제시상동동<NA>150.020.920.05.0350.0강상자형교T형 교각식DB-24154242015
3일반국도일반국도14호선고현3교경상남도거제시상동동<NA>50.020.920.05.0150.0강상자형교역 T형식교대DB-24154242015
4일반국도일반국도14호선고현천교경상남도거제시문동동<NA>100.030.829.422.5340.0강상자형교기타DB-24358262013
5일반국도일반국도14호선문동1교경상남도거제시양정동<NA>315.020.919.416.0935.0PSC I형교π형 교각DB-24358262013
6일반국도일반국도14호선문동2교경상남도거제시상동동<NA>50.020.920.05.0150.0강상자형교역 T형식교대DB-24358262016
7일반국도일반국도14호선문동3교경상남도거제시상동동<NA>300.020.920.05.0650.0강상자형교T형 교각식DB-24154242015
8일반국도일반국도14호선사곡교경상남도거제시사등면사곡리11.419.518.54.0111.4라멘교기타DB-24562041994
9일반국도일반국도14호선사곡입체교경상남도거제시사등면사곡리12.422.820.04.5112.4라멘교기타DB-24562041998
도로종류노선명시설명시도시군구읍면동교장총폭유효폭교고경간수최대경간장상부구조하부구조설계하중교통량준공년도
3854군도<NA>중문2교경상남도합천군삼가면문송리8.64.14.13.018.6RC슬래브교역 T형식교대DB-13.52561990
3855군도<NA>진읍교경상남도합천군율곡면갑산리12.06.25.92.0112.0RC T형교중력식교대DB-13.54221985
3856군도<NA>청현2교경상남도합천군가야면청현리9.45.04.55.019.4라멘교기타DB-99681977
3857군도<NA>택계교경상남도합천군용주면황계리22.57.56.73.0211.5라멘교기타DB-243122004
3858군도<NA>평촌교경상남도합천군청덕면운봉리12.08.67.53.026.0라멘교기타DB-245241996
3859군도<NA>포두교경상남도합천군덕곡면포두리24.06.45.06.0212.0RC슬래브교T형 교각식DB-18<NA>1987
3860군도<NA>하남1교경상남도합천군초계면상대리9.75.55.02.019.7RC T형교중력식교대DB-24501987
3861군도<NA>하용계1교경상남도합천군합천읍용계리7.98.47.82.017.9RC슬래브교중력식교대DB-13.5721968
3862군도<NA>황계교경상남도합천군용주면황계리19.05.04.72.037.0RC슬래브교중력식DB-13.55101975
3863군도<NA>회양교경상남도합천군대병면회양리39.09.58.06.0313.0RC슬래브교라멘식DB-1818091987