Overview

Dataset statistics

Number of variables18
Number of observations173
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.3 KiB
Average record size in memory149.8 B

Variable types

Numeric4
Categorical11
Text3

Dataset

Description전라남도 곡성군 교량현황(도로구분, 하천등급, 하천명, 교량명 등)
Author전라남도 곡성군
URLhttps://www.data.go.kr/data/3074903/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
관리기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 폭 and 2 other fieldsHigh correlation
연장 is highly overall correlated with 경간장High correlation
폭 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
경간장 is highly overall correlated with 연장 and 1 other fieldsHigh correlation
도로구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
노선번호 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
읍면동 is highly overall correlated with 노선번호High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:48:06.598273
Analysis finished2023-12-12 06:48:10.053658
Duration3.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87
Minimum1
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T15:48:10.129946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.6
Q144
median87
Q3130
95-th percentile164.4
Maximum173
Range172
Interquartile range (IQR)86

Descriptive statistics

Standard deviation50.084928
Coefficient of variation (CV)0.57568883
Kurtosis-1.2
Mean87
Median Absolute Deviation (MAD)43
Skewness0
Sum15051
Variance2508.5
MonotonicityStrictly increasing
2023-12-12T15:48:10.268766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
120 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
Other values (163) 163
94.2%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%

도로구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
농도
79 
국도
31 
지방도
24 
고속국도
19 
군도
17 

Length

Max length5
Median length2
Mean length2.4104046
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농도 79
45.7%
국도 31
 
17.9%
지방도 24
 
13.9%
고속국도 19
 
11.0%
군도 17
 
9.8%
지 방 도 3
 
1.7%

Length

2023-12-12T15:48:10.433893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:10.565794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농도 79
44.1%
국도 31
 
17.3%
지방도 24
 
13.4%
고속국도 19
 
10.6%
군도 17
 
9.5%
지 3
 
1.7%
방 3
 
1.7%
도 3
 
1.7%

노선번호
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
840
20 
205
17 
17
13 
101
11 
호남선
11 
Other values (32)
101 

Length

Max length3
Median length3
Mean length2.5953757
Min length1

Unique

Unique7 ?
Unique (%)4.0%

Sample

1st row2
2nd row2
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
840 20
 
11.6%
205 17
 
9.8%
17 13
 
7.5%
101 11
 
6.4%
호남선 11
 
6.4%
25호 8
 
4.6%
27 7
 
4.0%
60 7
 
4.0%
302 7
 
4.0%
203 5
 
2.9%
Other values (27) 67
38.7%

Length

2023-12-12T15:48:10.714971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
840 20
 
11.6%
205 17
 
9.8%
17 13
 
7.5%
101 11
 
6.4%
호남선 11
 
6.4%
25호 8
 
4.6%
27 7
 
4.0%
60 7
 
4.0%
302 7
 
4.0%
13 5
 
2.9%
Other values (27) 67
38.7%

하천등급
Categorical

Distinct12
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
지방하천
110 
소하천
20 
지방하천2급
16 
국가하천
 
7
동계천
 
4
Other values (7)
16 

Length

Max length6
Median length4
Mean length3.9075145
Min length2

Unique

Unique4 ?
Unique (%)2.3%

Sample

1st row지방하천
2nd row지방하천
3rd row지방하천
4th row지방하천
5th row국가하천

Common Values

ValueCountFrequency (%)
지방하천 110
63.6%
소하천 20
 
11.6%
지방하천2급 16
 
9.2%
국가하천 7
 
4.0%
동계천 4
 
2.3%
죽곡천 4
 
2.3%
군도 4
 
2.3%
농도 4
 
2.3%
보성강 1
 
0.6%
교차로 1
 
0.6%
Other values (2) 2
 
1.2%

Length

2023-12-12T15:48:10.878449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방하천 110
63.6%
소하천 20
 
11.6%
지방하천2급 16
 
9.2%
국가하천 7
 
4.0%
동계천 4
 
2.3%
죽곡천 4
 
2.3%
군도 4
 
2.3%
농도 4
 
2.3%
보성강 1
 
0.6%
교차로 1
 
0.6%
Other values (2) 2
 
1.2%

하천명
Text

Distinct54
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T15:48:11.115951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2196532
Min length2

