Overview

Dataset statistics

Number of variables29
Number of observations5573
Missing cells1313
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory243.0 B

Variable types

Numeric11
Text10
Categorical8

Dataset

Description어도(魚道)는 강과 하천에 설치된 보나 댐 같은 물의 흐름을 막는 구조물에 물고기가 지나갈 수 있도록 만든 생태통로로 약 5천여 개가 있음
URLhttps://www.data.go.kr/data/15115610/fileData.do

Alerts

하천등급 is highly imbalanced (69.2%)Imbalance
위치 is highly imbalanced (53.7%)Imbalance
형식3 is highly imbalanced (95.7%)Imbalance
보명칭 has 204 (3.7%) missing valuesMissing
관리기관 has 82 (1.5%) missing valuesMissing
위도-도 has 90 (1.6%) missing valuesMissing
위도-분 has 89 (1.6%) missing valuesMissing
위도-초 has 89 (1.6%) missing valuesMissing
경도-도 has 89 (1.6%) missing valuesMissing
경도-분 has 89 (1.6%) missing valuesMissing
경도-초 has 89 (1.6%) missing valuesMissing
폭 has 90 (1.6%) missing valuesMissing
길이 has 101 (1.8%) missing valuesMissing
높이 has 229 (4.1%) missing valuesMissing
위도-도 is highly skewed (γ1 = 22.54047768)Skewed
위도-초 is highly skewed (γ1 = 54.76665798)Skewed
경도-도 is highly skewed (γ1 = 65.07341765)Skewed
경도-초 is highly skewed (γ1 = 62.3726667)Skewed
폭 is highly skewed (γ1 = 31.50268623)Skewed
연번(NO) has unique valuesUnique
위도-분 has 124 (2.2%) zerosZeros
경도-분 has 103 (1.8%) zerosZeros
평균경사도 has 1416 (25.4%) zerosZeros

Reproduction

Analysis started2023-12-12 16:37:41.242561
Analysis finished2023-12-12 16:37:43.466874
Duration2.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번(NO)
Real number (ℝ)

UNIQUE 

Distinct5573
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2787
Minimum1
Maximum5573
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:43.559025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile279.6
Q11394
median2787
Q34180
95-th percentile5294.4
Maximum5573
Range5572
Interquartile range (IQR)2786

Descriptive statistics

Standard deviation1608.9309
Coefficient of variation (CV)0.57729848
Kurtosis-1.2
Mean2787
Median Absolute Deviation (MAD)1393
Skewness0
Sum15531951
Variance2588658.5
MonotonicityStrictly increasing
2023-12-13T01:37:43.697945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3714 1
 
< 0.1%
3722 1
 
< 0.1%
3721 1
 
< 0.1%
3720 1
 
< 0.1%
3719 1
 
< 0.1%
3718 1
 
< 0.1%
3717 1
 
< 0.1%
3716 1
 
< 0.1%
3715 1
 
< 0.1%
Other values (5563) 5563
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5573 1
< 0.1%
5572 1
< 0.1%
5571 1
< 0.1%
5570 1
< 0.1%
5569 1
< 0.1%
5568 1
< 0.1%
5567 1
< 0.1%
5566 1
< 0.1%
5565 1
< 0.1%
5564 1
< 0.1%

보명칭
Text

MISSING 

Distinct4420
Distinct (%)82.3%
Missing204
Missing (%)3.7%
Memory size43.7 KiB
2023-12-13T01:37:44.146816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length3.6861613
Min length1

Characters and Unicode

Total characters19791
Distinct characters438
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3729 ?
Unique (%)69.5%

Sample

1st row이포보
2nd row여주보
3rd row여주보
4th row강천보
5th row강천보
ValueCountFrequency (%)
중 13
 
0.2%
새 12
 
0.2%
새들 12
 
0.2%
석교리2 8
 
0.1%
신촌 8
 
0.1%
덕천리1 8
 
0.1%
남산 6
 
0.1%
용두 6
 
0.1%
신평 6
 
0.1%
석교리1 6
 
0.1%
Other values (4420) 5299
98.4%
2023-12-13T01:37:44.726734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2010
 
10.2%
천 1801
 
9.1%
1 1291
 
6.5%
리 911
 
4.6%
2 747
 
3.8%
3 529
 
2.7%
4 386
 
2.0%
산 312
 
1.6%
5 301
 
1.5%
6 255
 
1.3%
Other values (428) 11248
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13629
68.9%
Decimal Number 6036
30.5%
Dash Punctuation 49
 
0.2%
Open Punctuation 23
 
0.1%
Close Punctuation 22
 
0.1%
Space Separator 19
 
0.1%
Connector Punctuation 12
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 1801
 
13.2%
리 911
 
6.7%
산 312
 
2.3%
곡 246
 
1.8%
동 243
 
1.8%
대 236
 
1.7%
신 215
 
1.6%
평 213
 
1.6%
정 197
 
1.4%
지 182
 
1.3%
Other values (410) 9073
66.6%
Decimal Number
ValueCountFrequency (%)
0 2010
33.3%
1 1291
21.4%
2 747
 
12.4%
3 529
 
8.8%
4 386
 
6.4%
5 301
 
5.0%
6 255
 
4.2%
7 193
 
3.2%
8 179
 
3.0%
9 145
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 21
91.3%
[ 2
 
8.7%
Close Punctuation
ValueCountFrequency (%)
) 20
90.9%
] 2
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13629
68.9%
Common 6162
31.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 1801
 
13.2%
리 911
 
6.7%
산 312
 
2.3%
곡 246
 
1.8%
동 243
 
1.8%
대 236
 
1.7%
신 215
 
1.6%
평 213
 
1.6%
정 197
 
1.4%
지 182
 
1.3%
Other values (410) 9073
66.6%
Common
ValueCountFrequency (%)
0 2010
32.6%
1 1291
21.0%
2 747
 
12.1%
3 529
 
8.6%
4 386
 
6.3%
5 301
 
4.9%
6 255
 
4.1%
7 193
 
3.1%
8 179
 
2.9%
9 145
 
2.4%
Other values (8) 126
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13629
68.9%
ASCII 6162
31.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2010
32.6%
1 1291
21.0%
2 747
 
12.1%
3 529
 
8.6%
4 386
 
6.3%
5 301
 
4.9%
6 255
 
4.1%
7 193
 
3.1%
8 179
 
2.9%
9 145
 
2.4%
Other values (8) 126
 
2.0%
Hangul
ValueCountFrequency (%)
천 1801
 
13.2%
리 911
 
6.7%
산 312
 
2.3%
곡 246
 
1.8%
동 243
 
1.8%
대 236
 
1.7%
신 215
 
1.6%
평 213
 
1.6%
정 197
 
1.4%
지 182
 
1.3%
Other values (410) 9073
66.6%

어도명칭
Text

Distinct5304
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
2023-12-13T01:37:45.158474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length7.6294635
Min length1

Characters and Unicode

Total characters42519
Distinct characters440
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5116 ?
Unique (%)91.8%

Sample

1st row이포보 어도1
2nd row여주보 어도2
3rd row여주보 어도1
4th row강천보 어도2
5th row강천보 어도1
ValueCountFrequency (%)
어도1 4612
41.8%
어도2 575
 
5.2%
어도3 78
 
0.7%
신규어도1 44
 
0.4%
어도4 22
 
0.2%
어도1-16 21
 
0.2%
미등록보 14
 
0.1%
새들 14
 
0.1%
새 14
 
0.1%
중 14
 
0.1%
Other values (4594) 5619
51.0%
2023-12-13T01:37:45.700611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6039
14.2%
도 5503
12.9%
5462
12.8%
어 5457
12.8%
0 2035
 
4.8%
천 1834
 
4.3%
2 1356
 
3.2%
리 919
 
2.2%
3 619
 
1.5%
4 416
 
1.0%
Other values (430) 12879
30.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25303
59.5%
Decimal Number 11601
27.3%
Space Separator 5462
 
12.8%
Dash Punctuation 75
 
0.2%
Close Punctuation 32
 
0.1%
Open Punctuation 32
 
0.1%
Connector Punctuation 12
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 5503
21.7%
어 5457
21.6%
천 1834
 
7.2%
리 919
 
3.6%
산 323
 
1.3%
신 291
 
1.2%
동 253
 
1.0%
곡 248
 
1.0%
보 247
 
1.0%
대 238
 
0.9%
Other values (412) 9990
39.5%
Decimal Number
ValueCountFrequency (%)
1 6039
52.1%
0 2035
 
