Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells10117
Missing cells (%)12.6%
Duplicate rows736
Duplicate rows (%)7.4%
Total size in memory712.9 KiB
Average record size in memory73.0 B

Variable types

Text5
Categorical2
Numeric1

Dataset

Description전라남도 여수시 도시계획정보시스템(UPIS) 도시계획시설 결정 조서 현황 데이터 자료로 도시계획시설 결정조서를 제공합니다.
URLhttps://www.data.go.kr/data/15119172/fileData.do

Alerts

Dataset has 736 (7.4%) duplicate rowsDuplicates
규모(번호) has 2567 (25.7%) missing valuesMissing
폭 has 2529 (25.3%) missing valuesMissing
기점 has 2502 (25.0%) missing valuesMissing
종점 has 2519 (25.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:15:14.134311
Analysis finished2023-12-12 19:15:15.907425
Duration1.77 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소분류(세분명)
Text

Distinct105
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:15:16.130171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length3.9687
Min length2

Characters and Unicode

Total characters39687
Distinct characters109
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

Unique20 ?
Unique (%)0.2%

Sample

1st row초등학교
2nd row소로1류
3rd row소로2류
4th row완충녹지
5th row초등학교
ValueCountFrequency (%)
소로2류 2982
29.8%
소로3류 2248
22.5%
소로1류 709
 
7.1%
중로2류 478
 
4.8%
완충녹지 341
 
3.4%
중로1류 321
 
3.2%
주차장 313
 
3.1%
중로3류 274
 
2.7%
경관녹지 254
 
2.5%
어린이공원 251
 
2.5%
Other values (95) 1829
18.3%
2023-12-13T04:15:16.602225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 7459
18.8%
류 7391
18.6%
소 6064
15.3%
2 3562
9.0%
3 2702
 
6.8%
중 1125
 
2.8%
1 1116
 
2.8%
공 876
 
2.2%
지 776
 
2.0%
원 705
 
1.8%
Other values (99) 7911
19.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32268
81.3%
Decimal Number 7393
 
18.6%
Close Punctuation 13
 
< 0.1%
Open Punctuation 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 7459
23.1%
류 7391
22.9%
소 6064
18.8%
중 1125
 
3.5%
공 876
 
2.7%
지 776
 
2.4%
원 705
 
2.2%
녹 627
 
1.9%
장 480
 
1.5%
린 471
 
1.5%
Other values (93) 6294
19.5%
Decimal Number
ValueCountFrequency (%)
2 3562
48.2%
3 2702
36.5%
1 1116
 
15.1%
4 13
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32268
81.3%
Common 7419
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 7459
23.1%
류 7391
22.9%
소 6064
18.8%
중 1125
 
3.5%
공 876
 
2.7%
지 776
 
2.4%
원 705
 
2.2%
녹 627
 
1.9%
장 480
 
1.5%
린 471
 
1.5%
Other values (93) 6294
19.5%
Common
ValueCountFrequency (%)
2 3562
48.0%
3 2702
36.4%
1 1116
 
15.0%
) 13
 
0.2%
( 13
 
0.2%
4 13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32268
81.3%
ASCII 7419
 
18.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 7459
23.1%
류 7391
22.9%
소 6064
18.8%
중 1125
 
3.5%
공 876
 
2.7%
지 776
 
2.4%
원 705
 
2.2%
녹 627
 
1.9%
장 480
 
1.5%
린 471
 
1.5%
Other values (93) 6294
19.5%
ASCII
ValueCountFrequency (%)
2 3562
48.0%
3 2702
36.4%
1 1116
 
15.0%
) 13
 
0.2%
( 13
 
0.2%
4 13
 
0.2%

지역명
Text

Distinct3792
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:15:16.979030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length5.9981
Min length2

Characters and Unicode

Total characters59981
Distinct characters278
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2076 ?
Unique (%)20.8%

Sample

1st row초등학교
2nd row소로1-1
3rd row소로2-1045
4th row완충녹지
5th row중흥초교
ValueCountFrequency (%)
완충녹지 311
 