Characters and Unicode

Total characters557
Distinct characters80
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)17.3%

Sample

1st row봉조천
2nd row곡성천
3rd row온수천
4th row온수천
5th row보성강
ValueCountFrequency (%)
옥과천 29
16.8%
곡성천 12
 
6.9%
목사동천 12
 
6.9%
석곡천 10
 
5.8%
지방하천 9
 
5.2%
창정천 8
 
4.6%
입천 7
 
4.0%
온수천 6
 
3.5%
삼기천 6
 
3.5%
오곡천 5
 
2.9%
Other values (44) 69
39.9%
2023-12-12T15:48:11.508331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 155
27.8%
곡 36
 
6.5%
옥 31
 
5.6%
과 30
 
5.4%
성 16
 
2.9%
정 15
 
2.7%
동 13
 
2.3%
하 13
 
2.3%
목 12
 
2.2%
사 12
 
2.2%
Other values (70) 224
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 544
97.7%
Decimal Number 8
 
1.4%
Uppercase Letter 4
 
0.7%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 155
28.5%
곡 36
 
6.6%
옥 31
 
5.7%
과 30
 
5.5%
성 16
 
2.9%
정 15
 
2.8%
동 13
 
2.4%
하 13
 
2.4%
목 12
 
2.2%
사 12
 
2.2%
Other values (63) 211
38.8%
Decimal Number
ValueCountFrequency (%)
1 4
50.0%
2 2
25.0%
5 1
 
12.5%
0 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
G 2
50.0%
T 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 544
97.7%
Common 9
 
1.6%
Latin 4
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 155
28.5%
곡 36
 
6.6%
옥 31
 
5.7%
과 30
 
5.5%
성 16
 
2.9%
정 15
 
2.8%
동 13
 
2.4%
하 13
 
2.4%
목 12
 
2.2%
사 12
 
2.2%
Other values (63) 211
38.8%
Common
ValueCountFrequency (%)
1 4
44.4%
2 2
22.2%
5 1
 
11.1%
0 1
 
11.1%
- 1
 
11.1%
Latin
ValueCountFrequency (%)
G 2
50.0%
T 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 544
97.7%
ASCII 13
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 155
28.5%
곡 36
 
6.6%
옥 31
 
5.7%
과 30
 
5.5%
성 16
 
2.9%
정 15
 
2.8%
동 13
 
2.4%
하 13
 
2.4%
목 12
 
2.2%
사 12
 
2.2%
Other values (63) 211
38.8%
ASCII
ValueCountFrequency (%)
1 4
30.8%
2 2
15.4%
G 2
15.4%
T 2
15.4%
5 1
 
7.7%
0 1
 
7.7%
- 1
 
7.7%

교량명
Text

Distinct159
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T15:48:11.891992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.6069364
Min length3

Characters and Unicode

Total characters624
Distinct characters116
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

Unique145 ?
Unique (%)83.8%

Sample

1st row봉조2교
2nd row이화교
3rd row온수교
4th row염곡교
5th row목사동2교
ValueCountFrequency (%)
봉정교 3
 
1.7%
황산교 2
 
1.2%
칠봉교 2
 
1.2%
송강교 2
 
1.2%
묘천교 2
 
1.2%
현정교 2
 
1.2%
주산교 2
 
1.2%
고달교 2
 
1.2%
덕흥교 2
 
1.2%
신기교 2
 
1.2%
Other values (148) 152
87.9%
2023-12-12T15:48:12.412049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 175
28.0%
1 19
 
3.0%
봉 18
 
2.9%
( 18
 
2.9%
) 18
 
2.9%
2 17
 
2.7%
정 13
 
2.1%
산 12
 
1.9%
곡 10
 
1.6%
명 10
 
1.6%
Other values (106) 314
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 542
86.9%
Decimal Number 42
 
6.7%
Open Punctuation 18
 
2.9%
Close Punctuation 18
 
2.9%
Uppercase Letter 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 175
32.3%
봉 18
 
