Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells18245
Missing cells (%)22.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory712.9 KiB
Average record size in memory73.0 B

Variable types

Numeric1
Categorical1
Text6

Dataset

Description부산광역시_교통시설물관리시스템_교통안전시설물정보(차선정보)_20220630
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15084057

Alerts

번호 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 번호High correlation
리명 has 9072 (90.7%) missing valuesMissing
도로명 has 5597 (56.0%) missing valuesMissing
교차로명 has 3576 (35.8%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2024-04-21 09:19:56.662767
Analysis finished2024-04-21 09:19:59.607190
Duration2.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29600.506
Minimum6
Maximum59305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T18:19:59.816312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile2871.95
Q114728
median29709
Q344215.25
95-th percentile56246.15
Maximum59305
Range59299
Interquartile range (IQR)29487.25

Descriptive statistics

Standard deviation17105.597
Coefficient of variation (CV)0.57788191
Kurtosis-1.1946448
Mean29600.506
Median Absolute Deviation (MAD)14742.5
Skewness-0.0016818268
Sum2.9600506 × 108
Variance2.9260146 × 108
MonotonicityNot monotonic
2024-04-21T18:20:00.243909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29546 1
 
< 0.1%
28707 1
 
< 0.1%
41324 1
 
< 0.1%
37715 1
 
< 0.1%
29224 1
 
< 0.1%
25112 1
 
< 0.1%
45701 1
 
< 0.1%
53328 1
 
< 0.1%
18830 1
 
< 0.1%
9548 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
6 1
< 0.1%
12 1
< 0.1%
21 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
35 1
< 0.1%
44 1
< 0.1%
52 1
< 0.1%
64 1
< 0.1%
74 1
< 0.1%
ValueCountFrequency (%)
59305 1
< 0.1%
59299 1
< 0.1%
59295 1
< 0.1%
59293 1
< 0.1%
59290 1
< 0.1%
59287 1
< 0.1%
59283 1
< 0.1%
59274 1
< 0.1%
59269 1
< 0.1%
59256 1
< 0.1%

시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강서구
2286 
사하구
1131 
해운대구
1119 
기장군
925 
부산진구
767 
Other values (11)
3772 

Length

Max length4
Median length3
Mean length3.0172
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서구
2nd row부산진구
3rd row부산진구
4th row남구
5th row강서구

Common Values

ValueCountFrequency (%)
강서구 2286
22.9%
사하구 1131
11.3%
해운대구 1119
11.2%
기장군 925
9.2%
부산진구 767
 
7.7%
사상구 749
 
7.5%
북구 474
 
4.7%
남구 472
 
4.7%
연제구 457
 
4.6%
동래구 400
 
4.0%
Other values (6) 1220
12.2%

Length

2024-04-21T18:20:00.694012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 2286
22.9%
사하구 1131
11.3%
해운대구 1119
11.2%
기장군 925
9.2%
부산진구 767
 
7.7%
사상구 749
 
7.5%
북구 474
 
4.7%
남구 472
 
4.7%
연제구 457
 
4.6%
동래구 400
 
4.0%
Other values (6) 1220
12.2%

동명
Text

Distinct145
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T18:20:01.770267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0705
Min length2

Characters and Unicode

Total characters30705
Distinct characters120
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row송정동
2nd row전포동
3rd row범천동
4th row대연동
5th row강동동
ValueCountFrequency (%)
송정동 683
 
6.8%
우동 355
 
3.5%
정관읍 314
 
3.1%
명지동 303
 
3.0%
연산동 242
 
2.4%
기장읍 238
 
2.4%
신평동 221
 
2.2%
하단동 220
 
2.2%
거제동 215
 
2.1%
구평동 210
 
2.1%
Other values (135) 6999
70.0%
2024-04-21T18:20:03.265521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9396
30.6%
정 1161
 
3.8%
송 896
 
2.9%
대 859
 
2.8%
읍 748
 
2.4%
장 730
 
2.4%
명 515
 
1.7%
가 509
 
1.7%
지 492
 
1.6%
평 491
 
1.6%
Other values (110) 14908
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29912
97.4%
Decimal Number 793
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9396
31.4%
정 1161
 
3.9%
송 896
 
3.0%
대 859
 
2.9%
읍 748
 
2.5%
장 730
 
2.4%
명 515
 
1.7%
가 509
 
1.7%
지 492
 
1.6%
평 491
 
1.6%
Other values (104) 14115
47.2%
Decimal Number
ValueCountFrequency (%)
2 288
36.3%
1 264
33.3%
3 134
16.9%
4 67
 