3.1%
경관녹지 252
 
2.5%
노외주차장 191
 
1.9%
주차장 101
 
1.0%
교통광장 63
 
0.6%
어린이공원 50
 
0.5%
소로1-1 31
 
0.3%
소로3-1 28
 
0.3%
소공원 27
 
0.3%
소로2-1 26
 
0.3%
Other values (3782) 8920
89.2%
2023-12-13T04:15:17.594616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7450
12.4%
로 7413
12.4%
2 6116
 
10.2%
소 6083
 
10.1%
1 4722
 
7.9%
3 4636
 
7.7%
4 1621
 
2.7%
5 1585
 
2.6%
9 1484
 
2.5%
7 1478
 
2.5%
Other values (268) 17393
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26261
43.8%
Decimal Number 26020
43.4%
Dash Punctuation 7450
 
12.4%
Close Punctuation 109
 
0.2%
Open Punctuation 109
 
0.2%
Uppercase Letter 27
 
< 0.1%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 7413
28.2%
소 6083
23.2%
중 1160
 
4.4%
지 827
 
3.1%
공 765
 
2.9%
원 706
 
2.7%
녹 623
 
2.4%
장 622
 
2.4%
대 372
 
1.4%
충 354
 
1.3%
Other values (248) 7336
27.9%
Decimal Number
ValueCountFrequency (%)
2 6116
23.5%
1 4722
18.1%
3 4636
17.8%
4 1621
 
6.2%
5 1585
 
6.1%
9 1484
 
5.7%
7 1478
 
5.7%
8 1464
 
5.6%
0 1459
 
5.6%
6 1455
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
R 18
66.7%
A 3
 
11.1%
S 2
 
7.4%
B 2
 
7.4%
K 2
 
7.4%
Other Punctuation
ValueCountFrequency (%)
, 3
60.0%
. 2
40.0%
Dash Punctuation
ValueCountFrequency (%)
- 7450
100.0%
Close Punctuation
ValueCountFrequency (%)
) 109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33693
56.2%
Hangul 26261
43.8%
Latin 27
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 7413
28.2%
소 6083
23.2%
중 1160
 
4.4%
지 827
 
3.1%
공 765
 
2.9%
원 706
 
2.7%
녹 623
 
2.4%
장 622
 
2.4%
대 372
 
1.4%
충 354
 
1.3%
Other values (248) 7336
27.9%
Common
ValueCountFrequency (%)
- 7450
22.1%
2 6116
18.2%
1 4722
14.0%
3 4636
13.8%
4 1621
 
4.8%
5 1585
 
4.7%
9 1484
 
4.4%
7 1478
 
4.4%
8 1464
 
4.3%
0 1459
 
4.3%
Other values (5) 1678
 
5.0%
Latin
ValueCountFrequency (%)
R 18
66.7%
A 3
 
11.1%
S 2
 
7.4%
B 2
 
7.4%
K 2
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33720
56.2%
Hangul 26261
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7450
22.1%
2 6116
18.1%
1 4722
14.0%
3 4636
13.7%
4 1621
 
4.8%
5 1585
 
4.7%
9 1484
 
4.4%
7 1478
 
4.4%
8 1464
 
4.3%
0 1459
 
4.3%
Other values (10) 1705
 
5.1%
Hangul
ValueCountFrequency (%)
로 7413
28.2%
소 6083
23.2%
중 1160
 
4.4%
지 827
 
3.1%
공 765
 
2.9%
원 706
 
2.7%
녹 623
 
2.4%
장 622
 
2.4%
대 372
 
1.4%
충 354
 
1.3%
Other values (248) 7336
27.9%

규모(등급)
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
소로
5969 
<NA>
2586 
중로
1074 
대로
 
344
광로
 
14

Length

Max length4
Median length2
Mean length2.5185
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소로 5969
59.7%
<NA> 2586
25.9%
중로 1074
 
10.7%
대로 344
 
3.4%
광로 14
 
0.1%
골목길 13
 
0.1%

Length

2023-12-13T04:15:17.792050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:17.935889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소로 5969
59.7%
na 2586
25.9%
중로 1074
 
10.7%
대로 344
 
3.4%
광로 14
 
0.1%
골목길 13
 
0.1%

규모(류별)
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
3553 
3
2715 
<NA>
2586 
1
1115 
R
 
