Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells458
Missing cells (%)0.7%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory664.1 KiB
Average record size in memory68.0 B

Variable types

Text3
Numeric4

Dataset

Description국토안전관리원에서 제공하는 데이터이며 시설물의안전관리에관한특별법에 의거하여 국토안전관리원에서 운영중인 시설물정보관리종합시스템 내 등록된 공공교량시설물의 주요제원현황을 제공합니다.
URLhttps://www.data.go.kr/data/15017286/fileData.do

Alerts

Dataset has 3 (< 0.1%) duplicate rowsDuplicates
교량연장 is highly overall correlated with 경간수 and 1 other fieldsHigh correlation
경간수 is highly overall correlated with 교량연장High correlation
최대경간수 is highly overall correlated with 교량연장High correlation
교량형식 has 117 (1.2%) missing valuesMissing
차로수 has 254 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-12 08:33:56.970284
Analysis finished2023-12-12 08:34:00.210139
Duration3.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설물명
Text

Distinct8924
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:34:00.499357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length29
Mean length5.247
Min length2

Characters and Unicode

Total characters52470
Distinct characters565
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8262 ?
Unique (%)82.6%

Sample

1st row영단가도교
2nd row지촌교
3rd row장평2교(서천)
4th row송광교
5th row매산교(하남)
ValueCountFrequency (%)
육교 37
 
0.4%
복개구조물 31
 
0.3%
앞 29
 
0.3%
보도육교 24
 
0.2%
신기교 14
 
0.1%
ramp-a교 12
 
0.1%
용두교 11
 
0.1%
오산교 11
 
0.1%
신촌교 10
 
0.1%
금곡교 9
 
0.1%
Other values (9075) 10234
98.2%
2023-12-12T17:34:01.097124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 9873
 
18.8%
) 2823
 
5.4%
( 2822
 
5.4%
천 1699
 
3.2%
1 1365
 
2.6%
2 1173
 
2.2%
산 911
 
1.7%
육 825
 
1.6%
대 711
 
1.4%
상 646
 
1.2%
Other values (555) 29622
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40798
77.8%
Decimal Number 3483
 
6.6%
Close Punctuation 2826
 
5.4%
Open Punctuation 2825
 
5.4%
Uppercase Letter 1845
 
3.5%
Space Separator 422
 
0.8%
Dash Punctuation 155
 
0.3%
Lowercase Letter 54
 
0.1%
Other Punctuation 44
 
0.1%
Math Symbol 16
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 9873
24.2%
천 1699
 
4.2%
산 911
 
2.2%
육 825
 
2.0%
대 711
 
1.7%
상 646
 
1.6%
하 623
 
1.5%
도 578
 
1.4%
동 563
 
1.4%
가 518
 
1.3%
Other values (501) 23851
58.5%
Uppercase Letter
ValueCountFrequency (%)
C 636
34.5%
I 489
26.5%
T 134
 
7.3%
J 133
 
7.2%
A 108
 
5.9%
R 106
 
5.7%
P 67
 
3.6%
M 60
 
3.3%
B 38
 
2.1%
D 27
 
1.5%
Other values (11) 47
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 1365
39.2%
2 1173
33.7%
3 405
 
11.6%
4 174
 
5.0%
0 156
 
4.5%
5 71
 
2.0%
6 47
 
1.3%
8 33
 
0.9%
7 33
 
0.9%
9 26
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 19
43.2%
, 13
29.5%
# 5
 
11.4%
: 3
 
6.8%
/ 2
 
4.5%
· 1
 
2.3%
@ 1
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
a 16
29.6%
m 16
29.6%
p 16
29.6%
c 3
 
5.6%
i 3
 
5.6%
Math Symbol
ValueCountFrequency (%)
~ 14
87.5%
= 1
 
6.2%
+ 1
 
6.2%
Close Punctuation
ValueCountFrequency (%)
) 2823
99.9%
] 3
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 2822
99.9%
[ 3
 
