Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells10908
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Numeric3
Categorical2
Text3

Dataset

Description부산광역시_교통시설물관리시스템_교통안전시설물정보(노면방향표시정보)_20220630
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15084053

Alerts

번호 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
리명 is highly overall correlated with 번호 and 3 other fieldsHigh correlation
리명 is highly imbalanced (81.1%)Imbalance
도로명 has 5777 (57.8%) missing valuesMissing
교차로명 has 5131 (51.3%) missing valuesMissing
위도 is highly skewed (γ1 = -74.13425464)Skewed
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:10:09.739223
Analysis finished2023-12-10 16:10:11.879175
Duration2.14 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%
Mean10905.121
Minimum1
Maximum21867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:10:11.965635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1121.75
Q15404.75
median10905
Q316356.25
95-th percentile20736.05
Maximum21867
Range21866
Interquartile range (IQR)10951.5

Descriptive statistics

Standard deviation6310.9462
Coefficient of variation (CV)0.57871403
Kurtosis-1.1997931
Mean10905.121
Median Absolute Deviation (MAD)5478
Skewness0.0026236751
Sum1.090512 × 108
Variance39828042
MonotonicityNot monotonic
2023-12-11T01:10:12.100254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17759 1
 
< 0.1%
19290 1
 
< 0.1%
5715 1
 
< 0.1%
5239 1
 
< 0.1%
4709 1
 
< 0.1%
4535 1
 
< 0.1%
21188 1
 
< 0.1%
13130 1
 
< 0.1%
19190 1
 
< 0.1%
9375 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
5 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
14 1
< 0.1%
17 1
< 0.1%
23 1
< 0.1%
ValueCountFrequency (%)
21867 1
< 0.1%
21866 1
< 0.1%
21865 1
< 0.1%
21863 1
< 0.1%
21862 1
< 0.1%
21861 1
< 0.1%
21860 1
< 0.1%
21858 1
< 0.1%
21857 1
< 0.1%
21855 1
< 0.1%

시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강서구
2001 
해운대구
1296 
기장군
1114 
부산진구
824 
사하구
764 
Other values (11)
4001 

Length

Max length4
Median length3
Mean length3.0396
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강서구 2001
20.0%
해운대구 1296
13.0%
기장군 1114
11.1%
부산진구 824
8.2%
사하구 764
 
7.6%
사상구 732
 
7.3%
북구 662
 
6.6%
연제구 516
 
5.2%
남구 478
 
4.8%
동래구 400
 
4.0%
Other values (6) 1213
12.1%

Length

2023-12-11T01:10:12.241913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 2001
20.0%
해운대구 1296
13.0%
기장군 1114
11.1%
부산진구 824
8.2%
사하구 764
 
7.6%
사상구 732
 
7.3%
북구 662
 
6.6%
연제구 516
 
5.2%
남구 478
 
4.8%
동래구 400
 
4.0%
Other values (6) 1213
12.1%

동명
Text

Distinct144
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:10:12.494662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0237
Min length2

Characters and Unicode

Total characters30237
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

Unique9 ?
Unique (%)0.1%

Sample

1st row우동
2nd row대저1동
3rd row중동
4th row개금동
5th row대저2동
ValueCountFrequency (%)
송정동 575
 
5.8%
정관읍 417
 
4.2%
우동 406
 
4.1%
명지동 325
 
3.2%
기장읍 312
 
3.1%
연산동 289
 
2.9%
대저2동 262
 
2.6%
거제동 227
 
2.3%
화명동 218
 
2.2%
재송동 210
 
2.1%
Other values (134) 6759
67.6%
2023-12-11T01:10:12.904342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9084
30.0%
정 1159
 
3.8%
읍 959
 
3.2%
대 868
 
2.9%
송 828
 
2.7%
장 731
 
2.4%
명 605
 
2.0%
천 540
 
1.8%
연 523
 
1.7%
우 441
 
1.5%
Other values (110) 14499
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29536
97.7%
Decimal Number 701
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9084
30.8%
정 1159
 