3.3%
정 13
 
2.4%
산 12
 
2.2%
곡 10
 
1.8%
명 10
 
1.8%
동 10
 
1.8%
평 10
 
1.8%
천 10
 
1.8%
대 9
 
1.7%
Other values (98) 265
48.9%
Decimal Number
ValueCountFrequency (%)
1 19
45.2%
2 17
40.5%
3 4
 
9.5%
4 2
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 542
86.9%
Common 78
 
12.5%
Latin 4
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 175
32.3%
봉 18
 
3.3%
정 13
 
2.4%
산 12
 
2.2%
곡 10
 
1.8%
명 10
 
1.8%
동 10
 
1.8%
평 10
 
1.8%
천 10
 
1.8%
대 9
 
1.7%
Other values (98) 265
48.9%
Common
ValueCountFrequency (%)
1 19
24.4%
( 18
23.1%
) 18
23.1%
2 17
21.8%
3 4
 
5.1%
4 2
 
2.6%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 542
86.9%
ASCII 82
 
13.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 175
32.3%
봉 18
 
3.3%
정 13
 
2.4%
산 12
 
2.2%
곡 10
 
1.8%
명 10
 
1.8%
동 10
 
1.8%
평 10
 
1.8%
천 10
 
1.8%
대 9
 
1.7%
Other values (98) 265
48.9%
ASCII
ValueCountFrequency (%)
1 19
23.2%
( 18
22.0%
) 18
22.0%
2 17
20.7%
3 4
 
4.9%
C 2
 
2.4%
I 2
 
2.4%
4 2
 
2.4%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
전라남도
173 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 173
100.0%

Length

2023-12-12T15:48:12.548249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:12.651782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 173
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
곡성군
173 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
곡성군 173
100.0%

Length

2023-12-12T15:48:12.798574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:12.939783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
곡성군 173
100.0%

읍면동
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
곡성읍
23 
석곡면
22 
목사동면
20 
오곡면
19 
옥과면
18 
Other values (5)
71 

Length

Max length4
Median length3
Mean length2.9364162
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row오곡면
2nd row오곡면
3rd row석곡면
4th row석곡면
5th row목사동면

Common Values

ValueCountFrequency (%)
곡성읍 23
13.3%
석곡면 22
12.7%
목사동면 20
11.6%
오곡면 19
11.0%
옥과면 18
10.4%
입면 17
9.8%
죽곡면 17
9.8%
오산면 16
9.2%
겸면 14
8.1%
고달면 7
 
4.0%

Length

2023-12-12T15:48:13.092604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:13.270199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
곡성읍 23
13.3%
석곡면 22
12.7%
목사동면 20
11.6%
오곡면 19
11.0%
옥과면 18
10.4%
입면 17
9.8%
죽곡면 17
9.8%
오산면 16
9.2%
겸면 14
8.1%
고달면 7
 
4.0%

리
Text

Distinct77
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T15:48:13.552719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9710983
Min length2

Characters and Unicode

Total characters514
Distinct characters84
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

Unique29 ?
Unique (%)16.8%

Sample

1st row봉조리
2nd row오지리
3rd row온수리
4th row염곡리
5th row공북리
ValueCountFrequency (%)
오지리 7
 
4.0%
죽정리 6
 
3.5%
연반리 6
 
3.5%
연화리 5
 
2.9%
읍내리 5
 
2.9%
죽림리 5
 
2.9%
월봉리 4
 
2.3%
신기리 4
 
2.3%
봉정리 4
 
2.3%
용봉리 4
 
2.3%
Other values (66) 123
71.1%
2023-12-12T15:48:14.038230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 174
33.9%
봉 27
 
5.3%
정 21
 
4.1%
죽 14
 
2.7%
연 11
 
2.1%
월 11
 
2.1%
곡 10
 
1.9%
오 10
 
1.9%
평 9
 
1.8%
석 8
 
1.6%
Other values (74) 219
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 513
99.8%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 174
33.9%
봉 27
 
5.3%
정 21
 
4.1%
죽 14
 
2.7%
연 11
 
2.1%
월 11
 
2.1%
곡 10
 
1.9%
오 10
 
1.9%
평 9
 
1.8%
석 8
 
1.6%
Other values (73) 218
42.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 513
99.8%
Common 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 174
33.9%
봉 27
 