17.5%
2 1356
 
11.7%
3 619
 
5.3%
4 416
 
3.6%
5 311
 
2.7%
6 289
 
2.5%
7 201
 
1.7%
8 186
 
1.6%
9 149
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 30
93.8%
] 2
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 30
93.8%
[ 2
 
6.2%
Space Separator
ValueCountFrequency (%)
5462
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Other Punctuation
ValueCountFrequency (%)
# 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25303
59.5%
Common 17216
40.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 5503
21.7%
어 5457
21.6%
천 1834
 
7.2%
리 919
 
3.6%
산 323
 
1.3%
신 291
 
1.2%
동 253
 
1.0%
곡 248
 
1.0%
보 247
 
1.0%
대 238
 
0.9%
Other values (412) 9990
39.5%
Common
ValueCountFrequency (%)
1 6039
35.1%
5462
31.7%
0 2035
 
11.8%
2 1356
 
7.9%
3 619
 
3.6%
4 416
 
2.4%
5 311
 
1.8%
6 289
 
1.7%
7 201
 
1.2%
8 186
 
1.1%
Other values (8) 302
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25303
59.5%
ASCII 17216
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6039
35.1%
5462
31.7%
0 2035
 
11.8%
2 1356
 
7.9%
3 619
 
3.6%
4 416
 
2.4%
5 311
 
1.8%
6 289
 
1.7%
7 201
 
1.2%
8 186
 
1.1%
Other values (8) 302
 
1.8%
Hangul
ValueCountFrequency (%)
도 5503
21.7%
어 5457
21.6%
천 1834
 
7.2%
리 919
 
3.6%
산 323
 
1.3%
신 291
 
1.2%
동 253
 
1.0%
곡 248
 
1.0%
보 247
 
1.0%
대 238
 
0.9%
Other values (412) 9990
39.5%

관리기관
Text

MISSING 

Distinct258
Distinct (%)4.7%
Missing82
Missing (%)1.5%
Memory size43.7 KiB
2023-12-13T01:37:46.000860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length6
Mean length7.3600437
Min length3

Characters and Unicode

Total characters40414
Distinct characters132
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.4%

Sample

1st row수자원공사
2nd row수자원공사
3rd row수자원공사
4th row수자원공사
5th row수자원공사
ValueCountFrequency (%)
경남 899
 
8.2%
농어촌공사 835
 
7.6%
전남 755
 
6.9%
강원도 679
 
6.2%
전북 615
 
5.6%
경북 555
 
5.1%
경기도 358
 
3.3%
충북 286
 
2.6%
충남 258
 
2.4%
거창군 254
 
2.3%
Other values (256) 5443
49.8%
2023-12-13T01:37:46.486590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5460
 
13.5%
군 2849
 
7.0%
남 2148
 
5.3%
경 1878
 
4.6%
사 1751
 
4.3%
시 1669
 
4.1%
전 1502
 
3.7%
북 1493
 
3.7%
도 1085
 
2.7%
원 942
 
2.3%
Other values (122) 19637
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32808
81.2%
Space Separator 5460
 
13.5%
Close Punctuation 837
 
2.1%
Open Punctuation 837
 
2.1%
Other Punctuation 472
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 2849
 
8.7%
남 2148
 
6.5%
경 1878
 
5.7%
사 1751
 
5.3%
시 1669
 
5.1%
전 1502
 
4.6%
북 1493
 
4.6%
도 1085
 
3.3%
원 942
 
2.9%
강 926
 
2.8%
Other values (118) 16565
50.5%
Space Separator
ValueCountFrequency (%)
5460
100.0%
Close Punctuation
ValueCountFrequency (%)
) 837
100.0%
Open Punctuation
ValueCountFrequency (%)
( 837
100.0%
Other Punctuation
ValueCountFrequency (%)
. 472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32808
81.2%
Common 7606
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 2849
 
8.7%
남 2148
 
6.5%
경 1878
 
5.7%
사 1751
 
5.3%
시 1669
 
5.1%
전 1502
 
4.6%
북 1493
 
4.6%
도 1085
 
3.3%
원 942
 
2.9%
강 926
 
2.8%
Other values (118) 16565
50.5%
Common
ValueCountFrequency (%)
5460
71.8%
) 837
 
11.0%
( 837
 
11.0%
. 472
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32808
81.2%
ASCII 7606
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5460
71.8%
) 837
 
11.0%
( 837
 
11.0%
. 472
 
6.2%
Hangul
ValueCountFrequency (%)
군 2849
 
8.7%
남 2148
 
6.5%
경 1878
 
5.7%
사 1751
 
5.3%
시 1669
 
5.1%
전 1502
 
4.6%
북 1493
 
4.6%
도 1085
 
3.3%
원 942
 
2.9%
강 926
 
2.8%
Other values (118) 16565
50.5%

행정구역명
Text

Distinct1305
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
2023-12-13T01:37:47.101290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length12.924637
Min length8

Characters and Unicode

Total characters72029
Distinct characters275
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique385 ?
Unique (%)6.9%

Sample

1st row경기도 여주시 금사면
2nd row경기도 여주시 능서면
3rd row경기도 여주시 능서면
4th row경기도 여주시 중앙동
5th row경기도 여주시 중앙동
ValueCountFrequency (%)
경상남도 1039
 
6.2%
전라남도 947
 
5.7%
전라북도 839
 
5.0%
강원도 736
 
4.4%
경상북도 658
 
3.9%
경기도 404
 
2.4%
충청북도 373
 
2.2%
충청남도 330
 
2.0%
거창군 271
 
1.6%
강릉시 144
 
0.9%
Other values (1099) 10928
65.6%
2023-12-13T01:37:47.644446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17298
24.0%
도 5440
 
7.6%
면 3990
 
5.5%
군 3466
 
4.8%
남 2799
 
3.9%
시 2326
 
3.2%
북 2253
 
3.1%
경 2183
 
3.0%
상 1955
 
2.7%
전 1951
 
2.7%
Other values (265) 28368
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54654
75.9%
Space Separator 17298
 
24.0%
Decimal Number 74
 
0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 5440
 
10.0%
면 3990
 
7.3%
군 3466
 
6.3%
남 2799
 
5.1%
시 2326
 
4.3%
북 2253
 
4.1%
경 2183
 
4.0%
상 1955
 
3.6%
전 1951
 
3.6%
라 1786
 
3.3%
Other values (259) 26505
48.5%
Decimal Number
ValueCountFrequency (%)
2 29
39.2%
3 26
35.1%
1 17
23.0%
4 2
 
2.7%
Space Separator
ValueCountFrequency (%)
17298
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54654
75.9%
Common 17375
 
24.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 5440
 
10.0%
면 3990
 
7.3%
군 3466
 
6.3%
남 2799
 
5.1%
시 2326
 
4.3%
북 2253
 
4.1%
경 2183
 
4.0%
상 1955
 
3.6%
전 1951
 
3.6%
라 1786
 
3.3%
Other values (259) 26505
48.5%
Common
ValueCountFrequency (%)
17298
99.6%
2 29
 
0.2%
3 26
 
0.1%
1 17
 
0.1%
. 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54654
75.9%
ASCII 17375
 
24.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17298
99.6%
2 29
 
0.2%
3 26
 
0.1%
1 17
 
0.1%
. 3
 
< 0.1%
4 2
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 5440
 
10.0%
면 3990
 
7.3%
군 3466
 
6.3%
남 2799
 
5.1%
시 2326
 
4.3%
북 2253
 
4.1%
경 2183
 
4.0%
상 1955
 
3.6%
전 1951
 
3.6%
라 1786
 
3.3%
Other values (259) 26505
48.5%

시도
Categorical

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
경상남도
1039 
전라남도
947 
전라북도
839 
강원도
736 
경상북도
658 
Other values (12)
1354 

Length

Max length7
Median length4
Mean length3.8573479
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 1039
18.6%
전라남도 947
17.0%
전라북도 839
15.1%
강원도 736
13.2%
경상북도 658
11.8%
경기도 404
 
7.2%
충청남도 330
 
5.9%
충청북도 313
 
5.6%
울산광역시 63
 
1.1%
충청북도 60
 
1.1%
Other values (7) 184
 
3.3%

Length

2023-12-13T01:37:47.815967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상남도 1039
18.6%
전라남도 947
17.0%
전라북도 839
15.1%
강원도 736
13.2%
경상북도 658
11.8%
경기도 404
 