3.9%
읍 959
 
3.2%
대 868
 
2.9%
송 828
 
2.8%
장 731
 
2.5%
명 605
 
2.0%
천 540
 
1.8%
연 523
 
1.8%
우 441
 
1.5%
Other values (104) 13798
46.7%
Decimal Number
ValueCountFrequency (%)
2 332
47.4%
1 199
28.4%
4 68
 
9.7%
3 60
 
8.6%
6 28
 
4.0%
5 14
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29536
97.7%
Common 701
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9084
30.8%
정 1159
 
3.9%
읍 959
 
3.2%
대 868
 
2.9%
송 828
 
2.8%
장 731
 
2.5%
명 605
 
2.0%
천 540
 
1.8%
연 523
 
1.8%
우 441
 
1.5%
Other values (104) 13798
46.7%
Common
ValueCountFrequency (%)
2 332
47.4%
1 199
28.4%
4 68
 
9.7%
3 60
 
8.6%
6 28
 
4.0%
5 14
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29536
97.7%
ASCII 701
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9084
30.8%
정 1159
 
3.9%
읍 959
 
3.2%
대 868
 
2.9%
송 828
 
2.8%
장 731
 
2.5%
명 605
 
2.0%
천 540
 
1.8%
연 523
 
1.8%
우 441
 
1.5%
Other values (104) 13798
46.7%
ASCII
ValueCountFrequency (%)
2 332
47.4%
1 199
28.4%
4 68
 
9.7%
3 60
 
8.6%
6 28
 
4.0%
5 14
 
2.0%

리명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8885 
달산리
 
97
용수리
 
76
청강리
 
70
모전리
 
64
Other values (43)
 
808

Length

Max length4
Median length4
Mean length3.8827
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8885
88.8%
달산리 97
 
1.0%
용수리 76
 
0.8%
청강리 70
 
0.7%
모전리 64
 
0.6%
예림리 61
 
0.6%
좌천리 60
 
0.6%
동부리 58
 
0.6%
매학리 54
 
0.5%
시랑리 51
 
0.5%
Other values (38) 524
 
5.2%

Length

2023-12-11T01:10:13.026958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8885
88.8%
달산리 97
 
1.0%
용수리 76
 
0.8%
청강리 70
 
0.7%
모전리 64
 
0.6%
예림리 61
 
0.6%
좌천리 60
 
0.6%
동부리 58
 
0.6%
매학리 54
 
0.5%
시랑리 51
 
0.5%
Other values (38) 524
 
5.2%

도로명
Text

MISSING 

Distinct1606
Distinct (%)38.0%
Missing5777
Missing (%)57.8%
Memory size156.2 KiB
2023-12-11T01:10:13.291219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length9.2910253
Min length5

Characters and Unicode

Total characters39236
Distinct characters220
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

Unique793 ?
Unique (%)18.8%

Sample

1st row명륜로 54
2nd row자갈치로15번길 6-1
3rd row녹산산단232로 38-26
4th row서전로58번길 69
5th row서전로58번길 118
ValueCountFrequency (%)
중앙대로 170
 
2.0%
해운대로 130
 
1.5%
낙동대로 123
 
1.5%
16 107
 
1.3%
9 95
 
1.1%
10 80
 
0.9%
14 74
 
0.9%
7 72
 
0.9%
6 69
 
0.8%
금곡대로 68
 
0.8%
Other values (1558) 7457
88.3%
2023-12-11T01:10:13.641645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4222
 
10.8%
로 4044
 
10.3%
1 3144
 
8.0%
2 1854
 
4.7%
3 1736
 
4.4%
대 1656
 
4.2%
길 1636
 
4.2%
번 1480
 
3.8%
4 1437
 
3.7%
5 1320
 
3.4%
Other values (210) 16707
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19514
49.7%
Decimal Number 14837
37.8%
Space Separator 4222
 