5.3%
정 21
 
4.1%
죽 14
 
2.7%
연 11
 
2.1%
월 11
 
2.1%
곡 10
 
1.9%
오 10
 
1.9%
평 9
 
1.8%
석 8
 
1.6%
Other values (73) 218
42.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 513
99.8%
ASCII 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 174
33.9%
봉 27
 
5.3%
정 21
 
4.1%
죽 14
 
2.7%
연 11
 
2.1%
월 11
 
2.1%
곡 10
 
1.9%
오 10
 
1.9%
평 9
 
1.8%
석 8
 
1.6%
Other values (73) 218
42.5%
ASCII
ValueCountFrequency (%)
1
100.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.325838
Minimum8
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T15:48:14.236329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12
Q120
median30.5
Q362
95-th percentile178.84
Maximum450
Range442
Interquartile range (IQR)42

Descriptive statistics

Standard deviation69.187984
Coefficient of variation (CV)1.2283525
Kurtosis14.389739
Mean56.325838
Median Absolute Deviation (MAD)14.5
Skewness3.4255659
Sum9744.37
Variance4786.9772
MonotonicityNot monotonic
2023-12-12T15:48:14.380781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 11
 
6.4%
20.0 11
 
6.4%
16.0 9
 
5.2%
40.0 6
 
3.5%
60.0 5
 
2.9%
25.0 5
 
2.9%
12.0 5
 
2.9%
10.0 5
 
2.9%
15.0 4
 
2.3%
120.0 4
 
2.3%
Other values (75) 108
62.4%
ValueCountFrequency (%)
8.0 1
 
0.6%
9.0 1
 
0.6%
10.0 5
2.9%
11.0 1
 
0.6%
12.0 5
2.9%
12.5 1
 
0.6%
13.0 2
 
1.2%
13.6 1
 
0.6%
14.0 2
 
1.2%
15.0 4
2.3%
ValueCountFrequency (%)
450.0 1
0.6%
440.0 1
0.6%
400.0 1
0.6%
270.0 1
0.6%
260.0 1
0.6%
230.0 1
0.6%
200.0 1
0.6%
192.1 2
1.2%
170.0 2
1.2%
166.0 1
0.6%

폭
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.348613
Minimum3.5
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T15:48:14.585351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile4.92
Q16
median9
Q312.4
95-th percentile24.21
Maximum60
Range56.5
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation7.7530119
Coefficient of variation (CV)0.68316825
Kurtosis8.5686687
Mean11.348613
Median Absolute Deviation (MAD)3
Skewness2.2756497
Sum1963.31
Variance60.109193
MonotonicityNot monotonic
2023-12-12T15:48:14.753160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10.0 18
 
10.4%
5.0 16
 
9.2%
6.0 13
 
7.5%
24.21 13
 
7.5%
8.0 12
 
6.9%
9.0 9
 
5.2%
19.5 7
 
4.0%
5.5 5
 
2.9%
7.5 5
 
2.9%
6.5 5
 
2.9%
Other values (39) 70
40.5%
ValueCountFrequency (%)
3.5 2
 
1.2%
4.0 4
 
2.3%
4.7 1
 
0.6%
4.8 2
 
1.2%
5.0 16
9.2%
5.05 1
 
0.6%
5.2 1
 
0.6%
5.5 5
 
2.9%
5.6 1
 
0.6%
5.7 2
 
1.2%
ValueCountFrequency (%)
60.0 1
 
0.6%
37.41 1
 
0.6%
29.0 1
 
0.6%
28.31 2
 
1.2%
26.6 1
 
0.6%
25.5 1
 
0.6%
24.21 13
7.5%
23.4 1
 
0.6%
21.5 1
 
0.6%
21.0 3
 
1.7%

경간장
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.681618
Minimum4
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T15:48:14.908400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.602
Q110
median13
Q320.5
95-th percentile40.256
Maximum50
Range46
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation10.375677
Coefficient of variation (CV)0.62198263
Kurtosis1.5468327
Mean16.681618
Median Absolute Deviation (MAD)3
Skewness1.4846987
Sum2885.92
Variance107.65467
MonotonicityNot monotonic
2023-12-12T15:48:15.077925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 27
 