7.2%
충청북도 373
 
6.7%
충청남도 330
 
5.9%
울산광역시 63
 
1.1%
대전광역시 53
 
1.0%
Other values (6) 131
 
2.4%

시군구
Text

Distinct214
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
2023-12-13T01:37:48.147786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.899695
Min length2

Characters and Unicode

Total characters21733
Distinct characters121
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

Unique20 ?
Unique (%)0.4%

Sample

1st row여주시
2nd row여주시
3rd row여주시
4th row여주시
5th row여주시
ValueCountFrequency (%)
거창군 271
 
4.9%
강릉시 144
 
2.6%
남원시 140
 
2.5%
김천시 128
 
2.3%
장성군 123
 
2.2%
하동군 117
 
2.1%
고성군 108
 
1.9%
순천시 106
 
1.9%
임실군 102
 
1.8%
강진군 98
 
1.8%
Other values (152) 4236
76.0%
2023-12-13T01:37:48.605206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4990
23.0%
군 3405
15.7%
시 2060
 
9.5%
양 735
 
3.4%
주 699
 
3.2%
천 621
 
2.9%
성 613
 
2.8%
창 526
 
2.4%
산 328
 
1.5%
거 310
 
1.4%
Other values (111) 7446
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16743
77.0%
Space Separator 4990
 
23.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 3405
20.3%
시 2060
 
12.3%
양 735
 
4.4%
주 699
 
4.2%
천 621
 
3.7%
성 613
 
3.7%
창 526
 
3.1%
산 328
 
2.0%
거 310
 
1.9%
진 303
 
1.8%
Other values (110) 7143
42.7%
Space Separator
ValueCountFrequency (%)
4990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16743
77.0%
Common 4990
 
23.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 3405
20.3%
시 2060
 
12.3%
양 735
 
4.4%
주 699
 
4.2%
천 621
 
3.7%
성 613
 
3.7%
창 526
 
3.1%
산 328
 
2.0%
거 310
 
1.9%
진 303
 
1.8%
Other values (110) 7143
42.7%
Common
ValueCountFrequency (%)
4990
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16743
77.0%
ASCII 4990
 
23.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4990
100.0%
Hangul
ValueCountFrequency (%)
군 3405
20.3%
시 2060
 
12.3%
양 735
 
4.4%
주 699
 
4.2%
천 621
 
3.7%
성 613
 
3.7%
창 526
 
3.1%
산 328
 
2.0%
거 310
 
1.9%
진 303
 
1.8%
Other values (110) 7143
42.7%

읍면동
Text

Distinct1190
Distinct (%)21.5%
Missing50
Missing (%)0.9%
Memory size43.7 KiB
2023-12-13T01:37:48.948898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1962701
Min length2

Characters and Unicode

Total characters17653
Distinct characters266
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique325 ?
Unique (%)5.9%

Sample

1st row금사면
2nd row능서면
3rd row능서면
4th row중앙동
5th row중앙동
ValueCountFrequency (%)
가북면 64
 
1.2%
남면 59
 
1.1%
서면 57
 
1.0%
북면 46
 
0.8%
군동면 41
 
0.7%
고현동 39
 
0.7%
신원면 39
 
0.7%
대덕면 38
 
0.7%
처인구 34
 
0.6%
현북면 32
 
0.6%
Other values (924) 5074
91.9%
2023-12-13T01:37:49.464168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 3990
22.6%
1102
 
6.2%
읍 830
 
4.7%
동 811
 
4.6%
산 512
 
2.9%
북 355
 
2.0%
성 318
 
1.8%
구 310
 
1.8%
남 264
 
1.5%
천 262
 
1.5%
Other values (256) 8899
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16474
93.3%
Space Separator 1102
 
6.2%
Decimal Number 74
 
0.4%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 3990
24.2%
읍 830
 
5.0%
동 811
 
4.9%
산 512
 
3.1%
북 355
 
2.2%
성 318
 
1.9%
구 310
 
1.9%
남 264
 
1.6%
천 262
 
1.6%
서 251
 
1.5%
Other values (250) 8571
52.0%
Decimal Number
ValueCountFrequency (%)
2 29
39.2%
3 26
35.1%
1 17
23.0%
4 2
 
2.7%
Space Separator
ValueCountFrequency (%)
1102
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16474
93.3%
Common 1179
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 3990
24.2%
읍 830
 
5.0%
동 811
 
4.9%
산 512
 
3.1%
북 355
 
2.2%
성 318
 
1.9%
구 310
 
1.9%
남 264
 
1.6%
천 262
 
1.6%
서 251
 
1.5%
Other values (250) 8571
52.0%
Common
ValueCountFrequency (%)
1102
93.5%
2 29
 
2.5%
3 26
 
2.2%
1 17
 
1.4%
. 3
 
0.3%
4 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16474
93.3%
ASCII 1179
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 3990
24.2%
읍 830
 
5.0%
동 811
 
4.9%
산 512
 
3.1%
북 355
 
2.2%
성 318
 
1.9%
구 310
 
1.9%
남 264
 
1.6%
천 262
 
1.6%
서 251
 
1.5%
Other values (250) 8571
52.0%
ASCII
ValueCountFrequency (%)
1102
93.5%
2 29
 
2.5%
3 26
 
2.2%
1 17
 
1.4%
. 3
 
0.3%
4 2
 
0.2%

주소
Text

Distinct3854
Distinct (%)69.4%
Missing22
Missing (%)0.4%
Memory size43.7 KiB
2023-12-13T01:37:49.871212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length20.769591
Min length11

Characters and Unicode

Total characters115292
Distinct characters405
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2845 ?
Unique (%)51.3%

Sample

1st row경기도 여주시 대신면 천서리 678-1
2nd row경기도 여주시 영릉로 452
3rd row경기도 여주시 영릉로 452
4th row경기도 여주시 신단1길 137
5th row경기도 여주시 신단1길 137
ValueCountFrequency (%)
경상남도 1030
 
3.8%
전라남도 856
 
3.2%
전라북도 819
 
3.1%
강원도 736
 
2.7%
경상북도 635
 
2.4%
경기도 402
 
1.5%
충청북도 364
 
1.4%
충청남도 317
 
1.2%
거창군 271
 
1.0%
강릉시 144
 
0.5%
Other values (6295) 21233
79.2%
2023-12-13T01:37:50.407018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21279
 
18.5%
도 5393
 
4.7%
1 4605
 
4.0%
리 4359
 
3.8%
면 3610
 
3.1%
군 3499
 
3.0%
- 3334
 
2.9%
남 2886
 
2.5%
2 2507
 
2.2%
시 2304
 
2.0%
Other values (395) 61516
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68824
59.7%
Decimal Number 21841
 
18.9%
Space Separator 21279
 
18.5%
Dash Punctuation 3334
 
2.9%
Other Punctuation 7
 
< 0.1%
Uppercase Letter 3
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 5393
 
7.8%
리 4359
 
6.3%
면 3610
 
5.2%
군 3499
 
5.1%
남 2886
 
4.2%
시 2304
 
3.3%
북 2226
 
3.2%
경 2217
 
3.2%
전 2012
 
2.9%
상 2005
 
2.9%
Other values (377) 38313
55.7%
Decimal Number
ValueCountFrequency (%)
1 4605
21.1%
2 2507
11.5%
3 2128
9.7%
4 1977
9.1%
5 1962
9.0%
6 1929
8.8%
7 1848
8.5%
8 1729
 
7.9%
0 1588
 
7.3%
9 1568
 
7.2%
Other Punctuation
ValueCountFrequency (%)
. 4
57.1%
? 3
42.9%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
L 1
33.3%
Space Separator
ValueCountFrequency (%)
21279
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3334
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68824
59.7%
Common 46465
40.3%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 5393
 
7.8%
리 4359
 
6.3%
면 3610
 
5.2%
군 3499
 
5.1%
남 2886
 
4.2%
시 2304
 
3.3%
북 2226
 
3.2%
경 2217
 
3.2%
전 2012
 
2.9%
상 2005
 
2.9%
Other values (377) 38313
55.7%
Common
ValueCountFrequency (%)
21279
45.8%
1 4605
 