10.8%
Dash Punctuation 663
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 4044
20.7%
대 1656
 
8.5%
길 1636
 
8.4%
번 1480
 
7.6%
산 475
 
2.4%
동 467
 
2.4%
중 356
 
1.8%
앙 299
 
1.5%
수 297
 
1.5%
낙 272
 
1.4%
Other values (198) 8532
43.7%
Decimal Number
ValueCountFrequency (%)
1 3144
21.2%
2 1854
12.5%
3 1736
11.7%
4 1437
9.7%
5 1320
8.9%
7 1187
 
8.0%
6 1184
 
8.0%
8 1032
 
7.0%
9 1000
 
6.7%
0 943
 
6.4%
Space Separator
ValueCountFrequency (%)
4222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 663
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19722
50.3%
Hangul 19514
49.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 4044
20.7%
대 1656
 
8.5%
길 1636
 
8.4%
번 1480
 
7.6%
산 475
 
2.4%
동 467
 
2.4%
중 356
 
1.8%
앙 299
 
1.5%
수 297
 
1.5%
낙 272
 
1.4%
Other values (198) 8532
43.7%
Common
ValueCountFrequency (%)
4222
21.4%
1 3144
15.9%
2 1854
9.4%
3 1736
8.8%
4 1437
 
7.3%
5 1320
 
6.7%
7 1187
 
6.0%
6 1184
 
6.0%
8 1032
 
5.2%
9 1000
 
5.1%
Other values (2) 1606
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19722
50.3%
Hangul 19514
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4222
21.4%
1 3144
15.9%
2 1854
9.4%
3 1736
8.8%
4 1437
 
7.3%
5 1320
 
6.7%
7 1187
 
6.0%
6 1184
 
6.0%
8 1032
 
5.2%
9 1000
 
5.1%
Other values (2) 1606
 
8.1%
Hangul
ValueCountFrequency (%)
로 4044
20.7%
대 1656
 
8.5%
길 1636
 
8.4%
번 1480
 
7.6%
산 475
 
2.4%
동 467
 
2.4%
중 356
 
1.8%
앙 299
 
1.5%
수 297
 
1.5%
낙 272
 
1.4%
Other values (198) 8532
43.7%

교차로명
Text

MISSING 

Distinct1160
Distinct (%)23.8%
Missing5131
Missing (%)51.3%
Memory size156.2 KiB
2023-12-11T01:10:13.826762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.0492914
Min length3

Characters and Unicode

Total characters34323
Distinct characters425
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

Unique192 ?
Unique (%)3.9%

Sample

1st row승당삼거리
2nd row대저사거리
3rd row동백초교(6)
4th row순복음강변교회
5th row동래소방서
ValueCountFrequency (%)
앞 69
 
1.2%
명지주거단지 27
 
0.5%
주변 26
 
0.5%
화명동 26
 
0.5%
연산로타리 23
 
0.4%
49호광장(도시가스 23
 
0.4%
임해단지(3)명지동리 22
 
0.4%
만덕로타리 22
 
0.4%
남문구철길4거리(남문구로타리 22
 
0.4%
개포초교 22
 
0.4%
Other values (1246) 5245
94.9%
2023-12-11T01:10:14.138999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 1081
 
3.1%
지 914
 
2.7%
( 894
 
2.6%
) 890
 
2.6%
구 770
 
2.2%
로 718
 
2.1%
660
 
1.9%
동 653
 
1.9%
대 634
 
1.8%
산 598
 
1.7%
Other values (415) 26511
77.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29422
85.7%
Decimal Number 1660
 