18

Length

Max length4
Median length1
Mean length1.7758
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row1
3rd row2
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
2 3553
35.5%
3 2715
27.2%
<NA> 2586
25.9%
1 1115
 
11.2%
R 18
 
0.2%
4 13
 
0.1%

Length

2023-12-13T04:15:18.069195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:15:18.253872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3553
35.5%
3 2715
27.2%
na 2586
25.9%
1 1115
 
11.2%
r 18
 
0.2%
4 13
 
0.1%

규모(번호)
Text

MISSING 

Distinct1360
Distinct (%)18.3%
Missing2567
Missing (%)25.7%
Memory size156.2 KiB
2023-12-13T04:15:18.711653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.459034
Min length1

Characters and Unicode

Total characters18278
Distinct characters16
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique408 ?
Unique (%)5.5%

Sample

1st row1
2nd row1045
3rd row90
4th row295
5th row5
ValueCountFrequency (%)
1 212
 
2.9%
2 138
 
1.9%
5 109
 
1.5%
3 109
 
1.5%
4 102
 
1.4%
8 94
 
1.3%
6 94
 
1.3%
7 86
 
1.2%
9 86
 
1.2%
11 86
 
1.2%
Other values (1350) 6317
85.0%
2023-12-13T04:15:19.398671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3497
19.1%
2 2492
13.6%
3 1875
10.3%
4 1574
8.6%
5 1560
8.5%
9 1471
8.0%
7 1463
8.0%
8 1454
8.0%
0 1445
7.9%
6 1438
7.9%
Other values (6) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18269
> 99.9%
Other Letter 6
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3497
19.1%
2 2492
13.6%
3 1875
10.3%
4 1574
8.6%
5 1560
8.5%
9 1471
8.1%
7 1463
8.0%
8 1454
8.0%
0 1445
7.9%
6 1438
7.9%
Other Letter
ValueCountFrequency (%)
보 2
33.3%
라 1
16.7%
가 1
16.7%
나 1
16.7%
다 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18269
> 99.9%
Hangul 6
 
< 0.1%
Latin 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3497
19.1%
2 2492
13.6%
3 1875
10.3%
4 1574
8.6%
5 1560
8.5%
9 1471
8.1%
7 1463
8.0%
8 1454
8.0%
0 1445
7.9%
6 1438
7.9%
Hangul
ValueCountFrequency (%)
보 2
33.3%
라 1
16.7%
가 1
16.7%
나 1
16.7%
다 1
16.7%
Latin
ValueCountFrequency (%)
A 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18272
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3497
19.1%
2 2492
13.6%
3 1875
10.3%
4 1574
8.6%
5 1560
8.5%
9 1471
8.1%
7 1463
8.0%
8 1454
8.0%
0 1445
7.9%
6 1438
7.9%
Hangul
ValueCountFrequency (%)
보 2
33.3%
라 1
16.7%
가 1
16.7%
나 1
16.7%
다 1
16.7%

폭
Real number (ℝ)

MISSING 

Distinct67
Distinct (%)0.9%
Missing2529
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean9.7183576
Minimum1
Maximum303.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:15:19.620930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q310
95-th percentile24.5
Maximum303.1
Range302.1
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.1419035
Coefficient of variation (CV)0.7348879
Kurtosis390.66689
Mean9.7183576
Median Absolute Deviation (MAD)2
Skewness11.484646
Sum72605.85
Variance51.006785
MonotonicityNot monotonic
2023-12-13T04:15:19.875413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0 2965
29.6%
6.0 1670
16.7%
10.0 707
 
7.1%
4.0 487
 
4.9%
15.0 453
 
4.5%
20.0 296
 
3.0%
12.0 280
 
2.8%
25.0 166
 
1.7%
30.0 86
 
0.9%
35.0 71
 
0.7%
Other values (57) 290
 
2.9%
(Missing) 2529
25.3%
ValueCountFrequency (%)
1.0 2
 
< 0.1%
1.5 3
 
< 0.1%
2.0 39
 
0.4%
2.5 2
 
< 0.1%
3.0 41
 
0.4%
4.0 487
 
4.9%
5.0 39
 
0.4%
6.0 1670
16.7%
6.5 3
 
< 0.1%
7.0 5
 
0.1%
ValueCountFrequency (%)
303.1 1
 
< 0.1%
93.0 1
 
< 0.1%
85.0 1
 
< 0.1%
77.0 1
 
< 0.1%
64.5 1
 
< 0.1%
60.0 1
 
< 0.1%
55.0 1
 
< 0.1%
50.0 11
0.1%
48.0 1
 
< 0.1%
46.0 1
 
< 0.1%

기점
Text

MISSING 

Distinct5066
Distinct (%)67.6%
Missing2502
Missing (%)25.0%
Memory size156.2 KiB
2023-12-13T04:15:20.331769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length10.452387
Min length3