9.9%
- 3334
 
7.2%
2 2507
 
5.4%
3 2128
 
4.6%
4 1977
 
4.3%
5 1962
 
4.2%
6 1929
 
4.2%
7 1848
 
4.0%
8 1729
 
3.7%
Other values (6) 3167
 
6.8%
Latin
ValueCountFrequency (%)
B 2
66.7%
L 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68824
59.7%
ASCII 46468
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21279
45.8%
1 4605
 
9.9%
- 3334
 
7.2%
2 2507
 
5.4%
3 2128
 
4.6%
4 1977
 
4.3%
5 1962
 
4.2%
6 1929
 
4.2%
7 1848
 
4.0%
8 1729
 
3.7%
Other values (8) 3170
 
6.8%
Hangul
ValueCountFrequency (%)
도 5393
 
7.8%
리 4359
 
6.3%
면 3610
 
5.2%
군 3499
 
5.1%
남 2886
 
4.2%
시 2304
 
3.3%
북 2226
 
3.2%
경 2217
 
3.2%
전 2012
 
2.9%
상 2005
 
2.9%
Other values (377) 38313
55.7%

하천권역
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
낙동강권역
1780 
한강권역
1354 
섬진강권역
944 
금강권역
923 
영산강권역
567 

Length

Max length5
Median length5
Mean length4.5914229
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강권역 1780
31.9%
한강권역 1354
24.3%
섬진강권역 944
16.9%
금강권역 923
16.6%
영산강권역 567
 
10.2%
제주도권역 5
 
0.1%

Length

2023-12-13T01:37:50.540033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:37:50.648185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강권역 1780
31.9%
한강권역 1354
24.3%
섬진강권역 944
16.9%
금강권역 923
16.6%
영산강권역 567
 
10.2%
제주도권역 5
 
0.1%

하천수계
Categorical

Distinct26
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
낙동강수계
1405 
한강수계
931 
섬진강수계
669 
금강수계
585 
영산강수계
324 
Other values (21)
1659 

Length

Max length8
Median length5
Mean length5.2553382
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강수계 1405
25.2%
한강수계 931
16.7%
섬진강수계 669
12.0%
금강수계 585
10.5%
영산강수계 324
 
5.8%
섬진강남해권수계 275
 
4.9%
한강동해권수계 258
 
4.6%
낙동강남해권수계 192
 
3.4%
영산강서해권수계 140
 
2.5%
만경강수계 117
 
2.1%
Other values (16) 677
12.1%

Length

2023-12-13T01:37:50.795971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙동강수계 1405
25.2%
한강수계 931
16.7%
섬진강수계 669
12.0%
금강수계 585
10.5%
영산강수계 324
 
5.8%
섬진강남해권수계 275
 
4.9%
한강동해권수계 258
 
4.6%
낙동강남해권수계 192
 
3.4%
영산강서해권수계 140
 
2.5%
만경강수계 117
 
2.1%
Other values (16) 677
12.1%

하천등급
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
지방
5265 
국가
 
308

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방 5265
94.5%
국가 308
 
5.5%

Length

2023-12-13T01:37:50.923049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:37:51.010414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방 5265
94.5%
국가 308
 
5.5%

하천명
Text

Distinct990
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
2023-12-13T01:37:51.319452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9301992
Min length2

Characters and Unicode

Total characters16330
Distinct characters269
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

Unique250 ?
Unique (%)4.5%

Sample

1st row한강
2nd row한강
3rd row한강
4th row한강
5th row한강
ValueCountFrequency (%)
감천 68
 
1.2%
남대천 58
 
1.0%
황강 57
 
1.0%
탐진강 55
 
1.0%
가천천 41
 
0.7%
북천 40
 
0.7%
요천 36
 
0.6%
섬진강 36
 
0.6%
연곡천 35
 
0.6%
사천천 34
 
0.6%
Other values (981) 5187
91.9%
2023-12-13T01:37:51.865054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 5387
33.0%
강 612
 
3.7%
곡 345
 
2.1%
산 328
 
2.0%
대 297
 
1.8%
양 249
 
1.5%
남 222
 
1.4%
동 219
 
1.3%
성 212
 
1.3%
계 183
 
1.1%
Other values (259) 8276
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16256
99.5%
Space Separator 74
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 5387
33.1%
강 612
 
3.8%
곡 345
 
2.1%
산 328
 
2.0%
대 297
 
1.8%
양 249
 
1.5%
남 222
 
1.4%
동 219
 
1.3%
성 212
 
1.3%
계 183
 
1.1%
Other values (258) 8202
50.5%
Space Separator
ValueCountFrequency (%)
74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16256
99.5%
Common 74
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 5387
33.1%
강 612
 
3.8%
곡 345
 
2.1%
산 328
 
2.0%
대 297
 
1.8%
양 249
 
1.5%
남 222
 
1.4%
동 219
 
1.3%
성 212
 
1.3%
계 183
 
1.1%
Other values (258) 8202
50.5%
Common
ValueCountFrequency (%)
74
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16256
99.5%
ASCII 74
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 5387
33.1%
강 612
 
3.8%
곡 345
 
2.1%
산 328
 
2.0%
대 297
 
1.8%
양 249
 
1.5%
남 222
 
1.4%
동 219
 
1.3%
성 212
 
1.3%
계 183
 
1.1%
Other values (258) 8202
50.5%
ASCII
ValueCountFrequency (%)
74
100.0%

기점
Text

Distinct1168
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
2023-12-13T01:37:52.118079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length29
Mean length15.693343
Min length9

Characters and Unicode

Total characters87459
Distinct characters341
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)6.2%

Sample

1st row충북 단양 가곡 사평리 하일천 (지방)합류점
2nd row충북 단양 가곡 사평리 하일천 (지방)합류점
3rd row충북 단양 가곡 사평리 하일천 (지방)합류점
4th row충북 단양 가곡 사평리 하일천 (지방)합류점
5th row충북 단양 가곡 사평리 하일천 (지방)합류점
ValueCountFrequency (%)
경남 1020
 
4.2%
전남 938
 
3.9%
전북 845
 
3.5%
강원 736
 
3.0%
경북 682
 
2.8%
합류점 495
 
2.0%
경기 409
 
1.7%
충북 371
 
1.5%
충남 332
 
1.4%
거창 297
 
1.2%
Other values (2048) 18052
74.7%
2023-12-13T01:37:52.536220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18663
 
21.3%
남 2800
 
3.2%
북 2546
 
2.9%
경 2532
 
2.9%
리 2379
 
2.7%
지 2287
 
2.6%
전 2135
 
2.4%
선 1694
 
1.9%
천 1691
 
1.9%
산 1553
 
1.8%
Other values (331) 49179
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 61691
70.5%
Space Separator 18663
 
21.3%
Decimal Number 5257
 
6.0%
Close Punctuation 538
 
0.6%
Open Punctuation 538
 
0.6%
Other Punctuation 463
 
0.5%
Dash Punctuation 224
 
0.3%
Lowercase Letter 80
 
0.1%
Format 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 2800
 
4.5%
북 2546
 
4.1%
경 2532
 
4.1%
리 2379
 
3.9%
지 2287
 
3.7%
전 2135
 
3.5%
선 1694
 
2.7%
천 1691
 
2.7%
산 1553
 
2.5%
번 1544
 
2.5%
Other values (313) 40530
65.7%
Decimal Number
ValueCountFrequency (%)
1 984
18.7%
2 664
12.6%
3 552
10.5%
7 531
10.1%
6 526
10.0%
4 500
9.5%
5 446
8.5%
8 422
8.0%
0 320
 
6.1%
9 312
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 397
85.7%
, 66
 
14.3%
Space Separator
ValueCountFrequency (%)
18663
100.0%
Close Punctuation
ValueCountFrequency (%)
) 538
100.0%
Open Punctuation
ValueCountFrequency (%)
( 538
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 224
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 80
100.0%
Format
ValueCountFrequency (%)
­ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 61657
70.5%
Common 25688
29.4%
Latin 80
 
0.1%
Han 34
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 2800
 
4.5%
북 2546
 
4.1%
경 2532
 
4.1%
리 2379
 
3.9%
지 2287
 
3.7%
전 2135
 
3.5%
선 1694
 
2.7%
천 1691
 
2.7%
산 1553
 
2.5%
번 1544
 
2.5%
Other values (312) 40496
65.7%
Common
ValueCountFrequency (%)
18663
72.7%
1 984
 