8.4%
6 28
 
3.5%
5 12
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29912
97.4%
Common 793
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9396
31.4%
정 1161
 
3.9%
송 896
 
3.0%
대 859
 
2.9%
읍 748
 
2.5%
장 730
 
2.4%
명 515
 
1.7%
가 509
 
1.7%
지 492
 
1.6%
평 491
 
1.6%
Other values (104) 14115
47.2%
Common
ValueCountFrequency (%)
2 288
36.3%
1 264
33.3%
3 134
16.9%
4 67
 
8.4%
6 28
 
3.5%
5 12
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29912
97.4%
ASCII 793
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9396
31.4%
정 1161
 
3.9%
송 896
 
3.0%
대 859
 
2.9%
읍 748
 
2.5%
장 730
 
2.4%
명 515
 
1.7%
가 509
 
1.7%
지 492
 
1.6%
평 491
 
1.6%
Other values (104) 14115
47.2%
ASCII
ValueCountFrequency (%)
2 288
36.3%
1 264
33.3%
3 134
16.9%
4 67
 
8.4%
6 28
 
3.5%
5 12
 
1.5%

리명
Text

MISSING 

Distinct54
Distinct (%)5.8%
Missing9072
Missing (%)90.7%
Memory size156.2 KiB
2024-04-21T18:20:04.073678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9385776
Min length2

Characters and Unicode

Total characters2727
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row원리
2nd row용소리
3rd row임랑리
4th row만화리
5th row두명리
ValueCountFrequency (%)
달산리 70
 
7.5%
용수리 63
 
6.8%
청강리 53
 
5.7%
매학리 49
 
5.3%
예림리 48
 
5.2%
반룡리 33
 
3.6%
좌천리 33
 
3.6%
삼성리 33
 
3.6%
대라리 31
 
3.3%
이천리 30
 
3.2%
Other values (44) 485
52.3%
2024-04-21T18:20:05.231400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 928
34.0%
천 86
 
3.2%
산 85
 
3.1%
용 72
 
2.6%
달 70
 
2.6%
수 63
 
2.3%
동 58
 
2.1%
청 57
 
2.1%
전 54
 
2.0%
강 53
 
1.9%
Other values (59) 1201
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2727
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 928
34.0%
천 86
 
3.2%
산 85
 
3.1%
용 72
 
2.6%
달 70
 
2.6%
수 63
 
2.3%
동 58
 
2.1%
청 57
 
2.1%
전 54
 
2.0%
강 53
 
1.9%
Other values (59) 1201
44.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2727
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 928
34.0%
천 86
 
3.2%
산 85
 
3.1%
용 72
 
2.6%
달 70
 
2.6%
수 63
 
2.3%
동 58
 
2.1%
청 57
 
2.1%
전 54
 
2.0%
강 53
 
1.9%
Other values (59) 1201
44.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2727
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 928
34.0%
천 86
 
3.2%
산 85
 
3.1%
용 72
 
2.6%
달 70
 
2.6%
수 63
 
2.3%
동 58
 
2.1%
청 57
 
2.1%
전 54
 
2.0%
강 53
 
1.9%
Other values (59) 1201
44.0%

도로명
Text

MISSING 

Distinct1858
Distinct (%)42.2%
Missing5597
Missing (%)56.0%
Memory size156.2 KiB
2024-04-21T18:20:06.588235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length9.4519646
Min length5

Characters and Unicode

Total characters41617
Distinct characters231
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

Unique1035 ?
Unique (%)23.5%

Sample

1st row전포대로 242
2nd row신암로117번길 59
3rd row새벽시장길 56
4th row해운대로 1113
5th row사상로147번길 16
ValueCountFrequency (%)
중앙대로 113
 
1.3%
해운대로 110
 
1.2%
9 106
 
1.2%
7 100
 
1.1%
낙동대로 100
 
1.1%
10 98
 
1.1%
16 97
 
1.1%
11 94
 
1.1%
6 78
 
0.9%
8 74
 
0.8%
Other values (1790) 7834
89.0%
2024-04-21T18:20:08.382096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4401
 
10.6%
로 4206
 
10.1%
1 3432
 
8.2%
2 1976
 
4.7%
길 1943
 
4.7%
번 1794
 
4.3%
3 1750
 
4.2%
대 1465
 
3.5%
4 1461
 
3.5%
5 1357
 
3.3%
Other values (221) 17832
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20680
49.7%
Decimal Number 15772
37.9%
Space Separator 4401
 