Characters and Unicode

Total characters78372
Distinct characters202
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4130 ?
Unique (%)55.1%

Sample

1st row대1-3
2nd row중3-88 관기리 393답
3rd row대3-11
4th row중3-9
5th row매립지 웅천동 대로2-3
ValueCountFrequency (%)
웅천동 457
 
3.2%
죽림리 333
 
2.3%
문수동 262
 
1.8%
화장동 249
 
1.7%
우두리 236
 
1.6%
군내리 114
 
0.8%
덕충동 114
 
0.8%
일원 111
 
0.8%
둔덕동 111
 
0.8%
미평동 91
 
0.6%
Other values (5520) 12360
85.6%
2023-12-13T04:15:20.953833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7999
 
10.2%
1 7881
 
10.1%
6944
 
8.9%
2 6050
 
7.7%
3 4126
 
5.3%
동 3484
 
4.4%
대 2652
 
3.4%
4 2555
 
3.3%
5 2445
 
3.1%
소 2373
 
3.0%
Other values (192) 31863
40.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33207
42.4%
Other Letter 29312
37.4%
Dash Punctuation 7999
 
10.2%
Space Separator 6944
 
8.9%
Open Punctuation 435
 
0.6%
Close Punctuation 435
 
0.6%
Other Punctuation 17
 
< 0.1%
Uppercase Letter 14
 
< 0.1%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3484
 
11.9%
대 2652
 
9.0%
소 2373
 
8.1%
로 2076
 
7.1%
중 1842
 
6.3%
리 1381
 
4.7%
전 956
 
3.3%
도 873
 
3.0%
답 760
 
2.6%
산 716
 
2.4%
Other values (167) 12199
41.6%
Decimal Number
ValueCountFrequency (%)
1 7881
23.7%
2 6050
18.2%
3 4126
12.4%
4 2555
 
7.7%
5 2445
 
7.4%
6 2207
 
6.6%
7 2136
 
6.4%
8 2036
 
6.1%
9 1911
 
5.8%
0 1860
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
G 3
21.4%
A 2
14.3%
B 2
14.3%
S 2
14.3%
D 1
 
7.1%
I 1
 
7.1%
R 1
 
7.1%
N 1
 
7.1%
L 1
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 7999
100.0%
Space Separator
ValueCountFrequency (%)
6944
100.0%
Open Punctuation
ValueCountFrequency (%)
( 435
100.0%
Close Punctuation
ValueCountFrequency (%)
) 435
100.0%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49046
62.6%
Hangul 29312
37.4%
Latin 14
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3484
 
11.9%
대 2652
 
9.0%
소 2373
 
8.1%
로 2076
 
7.1%
중 1842
 
6.3%
리 1381
 
4.7%
전 956
 
3.3%
도 873
 
3.0%
답 760
 
2.6%
산 716
 
2.4%
Other values (167) 12199
41.6%
Common
ValueCountFrequency (%)
- 7999
16.3%
1 7881
16.1%
6944
14.2%
2 6050
12.3%
3 4126
8.4%
4 2555
 
5.2%
5 2445
 
5.0%
6 2207
 
4.5%
7 2136
 
4.4%
8 2036
 
4.2%
Other values (6) 4667
9.5%
Latin
ValueCountFrequency (%)
G 3
21.4%
A 2
14.3%
B 2
14.3%
S 2
14.3%
D 1
 
7.1%
I 1
 
7.1%
R 1
 
7.1%
N 1
 
7.1%
L 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49060
62.6%
Hangul 29312
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7999
16.3%
1 7881
16.1%
6944
14.2%
2 6050
12.3%
3 4126
8.4%
4 2555
 