3.8%
2 664
 
2.6%
3 552
 
2.1%
) 538
 
2.1%
( 538
 
2.1%
7 531
 
2.1%
6 526
 
2.0%
4 500
 
1.9%
5 446
 
1.7%
Other values (7) 1746
 
6.8%
Latin
ValueCountFrequency (%)
m 80
100.0%
Han
ValueCountFrequency (%)
小 34
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 61657
70.5%
ASCII 25763
29.5%
CJK 34
 
< 0.1%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18663
72.4%
1 984
 
3.8%
2 664
 
2.6%
3 552
 
2.1%
) 538
 
2.1%
( 538
 
2.1%
7 531
 
2.1%
6 526
 
2.0%
4 500
 
1.9%
5 446
 
1.7%
Other values (7) 1821
 
7.1%
Hangul
ValueCountFrequency (%)
남 2800
 
4.5%
북 2546
 
4.1%
경 2532
 
4.1%
리 2379
 
3.9%
지 2287
 
3.7%
전 2135
 
3.5%
선 1694
 
2.7%
천 1691
 
2.7%
산 1553
 
2.5%
번 1544
 
2.5%
Other values (312) 40496
65.7%
CJK
ValueCountFrequency (%)
小 34
100.0%
None
ValueCountFrequency (%)
­ 5
100.0%

종점
Text

Distinct1034
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
2023-12-13T01:37:52.868191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length33
Mean length19.650637
Min length10

Characters and Unicode

Total characters109513
Distinct characters303
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique268 ?
Unique (%)4.8%

Sample

1st row경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선
2nd row경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선
3rd row경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선
4th row경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선
5th row경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선
ValueCountFrequency (%)
합류점 3314
 
11.8%
경남 1047
 
3.7%
전남 916
 
3.2%
전북 795
 
2.8%
강원 732
 
2.6%
경북 645
 
2.3%
기점 530
 
1.9%
해안 479
 
1.7%
지방)합류점 431
 
1.5%
경기 416
 
1.5%
Other values (1516) 18895
67.0%
2023-12-13T01:37:53.302509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22658
20.7%
( 4709
 
4.3%
) 4706
 
4.3%
점 4702
 
4.3%
합 4148
 
3.8%
류 4067
 
3.7%
천 3937
 
3.6%
강 3281
 
3.0%
남 3098
 
2.8%
지 2810
 
2.6%
Other values (293) 51397
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76397
69.8%
Space Separator 22658
 
20.7%
Open Punctuation 4709
 
4.3%
Close Punctuation 4706
 
4.3%
Decimal Number 625
 
0.6%
Other Punctuation 301
 
0.3%
Lowercase Letter 74
 
0.1%
Dash Punctuation 43
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 4702
 
6.2%
합 4148
 
5.4%
류 4067
 
5.3%
천 3937
 
5.2%
강 3281
 
4.3%
남 3098
 
4.1%
지 2810
 
3.7%
방 2642
 
3.5%
경 2514
 
3.3%
가 2479
 
3.2%
Other values (276) 42719
55.9%
Decimal Number
ValueCountFrequency (%)
1 167
26.7%
8 114
18.2%
0 86
13.8%
9 85
13.6%
2 55
 
8.8%
5 41
 
6.6%
3 24
 
3.8%
4 22
 
3.5%
6 22
 
3.5%
7 9
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 255
84.7%
, 46
 
15.3%
Space Separator
ValueCountFrequency (%)
22658
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4709
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4706
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 74
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76397
69.8%
Common 33042
30.2%
Latin 74
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 4702
 
6.2%
합 4148
 
5.4%
류 4067
 
5.3%
천 3937
 
5.2%
강 3281
 
4.3%
남 3098
 
4.1%
지 2810
 
3.7%
방 2642
 
3.5%
경 2514
 
3.3%
가 2479
 
3.2%
Other values (276) 42719
55.9%
Common
ValueCountFrequency (%)
22658
68.6%
( 4709
 
14.3%
) 4706
 
14.2%
. 255
 
0.8%
1 167
 
0.5%
8 114
 
0.3%
0 86
 
0.3%
9 85
 
0.3%
2 55
 
0.2%
, 46
 
0.1%
Other values (6) 161
 
0.5%
Latin
ValueCountFrequency (%)
m 74
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76397
69.8%
ASCII 33116
30.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22658
68.4%
( 4709
 
14.2%
) 4706
 
14.2%
. 255
 
0.8%
1 167
 
0.5%
8 114
 
0.3%
0 86
 
0.3%
9 85
 
0.3%
m 74
 
0.2%
2 55
 
0.2%
Other values (7) 207
 
0.6%
Hangul
ValueCountFrequency (%)
점 4702
 
6.2%
합 4148
 
5.4%
류 4067
 
5.3%
천 3937
 
5.2%
강 3281
 
4.3%
남 3098
 
4.1%
지 2810
 
3.7%
방 2642
 
3.5%
경 2514
 
3.3%
가 2479
 
3.2%
Other values (276) 42719
55.9%

위도-도
Real number (ℝ)

MISSING  SKEWED 

Distinct11
Distinct (%)0.2%
Missing90
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean35.844611
Minimum27
Maximum204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:53.453497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile34
Q135
median35
Q336
95-th percentile37
Maximum204
Range177
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.1652659
Coefficient of variation (CV)0.14410161
Kurtosis579.72481
Mean35.844611
Median Absolute Deviation (MAD)1
Skewness22.540478
Sum196536
Variance26.679972
MonotonicityNot monotonic
2023-12-13T01:37:53.592701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
35 2474
44.4%
36 1206
21.6%
37 1036
18.6%
34 540
 
9.7%
38 208
 
3.7%
127 10
 
0.2%
33 5
 
0.1%
27 1
 
< 0.1%
203 1
 
< 0.1%
204 1
 
< 0.1%
(Missing) 90
 
1.6%
ValueCountFrequency (%)
27 1
 
< 0.1%
33 5
 
0.1%
34 540
 
9.7%
35 2474
44.4%
36 1206
21.6%
37 1036
18.6%
38 208
 
3.7%
65 1
 
< 0.1%
127 10
 
0.2%
203 1
 
< 0.1%
ValueCountFrequency (%)
204 1
 
< 0.1%
203 1
 
< 0.1%
127 10
 
0.2%
65 1
 
< 0.1%
38 208
 
3.7%
37 1036
18.6%
36 1206
21.6%
35 2474
44.4%
34 540
 
9.7%
33 5
 
0.1%

위도-분
Real number (ℝ)

MISSING  ZEROS 

Distinct63
Distinct (%)1.1%
Missing89
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean29.919767
Minimum0
Maximum601
Zeros124
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:53.730106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q116
median31
Q344
95-th percentile56
Maximum601
Range601
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.022725
Coefficient of variation (CV)0.63579123
Kurtosis155.61031
Mean29.919767
Median Absolute Deviation (MAD)14
Skewness5.4685073
Sum164080
Variance361.86407
MonotonicityNot monotonic
2023-12-13T01:37:53.862391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 134
 
2.4%
34 127
 
2.3%
0 124
 
2.2%
3 121
 
2.2%
38 119
 
2.1%
37 118
 
2.1%
33 117
 
2.1%
28 117
 
2.1%
59 116
 
2.1%
26 112
 
2.0%
Other values (53) 4279
76.8%
ValueCountFrequency (%)
0 124
2.2%
1 104
1.9%
2 111
2.0%
3 121
2.2%
4 77
1.4%
5 50
0.9%
6 68
1.2%
7 92
1.7%
8 66
1.2%
9 75
1.3%
ValueCountFrequency (%)
601 1
 
< 0.1%
314 1
 
< 0.1%
80 2
 
< 0.1%
59 116
2.1%
58 63
1.1%
57 69
1.2%
56 60
1.1%
55 92
1.7%
54 73
1.3%
53 95
1.7%

위도-초
Real number (ℝ)

MISSING  SKEWED 

Distinct837
Distinct (%)15.3%
Missing89
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean32.079798
Minimum0
Maximum7896
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:54.000535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q115
median29.1
Q345
95-th percentile57.1
Maximum7896
Range7896
Interquartile range (IQR)30

Descriptive statistics

Standard deviation125.9794
Coefficient of variation (CV)3.9270635
Kurtosis3169.1968
Mean32.079798
Median Absolute Deviation (MAD)15
Skewness54.766658
Sum175925.61
Variance15870.81
MonotonicityNot monotonic
2023-12-13T01:37:54.168005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.3 22
 