0.1%
Space Separator
ValueCountFrequency (%)
422
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 155
100.0%
Letter Number
ValueCountFrequency (%)
ⅱ 1
100.0%
Other Number
ValueCountFrequency (%)
② 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40797
77.8%
Common 9772
 
18.6%
Latin 1900
 
3.6%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 9873
24.2%
천 1699
 
4.2%
산 911
 
2.2%
육 825
 
2.0%
대 711
 
1.7%
상 646
 
1.6%
하 623
 
1.5%
도 578
 
1.4%
동 563
 
1.4%
가 518
 
1.3%
Other values (500) 23850
58.5%
Common
ValueCountFrequency (%)
) 2823
28.9%
( 2822
28.9%
1 1365
14.0%
2 1173
12.0%
422
 
4.3%
3 405
 
4.1%
4 174
 
1.8%
0 156
 
1.6%
- 155
 
1.6%
5 71
 
0.7%
Other values (17) 206
 
2.1%
Latin
ValueCountFrequency (%)
C 636
33.5%
I 489
25.7%
T 134
 
7.1%
J 133
 
7.0%
A 108
 
5.7%
R 106
 
5.6%
P 67
 
3.5%
M 60
 
3.2%
B 38
 
2.0%
D 27
 
1.4%
Other values (17) 102
 
5.4%
Han
ValueCountFrequency (%)
二 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40797
77.8%
ASCII 11669
 
22.2%
Number Forms 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%
CJK 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 9873
24.2%
천 1699
 
4.2%
산 911
 
2.2%
육 825
 
2.0%
대 711
 
1.7%
상 646
 
1.6%
하 623
 
1.5%
도 578
 
1.4%
동 563
 
1.4%
가 518
 
1.3%
Other values (500) 23850
58.5%
ASCII
ValueCountFrequency (%)
) 2823
24.2%
( 2822
24.2%
1 1365
11.7%
2 1173
10.1%
C 636
 
5.5%
I 489
 
4.2%
422
 
3.6%
3 405
 
3.5%
4 174
 
1.5%
0 156
 
1.3%
Other values (41) 1204
10.3%
Number Forms
ValueCountFrequency (%)
ⅱ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
② 1
100.0%
CJK
ValueCountFrequency (%)
二 1
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

주소
Text

Distinct262
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:34:01.453697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length7
Mean length7.7362
Min length6

Characters and Unicode

Total characters77362
Distinct characters150
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

Unique7 ?
Unique (%)0.1%

Sample

1st row경상북도영주시
2nd row강원특별자치도원주시
3rd row충청남도청양군
4th row경상북도김천시
5th row경기도안성시
ValueCountFrequency (%)
충청북도청주시 174
 
1.6%
강원특별자치도삼척시 158
 
1.4%
경상남도창원시 140
 
1.3%
울산광역시울주군 139
 
1.3%
경상남도진주시 133
 
1.2%
강원특별자치도강릉시 131
 
1.2%
경기도남양주시 126
 
1.1%
경기도용인시 126
 
1.1%
경상남도김해시 112
 
1.0%
경상북도경주시 111
 
1.0%
Other values (258) 9673
87.8%
2023-12-12T17:34:01.985504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 8751
 
11.3%
시 6529
 
8.4%
경 4372
 
5.7%
군 3863
 
5.0%
남 3201
 
4.1%
북 3173
 
4.1%
상 2580
 
3.3%
구 2340
 
3.0%
청 2036
 
2.6%
주 1970
 
2.5%
Other values (140) 38547
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76339
98.7%
Space Separator 1023
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 8751
 
11.5%
시 6529
 
8.6%
경 4372
 
5.7%
군 3863
 
5.1%
남 3201
 
4.2%
북 3173
 
4.2%
상 2580
 
3.4%
구 2340
 
3.1%
청 2036
 
2.7%
주 1970
 
2.6%
Other values (139) 37524
49.2%
Space Separator
ValueCountFrequency (%)
1023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76339
98.7%
Common 1023
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 8751
 