10.6%
Dash Punctuation 764
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 4206
20.3%
길 1943
 
9.4%
번 1794
 
8.7%
대 1465
 
7.1%
산 687
 
3.3%
동 525
 
2.5%
중 289
 
1.4%
장 286
 
1.4%
녹 255
 
1.2%
수 249
 
1.2%
Other values (209) 8981
43.4%
Decimal Number
ValueCountFrequency (%)
1 3432
21.8%
2 1976
12.5%
3 1750
11.1%
4 1461
9.3%
5 1357
 
8.6%
6 1311
 
8.3%
7 1299
 
8.2%
8 1169
 
7.4%
9 1043
 
6.6%
0 974
 
6.2%
Space Separator
ValueCountFrequency (%)
4401
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20937
50.3%
Hangul 20680
49.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 4206
20.3%
길 1943
 
9.4%
번 1794
 
8.7%
대 1465
 
7.1%
산 687
 
3.3%
동 525
 
2.5%
중 289
 
1.4%
장 286
 
1.4%
녹 255
 
1.2%
수 249
 
1.2%
Other values (209) 8981
43.4%
Common
ValueCountFrequency (%)
4401
21.0%
1 3432
16.4%
2 1976
9.4%
3 1750
 
8.4%
4 1461
 
7.0%
5 1357
 
6.5%
6 1311
 
6.3%
7 1299
 
6.2%
8 1169
 
5.6%
9 1043
 
5.0%
Other values (2) 1738
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20937
50.3%
Hangul 20680
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4401
21.0%
1 3432
16.4%
2 1976
9.4%
3 1750
 
8.4%
4 1461
 
7.0%
5 1357
 
6.5%
6 1311
 
6.3%
7 1299
 
6.2%
8 1169
 
5.6%
9 1043
 
5.0%
Other values (2) 1738
 
8.3%
Hangul
ValueCountFrequency (%)
로 4206
20.3%
길 1943
 
9.4%
번 1794
 
8.7%
대 1465
 
7.1%
산 687
 
3.3%
동 525
 
2.5%
중 289
 
1.4%
장 286
 
1.4%
녹 255
 
1.2%
수 249
 
1.2%
Other values (209) 8981
43.4%

교차로명
Text

MISSING 

Distinct1185
Distinct (%)18.4%
Missing3576
Missing (%)35.8%
Memory size156.2 KiB
2024-04-21T18:20:09.253565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length6.9083126
Min length3

Characters and Unicode

Total characters44379
Distinct characters429
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)1.7%

Sample

1st row신암우리약국
2nd row녹산하영산업앞 삼거리(화전지구15구
3rd row새벽시장
4th row지사과학산업단지 진입로
5th row(구) 송정검문소
ValueCountFrequency (%)
앞 58
 
0.8%
하단복개로 50
 
0.7%
명지주거단지 45
 
0.6%
주변 43
 
0.6%
화전지구산업단지 33
 
0.5%
화명동 29
 
0.4%
장안일반산업단지 27
 
0.4%
26
 
0.4%
센텀시티 26
 
0.4%
콘티코(터미널주유소직결 23
 
0.3%
Other values (1264) 6863
95.0%
2024-04-21T18:20:10.606513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 1294
 
2.9%
지 1173
 
2.6%
( 1074
 
2.4%
) 1066
 
2.4%
산 972
 
2.2%
구 916
 
2.1%
로 914
 
2.1%
대 890
 
2.0%
동 872
 
2.0%
교 828
 
1.9%
Other values (419) 34380
77.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38252
86.2%
Decimal Number 2243
 
5.1%
Open Punctuation 1074
 
2.4%
Close Punctuation 1066
 
2.4%
Space Separator 803
 
1.8%
Uppercase Letter 708
 
1.6%
Other Punctuation 97
 
0.2%
Dash Punctuation 79
 
0.2%
Other Symbol 35
 
0.1%
Math Symbol 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 1294
 
3.4%
지 1173
 
3.1%
산 972
 
2.5%
구 916
 
2.4%
로 914
 
2.4%
대 890
 
2.3%
동 872
 
2.3%
교 828
 
2.2%
타 685
 
1.8%
장 644
 
1.7%
Other values (379) 29064
76.0%
Uppercase Letter
ValueCountFrequency (%)
P 113
16.0%
B 90
12.7%
C 77
10.9%
L 66
9.3%
I 63
8.9%
G 61
8.6%
A 49
6.9%
T 42
 