0.4%
25.1 21
 
0.4%
24.7 20
 
0.4%
55.7 19
 
0.3%
59.2 19
 
0.3%
4.8 18
 
0.3%
36.3 17
 
0.3%
0.6 17
 
0.3%
31.0 17
 
0.3%
3.0 17
 
0.3%
Other values (827) 5297
95.0%
(Missing) 89
 
1.6%
ValueCountFrequency (%)
0.0 4
 
0.1%
0.1 13
0.2%
0.2 8
0.1%
0.3 5
 
0.1%
0.4 8
0.1%
0.41 1
 
< 0.1%
0.5 5
 
0.1%
0.51 1
 
< 0.1%
0.6 17
0.3%
0.63 1
 
< 0.1%
ValueCountFrequency (%)
7896.0 1
 
< 0.1%
4879.0 1
 
< 0.1%
60.0 4
 
0.1%
59.97 1
 
< 0.1%
59.9 13
0.2%
59.8 11
0.2%
59.73 2
 
< 0.1%
59.7 5
 
0.1%
59.69 1
 
< 0.1%
59.6 2
 
< 0.1%

경도-도
Real number (ℝ)

MISSING  SKEWED 

Distinct10
Distinct (%)0.2%
Missing89
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean127.28155
Minimum18
Maximum1277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:54.301065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile126
Q1127
median127
Q3128
95-th percentile129
Maximum1277
Range1259
Interquartile range (IQR)1

Descriptive statistics

Standard deviation16.18321
Coefficient of variation (CV)0.12714498
Kurtosis4650.6024
Mean127.28155
Median Absolute Deviation (MAD)1
Skewness65.073418
Sum698012
Variance261.89628
MonotonicityNot monotonic
2023-12-13T01:37:54.404295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
127 2724
48.9%
128 1502
27.0%
126 888
 
15.9%
129 354
 
6.4%
37 10
 
0.2%
205 2
 
< 0.1%
18 1
 
< 0.1%
1277 1
 
< 0.1%
56 1
 
< 0.1%
123 1
 
< 0.1%
(Missing) 89
 
1.6%
ValueCountFrequency (%)
18 1
 
< 0.1%
37 10
 
0.2%
56 1
 
< 0.1%
123 1
 
< 0.1%
126 888
 
15.9%
127 2724
48.9%
128 1502
27.0%
129 354
 
6.4%
205 2
 
< 0.1%
1277 1
 
< 0.1%
ValueCountFrequency (%)
1277 1
 
< 0.1%
205 2
 
< 0.1%
129 354
 
6.4%
128 1502
27.0%
127 2724
48.9%
126 888
 
15.9%
123 1
 
< 0.1%
56 1
 
< 0.1%
37 10
 
0.2%
18 1
 
< 0.1%

경도-분
Real number (ℝ)

MISSING  ZEROS 

Distinct65
Distinct (%)1.2%
Missing89
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean30.426988
Minimum0
Maximum749
Zeros103
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:54.536986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median31
Q346
95-th percentile57
Maximum749
Range749
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.021793
Coefficient of variation (CV)0.72375858
Kurtosis281.44767
Mean30.426988
Median Absolute Deviation (MAD)16
Skewness9.3864262
Sum166861.6
Variance484.95939
MonotonicityNot monotonic
2023-12-13T01:37:54.715294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.0 146
 
2.6%
59.0 138
 
2.5%
36.0 123
 
2.2%
44.0 119
 
2.1%
5.0 114
 
2.0%
52.0 114
 
2.0%
50.0 112
 
2.0%
9.0 111
 
2.0%
1.0 110
 
2.0%
49.0 109
 
2.0%
Other values (55) 4288
76.9%
ValueCountFrequency (%)
0.0 103
1.8%
1.0 110
2.0%
2.0 92
1.7%
3.0 96
1.7%
4.0 88
1.6%
5.0 114
2.0%
6.0 85
1.5%
7.0 94
1.7%
8.0 88
1.6%
9.0 111
2.0%
ValueCountFrequency (%)
749.0 1
 
< 0.1%
589.0 1
 
< 0.1%
225.0 1
 
< 0.1%
99.0 1
 
< 0.1%
59.0 138
2.5%
58.0 107
1.9%
57.0 79
1.4%
56.0 105
1.9%
55.0 99
1.8%
54.6 1
 
< 0.1%

경도-초
Real number (ℝ)

MISSING  SKEWED 

Distinct843
Distinct (%)15.4%
Missing89
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean31.137914
Minimum0
Maximum4376
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:54.890997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.015
Q115.0525
median30
Q345.1
95-th percentile57
Maximum4376
Range4376
Interquartile range (IQR)30.0475

Descriptive statistics

Standard deviation62.220454
Coefficient of variation (CV)1.9982217
Kurtosis4340.3196
Mean31.137914
Median Absolute Deviation (MAD)15.01
Skewness62.372667
Sum170760.32
Variance3871.3849
MonotonicityNot monotonic
2023-12-13T01:37:55.024275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.4 21
 
0.4%
46.6 20
 
0.4%
12.1 20
 
0.4%
50.2 18
 
0.3%
42.6 18
 
0.3%
48.8 18
 
0.3%
26.1 18
 
0.3%
24.9 18
 
0.3%
10.5 18
 
0.3%
59.6 17
 
0.3%
Other values (833) 5298
95.1%
(Missing) 89
 
1.6%
ValueCountFrequency (%)
0.0 5
0.1%
0.1 5
0.1%
0.2 8
0.1%
0.3 6
0.1%
0.4 7
0.1%
0.42 1
 
< 0.1%
0.5 5
0.1%
0.51 3
 
0.1%
0.6 9
0.2%
0.63 1
 
< 0.1%
ValueCountFrequency (%)
4376.0 1
 
< 0.1%
529.0 1
 
< 0.1%
502.0 1
 
< 0.1%
497.0 1
 
< 0.1%
169.5 1
 
< 0.1%
84.65 2
 
< 0.1%
60.0 8
0.1%
59.9 7
0.1%
59.8 4
0.1%
59.73 1
 
< 0.1%

위치
Categorical

IMBALANCE 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
중
2691 
좌
1410 
우
1364 
<NA>
 
44
전면
 
30
Other values (7)
 
34

Length

Max length5
Median length1
Mean length1.0459358
Min length1

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row좌
2nd row우
3rd row좌
4th row우
5th row좌

Common Values

ValueCountFrequency (%)
중 2691
48.3%
좌 1410
25.3%
우 1364
24.5%
<NA> 44
 
0.8%
전면 30
 
0.5%
우측중앙 15
 
0.3%
좌측중앙 12
 
0.2%
중좌 2
 
< 0.1%
중앙좌측 2
 
< 0.1%
중우 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Length

2023-12-13T01:37:55.209065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중 2691
48.3%
좌 1410
25.3%
우 1364
24.5%
na 44
 
0.8%
전면 31
 
0.6%
우측중앙 15
 
0.3%
좌측중앙 12
 
0.2%
중좌 2
 
< 0.1%
중앙좌측 2
 
< 0.1%
중우 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

형식1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
표준
3445 
비표준
2128 

Length

Max length3
Median length2
Mean length2.381841
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비표준
2nd row비표준
3rd row표준
4th row표준
5th row비표준

Common Values

ValueCountFrequency (%)
표준 3445
61.8%
비표준 2128
38.2%

Length

2023-12-13T01:37:55.379555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:37:55.478448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
표준 3445
61.8%
비표준 2128
38.2%

형식2
Categorical

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
도벽식
1484 
수로식
1318 
계단식
1108 
기타
772 
아이스하버식
724 
Other values (5)
167 

Length

Max length8
Median length3
Mean length3.3301633
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인공하도식
2nd row인공하도식
3rd row아이스하버식
4th row아이스하버식
5th row인공하도식

Common Values

ValueCountFrequency (%)
도벽식 1484
26.6%
수로식 1318
23.6%
계단식 1108
19.9%
기타 772
13.9%
아이스하버식 724
13.0%
버티칼슬롯식 129
 