4.8%
Open Punctuation 894
 
2.6%
Close Punctuation 890
 
2.6%
Space Separator 660
 
1.9%
Uppercase Letter 562
 
1.6%
Dash Punctuation 109
 
0.3%
Other Punctuation 86
 
0.3%
Other Symbol 27
 
0.1%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 1081
 
3.7%
지 914
 
3.1%
구 770
 
2.6%
로 718
 
2.4%
동 653
 
2.2%
대 634
 
2.2%
산 598
 
2.0%
교 593
 
2.0%
타 538
 
1.8%
거 479
 
1.6%
Other values (374) 22444
76.3%
Uppercase Letter
ValueCountFrequency (%)
P 94
16.7%
B 81
14.4%
C 68
12.1%
I 54
9.6%
L 50
8.9%
G 37
 
6.6%
A 32
 
5.7%
E 31
 
5.5%
S 27
 
4.8%
T 22
 
3.9%
Other values (10) 66
11.7%
Decimal Number
ValueCountFrequency (%)
2 411
24.8%
1 385
23.2%
3 220
13.3%
4 185
11.1%
6 119
 
7.2%
0 72
 
4.3%
9 68
 
4.1%
7 67
 
4.0%
5 67
 
4.0%
8 66
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 40
46.5%
. 28
32.6%
: 16
 
18.6%
@ 2
 
2.3%
Open Punctuation
ValueCountFrequency (%)
( 894
100.0%
Close Punctuation
ValueCountFrequency (%)
) 890
100.0%
Space Separator
ValueCountFrequency (%)
660
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 27
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29449
85.8%
Common 4306
 
12.5%
Latin 568
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 1081
 
3.7%
지 914
 
3.1%
구 770
 
2.6%
로 718
 
2.4%
동 653
 
2.2%
대 634
 
2.2%
산 598
 
2.0%
교 593
 
2.0%
타 538
 
1.8%
거 479
 
1.6%
Other values (375) 22471
76.3%
Latin
ValueCountFrequency (%)
P 94
16.5%
B 81
14.3%
C 68
12.0%
I 54
9.5%
L 50
8.8%
G 37
 
6.5%
A 32
 
5.6%
E 31
 
5.5%
S 27
 
4.8%
T 22
 
3.9%
Other values (11) 72
12.7%
Common
ValueCountFrequency (%)
( 894
20.8%
) 890
20.7%
660
15.3%
2 411
9.5%
1 385
8.9%
3 220
 
5.1%
4 185
 
4.3%
6 119
 
2.8%
- 109
 
2.5%
0 72
 
1.7%
Other values (9) 361
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29422
85.7%
ASCII 4874
 
14.2%
None 27
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 1081
 
3.7%
지 914
 
3.1%
구 770
 
2.6%
로 718
 
2.4%
동 653
 
2.2%
대 634
 
2.2%
산 598
 
2.0%
교 593
 
2.0%
타 538
 
1.8%
거 479
 
1.6%
Other values (374) 22444
76.3%
ASCII
ValueCountFrequency (%)
( 894
18.3%
) 890
18.3%
660
13.5%
2 411
8.4%
1 385
7.9%
3 220
 
4.5%
4 185
 
3.8%
6 119
 
2.4%
- 109
 
2.2%
P 94
 
1.9%
Other values (30) 907
18.6%
None
ValueCountFrequency (%)
㈜ 27
100.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9973
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.04275
Minimum121.64764
Maximum129.303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:10:14.263158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121.64764
5-th percentile128.8562
Q1128.97429
median129.05381
Q3129.11657
95-th percentile129.21371
Maximum129.303
Range7.6553581
Interquartile range (IQR)0.14228385

Descriptive statistics

Standard deviation0.12685465
Coefficient of variation (CV)0.00098304361
Kurtosis1153.7774
Mean129.04275
Median Absolute Deviation (MAD)0.0710778
Skewness-19.880318
Sum1290427.5
Variance0.016092102
MonotonicityNot monotonic
2023-12-11T01:10:14.406898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.9590813 2
 