11.5%
시 6529
 
8.6%
경 4372
 
5.7%
군 3863
 
5.1%
남 3201
 
4.2%
북 3173
 
4.2%
상 2580
 
3.4%
구 2340
 
3.1%
청 2036
 
2.7%
주 1970
 
2.6%
Other values (139) 37524
49.2%
Common
ValueCountFrequency (%)
1023
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76339
98.7%
ASCII 1023
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 8751
 
11.5%
시 6529
 
8.6%
경 4372
 
5.7%
군 3863
 
5.1%
남 3201
 
4.2%
북 3173
 
4.2%
상 2580
 
3.4%
구 2340
 
3.1%
청 2036
 
2.7%
주 1970
 
2.6%
Other values (139) 37524
49.2%
ASCII
ValueCountFrequency (%)
1023
100.0%

교량형식
Text

MISSING 

Distinct692
Distinct (%)7.0%
Missing117
Missing (%)1.2%
Memory size156.2 KiB
2023-12-12T17:34:02.403378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length39
Mean length9.9171304
Min length1

Characters and Unicode

Total characters98011
Distinct characters178
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique459 ?
Unique (%)4.6%

Sample

1st rowRCT빔교(RCT)
2nd rowRC슬래브교(RCS)
3rd rowPC빔교(PC I)
4th row라멘교(RA)
5th rowRC중공슬래브교(RCH)
ValueCountFrequency (%)
rc슬래브교(rcs 2557
20.1%
강박스거더교(stb 1480
11.6%
i 1373
10.8%
pc빔교(pc 1238
 
9.7%
라멘교(ra 1122
 
8.8%
프리플렉스빔교(pf 420
 
3.3%
rct빔교(rct 294
 
2.3%
psc 292
 
2.3%
girder 274
 
2.2%
beam 269
 
2.1%
Other values (529) 3389
26.7%
2023-12-12T17:34:02.944771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 10471
 
10.7%
교 8731
 
8.9%
( 8172
 
8.3%
) 8172
 
8.3%
R 7931
 
8.1%
S 5755
 
5.9%
P 5090
 
5.2%
브 2869
 
2.9%
슬 2868
 
2.9%
래 2856
 
2.9%
Other values (168) 35096
35.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 40845
41.7%
Other Letter 35372
36.1%
Open Punctuation 8172
 
8.3%
Close Punctuation 8172
 
8.3%
Space Separator 2830
 
2.9%
Lowercase Letter 1767
 
1.8%
Other Punctuation 602
 
0.6%
Dash Punctuation 139
 
0.1%
Math Symbol 83
 
0.1%
Decimal Number 27
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 8731
24.7%
브 2869
 
8.1%
슬 2868
 
8.1%
래 2856
 
8.1%
스 2232
 
6.3%
빔 2072
 
5.9%
강 1884
 
5.3%
거 1873
 
5.3%
더 1860
 
5.3%
박 1751
 
5.0%
Other values (107) 6376
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 10471
25.6%
R 7931
19.4%
S 5755
14.1%
P 5090
12.5%
B 2438
 
6.0%
T 2363
 
5.8%
I 2155
 
5.3%
A 1451
 
3.6%
E 709
 
1.7%
G 576
 
1.4%
Other values (12) 1906
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
e 443
25.1%
a 216
12.2%
m 203
11.5%
r 203
11.5%
d 103
 
5.8%
i 102
 
5.8%
o 77
 
4.4%
t 66
 
3.7%
l 64
 
3.6%
x 50
 
2.8%
Other values (11) 240
13.6%
Decimal Number
ValueCountFrequency (%)
2 9
33.3%
3 8
29.6%
4 4
14.8%
1 3
 