15.6%
15.0 12
 
6.9%
8.0 11
 
6.4%
12.0 8
 
4.6%
13.0 6
 
3.5%
25.0 6
 
3.5%
30.0 5
 
2.9%
14.0 5
 
2.9%
11.0 5
 
2.9%
32.0 3
 
1.7%
Other values (66) 85
49.1%
ValueCountFrequency (%)
4.0 1
 
0.6%
5.0 1
 
0.6%
5.5 1
 
0.6%
6.0 3
1.7%
6.25 1
 
0.6%
6.3 1
 
0.6%
6.5 1
 
0.6%
6.67 1
 
0.6%
6.7 1
 
0.6%
6.8 1
 
0.6%
ValueCountFrequency (%)
50.0 3
1.7%
46.0 1
 
0.6%
45.0 2
1.2%
43.3 1
 
0.6%
42.5 1
 
0.6%
40.64 1
 
0.6%
40.0 2
1.2%
34.0 2
1.2%
33.3 1
 
0.6%
32.5 1
 
0.6%

설계하중
Categorical

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
24.0
86 
18.0
40 
13.5
22 
9.0
19 
<NA>
 
6

Length

Max length4
Median length4
Mean length3.8901734
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13.5
2nd row18.0
3rd row24.0
4th row18.0
5th row24.0

Common Values

ValueCountFrequency (%)
24.0 86
49.7%
18.0 40
23.1%
13.5 22
 
12.7%
9.0 19
 
11.0%
<NA> 6
 
3.5%

Length

2023-12-12T15:48:15.209566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:15.347846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
24.0 86
49.7%
18.0 40
23.1%
13.5 22
 
12.7%
9.0 19
 
11.0%
na 6
 
3.5%

등급
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
C
84 
B
73 
A
16 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 84
48.6%
B 73
42.2%
A 16
 
9.2%

Length

2023-12-12T15:48:15.486954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:15.615497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 84
48.6%
b 73
42.2%
a 16
 
9.2%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
061-360-8464
173 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row061-360-8464
2nd row061-360-8464
3rd row061-360-8464
4th row061-360-8464
5th row061-360-8464

Common Values

ValueCountFrequency (%)
061-360-8464 173
100.0%

Length

2023-12-12T15:48:15.747423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:15.882404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
061-360-8464 173
100.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
전라남도 곡성군청
173 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도 곡성군청
2nd row전라남도 곡성군청
3rd row전라남도 곡성군청
4th row전라남도 곡성군청
5th row전라남도 곡성군청

Common Values

ValueCountFrequency (%)
전라남도 곡성군청 173
100.0%

Length

2023-12-12T15:48:15.996195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:16.141600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 173
50.0%
곡성군청 173
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2017-03-31
173 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-03-31
2nd row2017-03-31
3rd row2017-03-31
4th row2017-03-31
5th row2017-03-31

Common Values

ValueCountFrequency (%)
2017-03-31 173
100.0%

Length

2023-12-12T15:48:16.268221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:16.386334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-03-31 173
100.0%