5.9%
S 40
 
5.6%
E 31
 
4.4%
Other values (10) 76
10.7%
Decimal Number
ValueCountFrequency (%)
2 599
26.7%
1 473
21.1%
3 275
12.3%
4 202
 
9.0%
6 133
 
5.9%
5 126
 
5.6%
8 121
 
5.4%
7 108
 
4.8%
0 105
 
4.7%
9 101
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 46
47.4%
. 28
28.9%
: 23
23.7%
Open Punctuation
ValueCountFrequency (%)
( 1074
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1066
100.0%
Space Separator
ValueCountFrequency (%)
803
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 35
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38287
86.3%
Common 5379
 
12.1%
Latin 713
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 1294
 
3.4%
지 1173
 
3.1%
산 972
 
2.5%
구 916
 
2.4%
로 914
 
2.4%
대 890
 
2.3%
동 872
 
2.3%
교 828
 
2.2%
타 685
 
1.8%
장 644
 
1.7%
Other values (380) 29099
76.0%
Latin
ValueCountFrequency (%)
P 113
15.8%
B 90
12.6%
C 77
10.8%
L 66
9.3%
I 63
8.8%
G 61
8.6%
A 49
6.9%
T 42
 
5.9%
S 40
 
5.6%
E 31
 
4.3%
Other values (11) 81
11.4%
Common
ValueCountFrequency (%)
( 1074
20.0%
) 1066
19.8%
803
14.9%
2 599
11.1%
1 473
8.8%
3 275
 
5.1%
4 202
 
3.8%
6 133
 
2.5%
5 126
 
2.3%
8 121
 
2.2%
Other values (8) 507
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38252
86.2%
ASCII 6092
 
13.7%
None 35
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 1294
 
3.4%
지 1173
 
3.1%
산 972
 
2.5%
구 916
 
2.4%
로 914
 
2.4%
대 890
 
2.3%
동 872
 
2.3%
교 828
 
2.2%
타 685
 
1.8%
장 644
 
1.7%
Other values (379) 29064
76.0%
ASCII
ValueCountFrequency (%)
( 1074
17.6%
) 1066
17.5%
803
13.2%
2 599
9.8%
1 473
 
7.8%
3 275
 
4.5%
4 202
 
3.3%
6 133
 
2.2%
5 126
 
2.1%
8 121
 
2.0%
Other values (29) 1220
20.0%
None
ValueCountFrequency (%)
㈜ 35
100.0%

경도
Text

Distinct9963
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T18:20:11.575775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.994
Min length1

Characters and Unicode

Total characters109940
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9930 ?
Unique (%)99.3%

Sample

1st row128.8348534
2nd row129.0667348
3rd row129.0529598
4th row129.0923542
5th row128.9130930
ValueCountFrequency (%)
6
 
0.1%
129.1071052 2
 
< 0.1%
129.0128768 2
 
< 0.1%
128.9603919 2
 
< 0.1%
128.9115331 2
 
< 0.1%
129.0667422 2
 
< 0.1%
128.8848038 2
 
< 0.1%
128.9901971 2
 
< 0.1%
128.9817239 2
 
< 0.1%
129.0788395 2
 
< 0.1%
Other values (9953) 9976
99.8%
2024-04-21T18:20:12.909569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17951
16.3%
2 16517
15.0%
9 14775
13.4%
8 11422
10.4%
. 9994
9.1%
0 9395
8.5%
6 6269
 
5.7%
5 6180
 
5.6%
7 6174
 
5.6%
3 5764
 
5.2%
Other values (2) 5499
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99940
90.9%
Other Punctuation 9994
 
9.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17951
18.0%
2 16517
16.5%
9 14775
14.8%
8 11422
11.4%
0 9395
9.4%
6 6269
 
6.3%
5 6180
 
6.2%
7 6174
 
6.2%
3 5764
 
5.8%
4 5493
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 9994
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 109940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17951
16.3%
2 16517
15.0%
9 14775
13.4%
8 11422
10.4%
. 9994
9.1%
0 9395
8.5%
6 6269
 
5.7%
5 6180
 
5.6%
7 6174
 
5.6%
3 5764
 
5.2%
Other values (2) 5499
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17951
16.3%
2 16517
15.0%
9 14775
13.4%
8 11422
10.4%
. 9994
9.1%
0 9395
8.5%
6 6269
 