11.1%
5 2
 
7.4%
0 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 550
91.4%
, 36
 
6.0%
/ 12
 
2.0%
: 4
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 81
97.6%
∏ 1
 
1.2%
~ 1
 
1.2%
Open Punctuation
ValueCountFrequency (%)
( 8172
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8172
100.0%
Space Separator
ValueCountFrequency (%)
2830
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 139
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42607
43.5%
Hangul 35372
36.1%
Common 20027
20.4%
Greek 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 8731
24.7%
브 2869
 
8.1%
슬 2868
 
8.1%
래 2856
 
8.1%
스 2232
 
6.3%
빔 2072
 
5.9%
강 1884
 
5.3%
거 1873
 
5.3%
더 1860
 
5.3%
박 1751
 
5.0%
Other values (107) 6376
18.0%
Latin
ValueCountFrequency (%)
C 10471
24.6%
R 7931
18.6%
S 5755
13.5%
P 5090
11.9%
B 2438
 
5.7%
T 2363
 
5.5%
I 2155
 
5.1%
A 1451
 
3.4%
E 709
 
1.7%
G 576
 
1.4%
Other values (31) 3668
 
8.6%
Common
ValueCountFrequency (%)
( 8172
40.8%
) 8172
40.8%
2830
 
14.1%
. 550
 
2.7%
- 139
 
0.7%
+ 81
 
0.4%
, 36
 
0.2%
/ 12
 
0.1%
2 9
 
< 0.1%
3 8
 
< 0.1%
Other values (8) 18
 
0.1%
Greek
ValueCountFrequency (%)
π 4
80.0%
Π 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62631
63.9%
Hangul 35372
36.1%
None 7
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 10471
16.7%
( 8172
13.0%
) 8172
13.0%
R 7931
12.7%
S 5755
9.2%
P 5090
8.1%
2830
 
4.5%
B 2438
 
3.9%
T 2363
 
3.8%
I 2155
 
3.4%
Other values (47) 7254
11.6%
Hangul
ValueCountFrequency (%)
교 8731
24.7%
브 2869
 
8.1%
슬 2868
 
8.1%
래 2856
 
8.1%
스 2232
 
6.3%
빔 2072
 
5.9%
강 1884
 
5.3%
거 1873
 
5.3%
더 1860
 
5.3%
박 1751
 
5.0%
Other values (107) 6376
18.0%
None
ValueCountFrequency (%)
π 4
57.1%
´ 2
28.6%
Π 1
 
14.3%
Math Operators
ValueCountFrequency (%)
∏ 1
100.0%

교량연장
Real number (ℝ)

HIGH CORRELATION 

Distinct1508
Distinct (%)15.2%
Missing79
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean133.30914
Minimum5
Maximum10556.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:34:03.087603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q130.1
median55
Q3140
95-th percentile440
Maximum10556.7
Range10551.7
Interquartile range (IQR)109.9

Descriptive statistics

Standard deviation287.37102
Coefficient of variation (CV)2.1556737
Kurtosis355.71198
Mean133.30914
Median Absolute Deviation (MAD)31
Skewness14.078893
Sum1322560
Variance82582.101
MonotonicityNot monotonic
2023-12-12T17:34:03.230389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 493
 
4.9%
40.0 323
 
3.2%
20.0 287
 
2.9%
50.0 267
 
2.7%
60.0 224
 
2.2%
120.0 213
 
2.1%
90.0 202
 
2.0%
24.0 197
 
2.0%
45.0 190
 
1.9%
150.0 179
 
1.8%
Other values (1498) 7346
73.5%
ValueCountFrequency (%)
5.0 38
0.4%
5.1 1
 
< 0.1%
5.3 1
 
< 0.1%
5.8 1
 
< 0.1%
5.9 1
 
< 0.1%
6.0 45
0.4%
6.4 1
 
< 0.1%
6.5 2
 
< 0.1%
6.7 1
 
< 0.1%
6.9 2
 
< 0.1%
ValueCountFrequency (%)
10556.7 1
< 0.1%
8428.6 1
< 0.1%
8298.0 1
< 0.1%
4997.0 1
< 0.1%
4850.0 1
< 0.1%
4800.0 1
< 0.1%
3797.0 1
< 0.1%
3635.0 1
< 0.1%
3585.0 1
< 0.1%
3500.0 1
< 0.1%