Interactions

2023-12-12T15:48:09.259082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.704454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.158654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.854833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.354336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.809819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.264086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.953898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.429855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.918912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.656647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.058065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.551022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.043179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.744056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.159001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:48:16.731263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번도로구분노선번호하천등급하천명읍면동리연장폭경간장설계하중등급
연번1.0000.9380.9470.5220.8290.8670.8760.2730.6600.5310.6300.291
도로구분0.9381.0000.9700.5220.8080.6110.8790.3140.6370.3910.4390.290
노선번호0.9470.9701.0000.0000.2910.8980.9630.0000.6530.5050.7180.298
하천등급0.5220.5220.0001.0000.9870.7180.9720.4660.6330.2470.4560.218
하천명0.8290.8080.2910.9871.0000.9870.9910.0000.9320.0000.4970.217
읍면동0.8670.6110.8980.7180.9871.0001.0000.3480.4200.3590.4570.299
리0.8760.8790.9630.9720.9911.0001.0000.8110.8190.0000.7870.434
연장0.2730.3140.0000.4660.0000.3480.8111.0000.0000.6560.1940.065
폭0.6600.6370.6530.6330.9320.4200.8190.0001.0000.3860.4280.155
경간장0.5310.3910.5050.2470.0000.3590.0000.6560.3861.0000.6210.490
설계하중0.6300.4390.7180.4560.4970.4570.7870.1940.4280.6211.0000.303
등급0.2910.2900.2980.2180.2170.2990.4340.0650.1550.4900.3031.000
2023-12-12T15:48:16.882337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설계하중노선번호하천등급등급도로구분읍면동
설계하중1.0000.4060.2190.2900.2950.284
노선번호0.4061.0000.0000.1340.7640.539
하천등급0.2190.0001.0000.0950.2260.401
등급0.2900.1340.0951.0000.1230.183
도로구분0.2950.7640.2260.1231.0000.374
읍면동0.2840.5390.4010.1830.3741.000
2023-12-12T15:48:17.002630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번연장폭경간장도로구분노선번호하천등급읍면동설계하중등급
연번1.0000.2660.6890.4380.8320.6590.2440.4400.4210.158
연장0.2661.0000.3540.6900.1780.0000.2140.1720.0850.037
폭0.6890.3541.0000.5690.4450.3010.3670.2250.3050.103
경간장0.4380.6900.5691.0000.2150.1810.1030.1160.4160.331
도로구분0.8320.1780.4450.2151.0000.7640.2260.3740.2950.123
노선번호0.6590.0000.3010.1810.7641.0000.0000.5390.4060.134
하천등급0.2440.2140.3670.1030.2260.0001.0000.4010.2190.095
읍면동0.4400.1720.2250.1160.3740.5390.4011.0000.2840.183
설계하중0.4210.0850.3050.4160.2950.4060.2190.2841.0000.290
등급0.1580.0370.1030.3310.1230.1340.0950.1830.2901.000

Missing values

2023-12-12T15:48:09.705811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:48:09.959448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번도로구분노선번호하천등급하천명교량명시도명시군구명읍면동리연장폭경간장설계하중등급관리기관전화번호관리기관명데이터기준일자
01군도2지방하천봉조천봉조2교전라남도곡성군오곡면봉조리16.05.08.013.5C061-360-8464전라남도 곡성군청2017-03-31
12군도2지방하천곡성천이화교전라남도곡성군오곡면오지리71.88.014.3618.0C061-360-8464전라남도 곡성군청2017-03-31
23군도3지방하천온수천온수교전라남도곡성군석곡면온수리30.010.010.024.0B061-360-8464전라남도 곡성군청2017-03-31
34군도3지방하천온수천염곡교전라남도곡성군석곡면염곡리21.08.010.018.0C061-360-8464전라남도 곡성군청2017-03-31
45군도4국가하천보성강목사동2교전라남도곡성군목사동면공북리192.110.032.024.0C061-360-8464전라남도 곡성군청2017-03-31
56군도5소하천신기천신기교전라남도곡성군목사동면신기리10.05.010.013.5C061-360-8464전라남도 곡성군청2017-03-31
67군도5지방하천대곡천대신교전라남도곡성군목사동면대곡리17.05.08.513.5C061-360-8464전라남도 곡성군청2017-03-31
78군도6지방하천창정천삼오교전라남도곡성군입면삼오리23.08.611.518.0B061-360-8464전라남도 곡성군청2017-03-31
89군도6소하천운교천운교2교전라남도곡성군겸면운교리24.06.06.013.5C061-360-8464전라남도 곡성군청2017-03-31
910군도6지방하천삼기천송강교전라남도곡성군겸면송강리52.09.013.024.0C061-360-8464전라남도 곡성군청2017-03-31
연번도로구분노선번호하천등급하천명교량명시도명시군구명읍면동리연장폭경간장설계하중등급관리기관전화번호관리기관명데이터기준일자
163164고속국도호남선농도구봉길유정교전라남도곡성군석곡면유정리15.6160.015.6124.0B061-360-8464전라남도 곡성군청2017-03-31
164165고속국도호남선지방하천백록천봉암교전라남도곡성군석곡면봉전리46.023.423.024.0C061-360-8464전라남도 곡성군청2017-03-31
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