5.2%
5 2445
 
5.0%
6 2207
 
4.5%
7 2136
 
4.4%
8 2036
 
4.2%
Other values (15) 4681
9.5%
Hangul
ValueCountFrequency (%)
동 3484
 
11.9%
대 2652
 
9.0%
소 2373
 
8.1%
로 2076
 
7.1%
중 1842
 
6.3%
리 1381
 
4.7%
전 956
 
3.3%
도 873
 
3.0%
답 760
 
2.6%
산 716
 
2.4%
Other values (167) 12199
41.6%

종점
Text

MISSING 

Distinct5400
Distinct (%)72.2%
Missing2519
Missing (%)25.2%
Memory size156.2 KiB
2023-12-13T04:15:21.453874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length10.627991
Min length1

Characters and Unicode

Total characters79508
Distinct characters235
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4326 ?
Unique (%)57.8%

Sample

1st row소2-1
2nd row중3-82 관기리 387-29답
3rd row중3-13
4th row소3-341
5th row매립지 웅천동 중로3-6
ValueCountFrequency (%)
웅천동 454
 
3.1%
죽림리 340
 
2.4%
문수동 280
 
1.9%
화장동 250
 
1.7%
우두리 233
 
1.6%
둔덕동 132
 
0.9%
덕충동 119
 
0.8%
군내리 116
 
0.8%
일원 112
 
0.8%
미평동 87
 
0.6%
Other values (6125) 12306
85.3%
2023-12-13T04:15:22.265617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 7454
 
9.4%
1 7024
 
8.8%
6950
 
8.7%
2 6109
 
7.7%
3 4404
 
5.5%
동 3614
 
4.5%
소 3612
 
4.5%
4 2706
 
3.4%
5 2527
 
3.2%
7 2460
 
3.1%
Other values (225) 32648
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33680
42.4%
Other Letter 30562
38.4%
Dash Punctuation 7454
 
9.4%
Space Separator 6950
 
8.7%
Close Punctuation 414
 
0.5%
Open Punctuation 414
 
0.5%
Uppercase Letter 13
 
< 0.1%
Math Symbol 11
 
< 0.1%
Other Punctuation 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3614
 
11.8%
소 3612
 
11.8%
로 1784
 
5.8%
대 1421
 
4.6%
리 1415
 
4.6%
중 1219
 
4.0%
전 968
 
3.2%
산 857
 
2.8%
도 806
 
2.6%
답 789
 
2.6%
Other values (201) 14077
46.1%
Decimal Number
ValueCountFrequency (%)
1 7024
20.9%
2 6109
18.1%
3 4404
13.1%
4 2706
 
8.0%
5 2527
 
7.5%
7 2460
 
7.3%
6 2318
 
6.9%
9 2074
 
6.2%
8 2066
 
6.1%
0 1992
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 4
30.8%
I 2
15.4%
D 2
15.4%
R 2
15.4%
L 1
 
7.7%
K 1
 
7.7%
S 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 9
90.0%
. 1
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 7454
100.0%
Space Separator
ValueCountFrequency (%)
6950
100.0%
Close Punctuation
ValueCountFrequency (%)
) 414
100.0%
Open Punctuation
ValueCountFrequency (%)
( 414
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48933
61.5%
Hangul 30562
38.4%
Latin 13
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3614
 
11.8%
소 3612
 
11.8%
로 1784
 
5.8%
대 1421
 
4.6%
리 1415
 
4.6%
중 1219
 
4.0%
전 968
 
3.2%
산 857
 
2.8%
도 806
 
2.6%
답 789
 
2.6%
Other values (201) 14077
46.1%
Common
ValueCountFrequency (%)
- 7454
15.2%
1 7024
14.4%
6950
14.2%
2 6109
12.5%
3 4404
9.0%
4 2706
 
5.5%
5 2527
 
5.2%
7 2460
 
5.0%
6 2318
 
4.7%
9 2074
 
4.2%
Other values (7) 4907
10.0%
Latin
ValueCountFrequency (%)
B 4
30.8%
I 2
15.4%
D 2
15.4%
R 2
15.4%
L 1
 