차로수
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing98
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.4596041
Minimum0
Maximum12
Zeros254
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:34:03.353407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile6
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.47987
Coefficient of variation (CV)0.60167001
Kurtosis4.744182
Mean2.4596041
Median Absolute Deviation (MAD)0
Skewness1.7401136
Sum24355
Variance2.1900153
MonotonicityNot monotonic
2023-12-12T17:34:03.461305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 5329
53.3%
4 1682
 
16.8%
1 1585
 
15.8%
3 403
 
4.0%
6 324
 
3.2%
0 254
 
2.5%
5 144
 
1.4%
8 93
 
0.9%
7 39
 
0.4%
10 27
 
0.3%
Other values (3) 22
 
0.2%
(Missing) 98
 
1.0%
ValueCountFrequency (%)
0 254
 
2.5%
1 1585
 
15.8%
2 5329
53.3%
3 403
 
4.0%
4 1682
 
16.8%
5 144
 
1.4%
6 324
 
3.2%
7 39
 
0.4%
8 93
 
0.9%
9 12
 
0.1%
ValueCountFrequency (%)
12 6
 
0.1%
11 4
 
< 0.1%
10 27
 
0.3%
9 12
 
0.1%
8 93
 
0.9%
7 39
 
0.4%
6 324
 
3.2%
5 144
 
1.4%
4 1682
16.8%
3 403
 
4.0%

경간수
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)0.8%
Missing84
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean4.6055869
Minimum1
Maximum267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:34:03.604463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile12
Maximum267
Range266
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.6297878
Coefficient of variation (CV)1.6566374
Kurtosis249.31364
Mean4.6055869
Median Absolute Deviation (MAD)1
Skewness12.136392
Sum45669
Variance58.213661
MonotonicityNot monotonic
2023-12-12T17:34:03.751716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 2092
20.9%
2 1977
19.8%
1 1874
18.7%
4 1066
10.7%
5 706
 
7.1%
6 576
 
5.8%
7 338
 
3.4%
8 285
 
2.9%
9 227
 
2.3%
10 141
 
1.4%
Other values (74) 634
 
6.3%
ValueCountFrequency (%)
1 1874
18.7%
2 1977
19.8%
3 2092
20.9%
4 1066
10.7%
5 706
 
7.1%
6 576
 
5.8%
7 338
 
3.4%
8 285
 
2.9%
9 227
 
2.3%
10 141
 
1.4%
ValueCountFrequency (%)
267 1
< 0.1%
172 1
< 0.1%
138 1
< 0.1%
136 1
< 0.1%
130 1
< 0.1%
129 1
< 0.1%
127 1
< 0.1%
125 1
< 0.1%
114 1
< 0.1%
113 2
< 0.1%

최대경간수
Real number (ℝ)

HIGH CORRELATION 

Distinct576
Distinct (%)5.8%
Missing80
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean29.194062
Minimum0
Maximum850
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:34:03.892342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q113.4
median26
Q340
95-th percentile60
Maximum850
Range850
Interquartile range (IQR)26.6

Descriptive statistics

Standard deviation24.091287
Coefficient of variation (CV)0.82521188
Kurtosis191.40877
Mean29.194062
Median Absolute Deviation (MAD)13
Skewness8.2910499
Sum289605.1
Variance580.39012
MonotonicityNot monotonic
2023-12-12T17:34:04.019213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 1127
 
11.3%
15.0 615
 
6.2%
50.0 603
 
6.0%
10.0 464
 
4.6%
40.0 411
 
4.1%
35.0 331
 
3.3%
12.0 325
 
3.2%
45.0 297
 
3.0%
25.0 263
 
2.6%
14.0 226
 
2.3%
Other values (566) 5258
52.6%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
2.4 1
 