5.7%
5 6180
 
5.6%
7 6174
 
5.6%
3 5764
 
5.2%
Other values (2) 5499
 
5.0%

위도
Text

Distinct9989
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T18:20:13.801998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.994
Min length1

Characters and Unicode

Total characters109940
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9982 ?
Unique (%)99.8%

Sample

1st row35.09344244
2nd row35.15781384
3rd row35.14939974
4th row35.14807426
5th row35.16388806
ValueCountFrequency (%)
6
 
0.1%
35.09374011 2
 
< 0.1%
35.08270703 2
 
< 0.1%
35.16187595 2
 
< 0.1%
35.08903995 2
 
< 0.1%
35.21546271 2
 
< 0.1%
35.14953386 2
 
< 0.1%
35.10469911 1
 
< 0.1%
35.11697874 1
 
< 0.1%
35.09344244 1
 
< 0.1%
Other values (9979) 9979
99.8%
2024-04-21T18:20:15.044004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 17236
15.7%
5 16910
15.4%
1 12777
11.6%
. 9994
9.1%
0 8778
8.0%
2 8516
7.7%
9 7471
6.8%
8 7388
6.7%
6 7086
6.4%
7 6930
6.3%
Other values (2) 6854
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99940
90.9%
Other Punctuation 9994
 
9.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 17236
17.2%
5 16910
16.9%
1 12777
12.8%
0 8778
8.8%
2 8516
8.5%
9 7471
7.5%
8 7388
7.4%
6 7086
7.1%
7 6930
6.9%
4 6848
 
6.9%
Other Punctuation
ValueCountFrequency (%)
. 9994
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 109940
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 17236
15.7%
5 16910
15.4%
1 12777
11.6%
. 9994
9.1%
0 8778
8.0%
2 8516
7.7%
9 7471
6.8%
8 7388
6.7%
6 7086
6.4%
7 6930
6.3%
Other values (2) 6854
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 17236
15.7%
5 16910
15.4%
1 12777
11.6%
. 9994
9.1%
0 8778
8.0%
2 8516
7.7%
9 7471
6.8%
8 7388
6.7%
6 7086
6.4%
7 6930
6.3%
Other values (2) 6854
 
6.2%

Interactions

2024-04-21T18:19:58.563536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T18:20:15.304560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호시군구명리명
번호1.0000.8500.908
시군구명0.8501.0000.888
리명0.9080.8881.000
2024-04-21T18:20:15.536015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호시군구명
번호1.0000.549
시군구명0.5491.000

Missing values

2024-04-21T18:19:58.836125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T18:19:59.109758image/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.
2024-04-21T18:19:59.447697image/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

번호시군구명동명리명도로명교차로명경도위도
2954529546강서구송정동<NA><NA><NA>128.834853435.09344244
188189부산진구전포동<NA>전포대로 242<NA>129.066734835.15781384
4777247773부산진구범천동<NA>신암로117번길 59신암우리약국129.052959835.14939974
2938629387남구대연동<NA><NA><NA>129.092354235.14807426
3089030891강서구강동동<NA><NA><NA>128.913093035.16388806
5328953290강서구화전동<NA><NA>녹산하영산업앞 삼거리(화전지구15구128.887829035.10359432
5333453335강서구봉림동<NA><NA><NA>128.896843035.16630667
47634764서구충무동1가<NA>새벽시장길 56새벽시장129.024134535.09476113
2014620147강서구생곡동<NA><NA>지사과학산업단지 진입로128.860813235.13235394
3161531616강서구신호동<NA><NA><NA>128.873001335.08331943
번호시군구명동명리명도로명교차로명경도위도
2847528476부산진구개금동<NA><NA><NA>129.030367535.15889326
2266722668강서구송정동<NA><NA><NA>128.827855735.09797467
4810548106해운대구석대동<NA><NA><NA>129.130945135.23719666
83378338사하구다대동<NA>다대로539번길 10은마주유소128.972031835.05882935
2095720958강서구송정동<NA>녹산산단335로 8<NA>128.855334135.08882892
2596425965강서구명지동<NA><NA>청량사입구128.921292635.11411924
1765517656강서구녹산동<NA><NA>화전지구산업단지- 2구역128.883582135.11234302
1856518566강서구죽동동<NA><NA><NA>128.893832735.19859953
3150131502강서구명지동<NA><NA><NA>128.903590735.08875072
3000630007사하구다대동<NA><NA><NA>128.971033535.05266613