7.7%
K 1
 
7.7%
S 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48946
61.6%
Hangul 30562
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 7454
15.2%
1 7024
14.4%
6950
14.2%
2 6109
12.5%
3 4404
9.0%
4 2706
 
5.5%
5 2527
 
5.2%
7 2460
 
5.0%
6 2318
 
4.7%
9 2074
 
4.2%
Other values (14) 4920
10.1%
Hangul
ValueCountFrequency (%)
동 3614
 
11.8%
소 3612
 
11.8%
로 1784
 
5.8%
대 1421
 
4.6%
리 1415
 
4.6%
중 1219
 
4.0%
전 968
 
3.2%
산 857
 
2.8%
도 806
 
2.6%
답 789
 
2.6%
Other values (201) 14077
46.1%

Interactions

2023-12-13T04:15:15.204381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:15:22.371171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규모(등급)규모(류별)폭
규모(등급)1.0000.2790.483
규모(류별)0.2791.0000.208
폭0.4830.2081.000
2023-12-13T04:15:22.464290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규모(류별)규모(등급)
규모(류별)1.0000.107
규모(등급)0.1071.000
2023-12-13T04:15:22.552937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폭규모(등급)규모(류별)
폭1.0000.4170.159
규모(등급)0.4171.0000.107
규모(류별)0.1590.1071.000

Missing values

2023-12-13T04:15:15.389608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:15:15.587453image/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.
2023-12-13T04:15:15.794865image/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

소분류(세분명)지역명규모(등급)규모(류별)규모(번호)폭기점종점
6477초등학교초등학교<NA><NA><NA><NA><NA><NA>
4936소로1류소로1-1소로1110.0대1-3소2-1
6492소로2류소로2-1045소로210458.0중3-88 관기리 393답중3-82 관기리 387-29답
4631완충녹지완충녹지<NA><NA><NA><NA><NA><NA>
4652초등학교중흥초교<NA><NA><NA><NA><NA><NA>
1674소로3류소로3-90소로3904.0대3-11중3-13
8069소로3류소로3-295소로32956.0중3-9소3-341
13333주차장공용주차장<NA><NA><NA><NA><NA><NA>
12075중로3류중로3-5중로3512.0매립지 웅천동 대로2-3매립지 웅천동 중로3-6
12116소로1류소로1-409소로140910.5덕충동 61-45대로3-1 만흥동 149-3
소분류(세분명)지역명규모(등급)규모(류별)규모(번호)폭기점종점
8082소로2류소로2-1393소로213938.0중1-49 화장동 931도소1-377 화장동 931도
9913소로2류소로2-990소로29908.0대2-3 우두리654-14전소2-985 우두리654-11대
9916소로2류소로2-1009소로210098.0대3-15 우두리 806-8대소2-970 우두리 666-41도
5099소로2류소로2-600소로26008.0소2-298중3-33
8759소로3류소로3-104소로31046.0주삼동 427답주삼동 409-8전
2058소로2류소로2-7소로278.0중로1-5 1892도소로1-1 1220도
1580소로3류소로3-19소로3196.0소2-25소2-24
10441주차장노외주차장<NA><NA><NA><NA><NA><NA>
12256소로2류소로2-26소로2268.0웅천동 1187 일원 웅천동 소로2-19웅천동 1186-1 일원 웅천동 소로2-23
942소로2류소로2-322소로23228.0문수동 256-4전문수동 197잡

Duplicate rows

Most frequently occurring

소분류(세분명)지역명규모(등급)규모(류별)규모(번호)폭기점종점# duplicates
568완충녹지완충녹지<NA><NA><NA><NA><NA><NA>311
4경관녹지경관녹지<NA><NA><NA><NA><NA><NA>252
597주차장노외주차장<NA><NA><NA><NA><NA><NA>191
598주차장주차장<NA><NA><NA><NA><NA><NA>101
26교통광장교통광장<NA><NA><NA><NA><NA><NA>63
546어린이공원어린이공원<NA><NA><NA><NA><NA><NA>50
128소공원소공원<NA><NA><NA><NA><NA><NA>26
11공공공지공공공지<NA><NA><NA><NA><NA><NA>24
13공공청사공공청사<NA><NA><NA><NA><NA><NA>20
705초등학교초등학교<NA><NA><NA><NA><NA><NA>20