< 0.1%
2.5 4
 
< 0.1%
2.8 1
 
< 0.1%
3.0 16
0.2%
3.2 3
 
< 0.1%
3.3 2
 
< 0.1%
3.5 6
 
0.1%
3.6 1
 
< 0.1%
3.7 2
 
< 0.1%
ValueCountFrequency (%)
850.0 1
 
< 0.1%
500.0 3
< 0.1%
320.0 1
 
< 0.1%
300.1 1
 
< 0.1%
270.0 1
 
< 0.1%
260.0 1
 
< 0.1%
250.0 1
 
< 0.1%
240.0 1
 
< 0.1%
225.0 1
 
< 0.1%
221.0 1
 
< 0.1%

Interactions

2023-12-12T17:33:59.480198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:58.354880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:58.768391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:59.123073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:59.573989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:58.460054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:58.855214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:59.214216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:59.672556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:58.579819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:58.951460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:59.313576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:59.781366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:58.689374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:59.041806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:33:59.398201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:34:04.095879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교량연장차로수경간수최대경간수
교량연장1.0000.1120.8480.352
차로수0.1121.0000.0750.091
경간수0.8480.0751.0000.196
최대경간수0.3520.0910.1961.000
2023-12-12T17:34:04.184731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교량연장차로수경간수최대경간수
교량연장1.0000.1810.7650.680
차로수0.1811.0000.0270.258
경간수0.7650.0271.0000.105
최대경간수0.6800.2580.1051.000

Missing values

2023-12-12T17:33:59.905394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:34:00.013462image/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.
2023-12-12T17:34:00.131337image/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

시설물명주소교량형식교량연장차로수경간수최대경간수
21992영단가도교경상북도영주시RCT빔교(RCT)8.9118.9
20760지촌교강원특별자치도원주시RC슬래브교(RCS)30.01310.0
21327장평2교(서천)충청남도청양군PC빔교(PC I)60.02230.0
18951송광교경상북도김천시라멘교(RA)25.02212.5
2656매산교(하남)경기도안성시RC중공슬래브교(RCH)108.02619.5
26404가정천교전라북도임실군RC슬래브교(RCS)27.6239.2
15383다대주공입구육교부산광역시사하구강박스거더교(STB)77.00330.0
6227자작4교(상)경기도가평군P.S.C Beam120.02430.0
9944남천과선교경상북도경산시강박스거더교(STB)325.045125.0
22786우암초등학교 앞 보도육교경상남도김해시강박스거더교(STB)22.70122.7
시설물명주소교량형식교량연장차로수경간수최대경간수
27362고방산교강원특별자치도양구군강판형교(SPG)72.02612.0
23903향양교경기도파주시PSC I35.05135.0
3730남사박교(판교)경기도안산시 상록구강박스거더교(STB)290.06655.0
8662개화 RAMP-D교서울특별시강서구강박스거더교(STB)260.02560.0
15081가사2교경상북도포항시 북구RC슬래브교(RCS)35.02313.0
18043동대교충청북도옥천군RC슬래브교(RCS)20.02210.0
16619신방교울산광역시울주군RC박스거더교(RCB)75.01203.8
15265자내실교경상남도산청군라멘교(RA)24.01212.0
13305사동교(인천0)경기도이천시프리플렉스빔교(PF)80.04240.0
19686광원교강원특별자치도홍천군RC슬래브교(RCS)96.02616.0

Duplicate rows

Most frequently occurring

시설물명주소교량형식교량연장차로수경간수최대경간수# duplicates
0사당천복개구조물서울특별시서초구<NA><NA><NA><NA><NA>2
1원효교전라남도곡성군RC슬래브교(RCS)20.02210.02
2주포교충청남도보령시RC슬래브교(RCS)40.04315.02