2.3%
인공하도식 14
 
0.3%
데닐식 11
 
0.2%
갑문식 8
 
0.1%
기타 (강남식) 5
 
0.1%

Length

2023-12-13T01:37:55.580930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:37:55.721251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도벽식 1484
26.6%
수로식 1318
23.6%
계단식 1108
19.9%
기타 777
13.9%
아이스하버식 724
13.0%
버티칼슬롯식 129
 
2.3%
인공하도식 14
 
0.3%
데닐식 11
 
0.2%
갑문식 8
 
0.1%
강남식 5
 
0.1%

형식3
Categorical

IMBALANCE 

Distinct43
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size43.7 KiB
<NA>
5461 
자연형여울(스톤네트)
 
26
노치계단
 
9
돌붙임
 
6
노치계단식
 
5
Other values (38)
 
66

Length

Max length24
Median length4
Mean length4.0809259
Min length2

Unique

Unique23 ?
Unique (%)0.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5461
98.0%
자연형여울(스톤네트) 26
 
0.5%
노치계단 9
 
0.2%
돌붙임 6
 
0.1%
노치계단식 5
 
0.1%
풀(pool)이 없는 계단식 4
 
0.1%
돌붙임 수로식 4
 
0.1%
돌붙임수로식 4
 
0.1%
변형된 형태의 계단식 4
 
0.1%
돌붙임식 4
 
0.1%
Other values (33) 46
 
0.8%

Length

2023-12-13T01:37:55.843288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 5461
97.2%
자연형여울(스톤네트 26
 
0.5%
계단식 11
 
0.2%
돌붙임 10
 
0.2%
노치계단 9
 
0.2%
변형된 5
 
0.1%
노치계단식 5
 
0.1%
수로식 5
 
0.1%
어도 4
 
0.1%
형태의 4
 
0.1%
Other values (42) 77
 
1.4%

폭
Real number (ℝ)

MISSING  SKEWED 

Distinct338
Distinct (%)6.2%
Missing90
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean3.01863
Minimum0.043000001
Maximum260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:55.956449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.043000001
5-th percentile1
Q11.5
median2.2
Q33.0549999
95-th percentile6.5899999
Maximum260
Range259.957
Interquartile range (IQR)1.5549999

Descriptive statistics

Standard deviation4.7720738
Coefficient of variation (CV)1.580874
Kurtosis1569.6911
Mean3.01863
Median Absolute Deviation (MAD)0.79999995
Skewness31.502686
Sum16551.148
Variance22.772688
MonotonicityNot monotonic
2023-12-13T01:37:56.097217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 774
 
13.9%
3.0 637
 
11.4%
1.5 456
 
8.2%
1.0 410
 
7.4%
2.5 251
 
4.5%
4.0 170
 
3.1%
1.600000024 169
 
3.0%
5.0 162
 
2.9%
2.599999905 143
 
2.6%
1.200000048 104
 
1.9%
Other values (328) 2207
39.6%
ValueCountFrequency (%)
0.043000001 1
 
< 0.1%
0.129999995 1
 
< 0.1%
0.25 1
 
< 0.1%
0.270000011 1
 
< 0.1%
0.300000012 8
0.1%
0.314999998 1
 
< 0.1%
0.340000004 1
 
< 0.1%
0.356999993 1
 
< 0.1%
0.360000014 1
 
< 0.1%
0.400000006 1
 
< 0.1%
ValueCountFrequency (%)
260.0 1
 
< 0.1%
94.0 1
 
< 0.1%
59.0 1
 
< 0.1%
53.20000076 1
 
< 0.1%
48.0 1
 
< 0.1%
46.0 1
 
< 0.1%
40.0 1
 
< 0.1%
37.29999924 1
 
< 0.1%
32.0 1
 
< 0.1%
31.0 3
0.1%

길이
Real number (ℝ)

MISSING 

Distinct648
Distinct (%)11.8%
Missing101
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean20.480887
Minimum0.14
Maximum1336.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:56.237444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile4.6999998
Q18
median13
Q324.299999
95-th percentile52
Maximum1336.2
Range1336.06
Interquartile range (IQR)16.299999

Descriptive statistics

Standard deviation37.987862
Coefficient of variation (CV)1.8547958
Kurtosis434.29366
Mean20.480887
Median Absolute Deviation (MAD)6.6699998
Skewness17.493549
Sum112071.41
Variance1443.0777
MonotonicityNot monotonic
2023-12-13T01:37:56.374773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.0 327
 
5.9%
7.0 313
 
5.6%
8.0 284
 
5.1%
10.0 266
 
4.8%
12.0 196
 
3.5%
9.0 195
 
3.5%
13.0 176
 
3.2%
5.0 157
 
2.8%
15.0 153
 
2.7%
20.0 145
 
2.6%
Other values (638) 3260
58.5%
ValueCountFrequency (%)
0.140000001 1
 
< 0.1%
0.800000012 1
 
< 0.1%
1.0 5
0.1%
1.200000048 1
 
< 0.1%
1.299999952 2
 
< 0.1%
1.450000048 2
 
< 0.1%
1.5 4
0.1%
1.600000024 2
 
< 0.1%
1.700000048 2
 
< 0.1%
1.899999976 3
0.1%
ValueCountFrequency (%)
1336.199951 1
< 0.1%
900.0 1
< 0.1%
820.0 1
< 0.1%
730.0 1
< 0.1%
700.0 1
< 0.1%
574.0 1
< 0.1%
560.5 2
< 0.1%
559.0 1
< 0.1%
458.0 1
< 0.1%
420.0 1
< 0.1%

높이
Real number (ℝ)

MISSING 

Distinct283
Distinct (%)5.3%
Missing229
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean1.2235846
Minimum0.059999999
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:56.798035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.059999999
5-th percentile0.40000001
Q10.64999998
median1
Q31.5
95-th percentile2.7
Maximum60
Range59.94
Interquartile range (IQR)0.85000002

Descriptive statistics

Standard deviation1.3507456
Coefficient of variation (CV)1.103925
Kurtosis709.18622
Mean1.2235846
Median Absolute Deviation (MAD)0.39999998
Skewness19.204689
Sum6538.836
Variance1.8245137
MonotonicityNot monotonic
2023-12-13T01:37:56.922861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 520
 
9.3%
0.600000024 419
 
7.5%
0.699999988 363
 
6.5%
0.5 355
 
6.4%
0.800000012 342
 
6.1%
1.200000048 317
 
5.7%
1.5 283
 
5.1%
0.400000006 233
 
4.2%
0.899999976 209
 
3.8%
1.100000024 192
 
3.4%
Other values (273) 2111
37.9%
(Missing) 229
 
4.1%
ValueCountFrequency (%)
0.059999999 1
 
< 0.1%
0.079999998 3
 
0.1%
0.090000004 1
 
< 0.1%
0.100000001 8
 
0.1%
0.119999997 3
 
0.1%
0.140000001 1
 
< 0.1%
0.150000006 2
 
< 0.1%
0.180000007 2
 
< 0.1%
0.200000003 45
0.8%
0.209999993 2
 
< 0.1%
ValueCountFrequency (%)
60.0 1
 
< 0.1%
22.29999924 1
 
< 0.1%
21.0 1
 
< 0.1%
16.60000038 2
< 0.1%
15.0 3
0.1%
14.0 1
 
< 0.1%
12.0 1
 
< 0.1%
11.39999962 2
< 0.1%
11.10000038 3
0.1%
11.0 1
 
< 0.1%

평균경사도
Real number (ℝ)

ZEROS 

Distinct84
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6449614
Minimum0
Maximum180
Zeros1416
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size49.1 KiB
2023-12-13T01:37:57.051800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q36
95-th percentile12
Maximum180
Range180
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.1287347
Coefficient of variation (CV)1.5347242
Kurtosis329.24029
Mean4.6449614
Median Absolute Deviation (MAD)3
Skewness14.125765
Sum25886.37
Variance50.818858
MonotonicityNot monotonic
2023-12-13T01:37:57.191232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1416
25.4%
3.0 605
10.9%
4.0 591
10.6%
5.0 513
 
9.2%
6.0 434
 
7.8%
2.0 397
 
7.1%
7.0 283
 
5.1%
8.0 230
 
4.1%
10.0 218
 
3.9%
9.0 155
 
2.8%
Other values (74) 731
13.1%
ValueCountFrequency (%)
0.0 1416
25.4%
0.029999999 1
 
< 0.1%
0.050000001 107
 
1.9%
0.200000003 2
 
< 0.1%
0.300000012 2
 
< 0.1%
0.400000006 1
 
< 0.1%
0.5 2
 
< 0.1%
0.600000024 1
 
< 0.1%
1.0 103
 
1.8%
1.299999952 1
 
< 0.1%
ValueCountFrequency (%)
180.0 5
0.1%
54.0 1
 
< 0.1%
47.0 1
 
< 0.1%
45.0 3
0.1%
42.0 2
 
< 0.1%
41.0 1
 
< 0.1%
40.0 3
0.1%
39.0 1
 
< 0.1%
37.0 1
 
< 0.1%
36.0 1
 
< 0.1%

Sample

연번(NO)보명칭어도명칭관리기관행정구역명시도시군구읍면동주소하천권역하천수계하천등급하천명기점종점위도-도위도-분위도-초경도-도경도-분경도-초위치형식1형식2형식3폭길이높이평균경사도
01이포보이포보 어도1수자원공사경기도 여주시 금사면경기도여주시금사면경기도 여주시 대신면 천서리 678-1한강권역한강수계국가한강충북 단양 가곡 사평리 하일천 (지방)합류점경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선372417.412732.033.2좌비표준인공하도식<NA>20.0458.0<NA>0.4
12여주보여주보 어도2수자원공사경기도 여주시 능서면경기도여주시능서면경기도 여주시 영릉로 452한강권역한강수계국가한강충북 단양 가곡 사평리 하일천 (지방)합류점경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선371936.612736.020.5우비표준인공하도식<NA>30.0420.0<NA>0.2
23여주보여주보 어도1수자원공사경기도 여주시 능서면경기도여주시능서면경기도 여주시 영릉로 452한강권역한강수계국가한강충북 단양 가곡 사평리 하일천 (지방)합류점경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선371936.612736.020.5좌표준아이스하버식<NA>5.0106.0<NA>2.9
34강천보강천보 어도2수자원공사경기도 여주시 중앙동경기도여주시중앙동경기도 여주시 신단1길 137한강권역한강수계국가한강충북 단양 가곡 사평리 하일천 (지방)합류점경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선371639.012741.02.1우표준아이스하버식<NA>13.0121.0<NA>2.9
45강천보강천보 어도1수자원공사경기도 여주시 중앙동경기도여주시중앙동경기도 여주시 신단1길 137한강권역한강수계국가한강충북 단양 가곡 사평리 하일천 (지방)합류점경기 김포 월곳 용강리유도31m산정 부터남북으로그은직선371639.012741.02.1좌비표준인공하도식<NA>12.0574.0<NA>0.6
56경안천001경안천001 어도1경기도 광주시경기도 광주시 오포읍경기도광주시오포읍경기도 광주시 오포읍 양벌리 135한강권역한강수계국가경안천경기 광주 오포 용인시 모현면 경계경기 광주 남종 한강(국가) 합류점372248.812714.046.2우비표준수로식<NA>2.518.01.13.0
67안양천002안양천002 어도1경기도 안양시경기도 안양시 동안구경기도안양시동안구경기도 안양시 만안구 안양7동 1157-70한강권역한강수계지방안양천경기 의왕 왕곡 왕곡리경기 안양 안양 안양천(국가) 기점372246.212656.025.9좌표준도벽식<NA>1.025.01.64.0
78안양천004안양천004 어도2경기도 군포시경기도 군포시 재궁동경기도군포시재궁동경기도 군포시 군포1동 908-65한강권역한강수계지방안양천경기 의왕 왕곡 왕곡리경기 안양 안양 안양천(국가) 기점372123.412657.037.0우비표준수로식<NA>1.416.00.83.0
89안양천004안양천004 어도1경기도 군포시경기도 군포시 재궁동경기도군포시재궁동경기도 군포시 군포1동 908-65한강권역한강수계지방안양천경기 의왕 왕곡 왕곡리경기 안양 안양 안양천(국가) 기점372123.412657.037.0좌비표준수로식<NA>1.416.00.83.0
910안양천007안양천007 어도1경기도 의왕시경기도 의왕시 고천동경기도의왕시고천동경기도 의왕시 고천동 339-29한강권역한강수계지방안양천경기 의왕 왕곡 왕곡리경기 안양 안양 안양천(국가) 기점37217.912658.010.0좌표준도벽식<NA>1.614.01.04.0
연번(NO)보명칭어도명칭관리기관행정구역명시도시군구읍면동주소하천권역하천수계하천등급하천명기점종점위도-도위도-분위도-초경도-도경도-분경도-초위치형식1형식2형식3폭길이높이평균경사도
55635564<NA>달성보 어도1수자원공사경상북도 고령군 개진면경상북도고령군개진면경상북도 고령군 개진면 인안리 산2-1낙동강권역낙동강수계국가낙동강경북 안동 도산 토계삼각점(352.7m) 단천삼각점(648.1m)직선부산 강서 명지 낙동강하구둑의 외곽선35444.312825.02.6우표준아이스하버식<NA>6.982.9000026.00.0
55645565<NA>달성보 어도2수자원공사경상북도 고령군 개진면경상북도고령군개진면경상북도 고령군 개진면 인안리 산2-1낙동강권역낙동강수계국가낙동강경북 안동 도산 토계삼각점(352.7m) 단천삼각점(648.1m)직선부산 강서 명지 낙동강하구둑의 외곽선65444.3128225.02.6좌표준도벽식<NA>8.0362.04.20.0
55655566<NA>미등록 어도2전남 고흥군전라남도 고흥군 풍양면전라남도고흥군풍양면전남 고흥군 풍양면 야막리 626-9섬진강권역섬진강남해권수계지방고읍천전남 장흥 관산 농안제방수로전남 장흥 관산 인마 해안343431.8212714.024.35중비표준기타<NA>2.814.01.50.0
55665567<NA>신규보 신규어도1(사리 900-4)경남 산청군경상남도 산청군 시천면경상남도산청군시천면경상남도 산청군 시천면 사리 900-4낙동강권역낙동강수계지방덕천강경남 산청 삼장 유평경남 진주 수곡 덕천강(국가) 기점351625.9312750.046.34좌표준아이스하버식<NA>7.224.51.50.0
55675568<NA>신규보 신규어도1경남 밀양시경상남도 밀양시 삼문동경상남도밀양시삼문동경상남도 밀양시 삼문동 431-2낙동강권역낙동강수계국가밀양강경남 밀양 상동 청도천(지방) 합류점경남 밀양 삼랑진 낙동강(국가) 합류점352915.8212844.043.8우표준계단식<NA>3.147.2000012.70.0
55685569<NA>신규보 신규어도2경남 밀양시경상남도 밀양시 삼문동경상남도밀양시삼문동경상남도 밀양시 삼문동 431-2낙동강권역낙동강수계국가밀양강경남 밀양 상동 청도천(지방) 합류점경남 밀양 삼랑진 낙동강(국가) 합류점352915.8212844.043.8좌표준계단식<NA>3.147.7999992.70.0
55695570<NA>미등록보 미등록어도1경남 양산시경상남도 양산시경상남도양산시<NA>경상남도 양산시 신기동 600낙동강권역낙동강수계국가양산천경남 양산 상북 양산시상북면,삭막동의경계경남 양산 동 낙동강(국가) 합류점352130.01292.014.0우표준계단식<NA>3.013.31.30.0
55705571<NA>미등록보 미등록어도2경남 양산시경상남도 양산시 신기동경상남도양산시신기동경상남도 양산시 신기동 600낙동강권역낙동강수계국가양산천경남 양산 상북 양산시상북면,삭막동의경계경남 양산 동 낙동강(국가) 합류점352130.01292.014.0좌표준계단식<NA>3.013.11.30.0
55715572<NA>신규보 신규어도1경남 밀양시경상남도 밀양시 청도면경상남도밀양시청도면경상남도 밀양시 청도면 인산리 1172낙동강권역낙동강수계지방청도천경남 밀양 청도 구기경남 창녕 부곡 낙동강(국가) 합류점353258.012837.059.0좌표준계단식<NA>1.05.00.50.0
55725573<NA>신규보 신규어도2(봉성리 138)경남 함안군경상남도 함안군 함안면경상남도함안군함안면경상남도 함안군 함안면 봉성리 138낙동강권역낙동강수계지방함안천경남 함안 여항 내곡경남 함안 함안 함안천(국가) 기점351345.012825.042.0좌표준계단식<NA>1.511.30.90.0