< 0.1%
129.0220588 2
 
< 0.1%
129.0684026 2
 
< 0.1%
129.0973457 2
 
< 0.1%
129.0162021 2
 
< 0.1%
128.8967717 2
 
< 0.1%
129.1106638 2
 
< 0.1%
129.1231811 2
 
< 0.1%
129.0819443 2
 
< 0.1%
129.0651109 2
 
< 0.1%
Other values (9963) 9980
99.8%
ValueCountFrequency (%)
121.6476386 1
< 0.1%
128.8113928 1
< 0.1%
128.8115386 1
< 0.1%
128.81249 1
< 0.1%
128.8124946 1
< 0.1%
128.8124987 1
< 0.1%
128.8129783 1
< 0.1%
128.8129867 1
< 0.1%
128.8153265 1
< 0.1%
128.8156893 1
< 0.1%
ValueCountFrequency (%)
129.3029967 1
< 0.1%
129.3029782 1
< 0.1%
129.3026325 1
< 0.1%
129.3021414 1
< 0.1%
129.3021208 1
< 0.1%
129.2935809 1
< 0.1%
129.2929375 1
< 0.1%
129.2929194 1
< 0.1%
129.2927701 1
< 0.1%
129.2886808 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9987
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.170999
Minimum21.581028
Maximum35.372142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:10:14.513240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.581028
5-th percentile35.085628
Q135.127441
median35.167628
Q335.203828
95-th percentile35.318785
Maximum35.372142
Range13.791115
Interquartile range (IQR)0.076386223

Descriptive statistics

Standard deviation0.15013305
Coefficient of variation (CV)0.0042686604
Kurtosis6715.6202
Mean35.170999
Median Absolute Deviation (MAD)0.037964845
Skewness-74.134255
Sum351709.99
Variance0.022539932
MonotonicityNot monotonic
2023-12-11T01:10:14.634085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.18005045 2
 
< 0.1%
35.15574258 2
 
< 0.1%
35.13391298 2
 
< 0.1%
35.13386517 2
 
< 0.1%
35.07997356 2
 
< 0.1%
35.20461756 2
 
< 0.1%
35.08894214 2
 
< 0.1%
35.07716884 2
 
< 0.1%
35.21320251 2
 
< 0.1%
35.15072737 2
 
< 0.1%
Other values (9977) 9980
99.8%
ValueCountFrequency (%)
21.58102756 1
< 0.1%
35.02273309 1
< 0.1%
35.02298247 1
< 0.1%
35.0230124 1
< 0.1%
35.02304354 1
< 0.1%
35.02307939 1
< 0.1%
35.02310968 1
< 0.1%
35.02804431 1
< 0.1%
35.03007551 1
< 0.1%
35.03028431 1
< 0.1%
ValueCountFrequency (%)
35.37214219 1
< 0.1%
35.37174996 1
< 0.1%
35.37174421 1
< 0.1%
35.37122946 1
< 0.1%
35.36797266 1
< 0.1%
35.36754638 1
< 0.1%
35.36699885 1
< 0.1%
35.36594499 1
< 0.1%
35.35897251 1
< 0.1%
35.35851212 1
< 0.1%

Interactions

2023-12-11T01:10:11.104654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:10.537639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:10.804159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:11.188815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:10.616536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:10.916338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:11.280698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:10.699772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:11.012464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:10:14.718282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호시군구명리명경도위도
번호1.0000.8740.9150.0050.005
시군구명0.8741.0000.8510.0000.000
리명0.9150.8511.000NaNNaN
경도0.0050.000NaN1.0000.707
위도0.0050.000NaN0.7071.000
2023-12-11T01:10:14.799929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명리명
시군구명1.0000.734
리명0.7341.000
2023-12-11T01:10:14.864969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호경도위도시군구명리명
번호1.0000.5620.4230.5910.709
경도0.5621.0000.6050.0001.000
위도0.4230.6051.0000.0001.000
시군구명0.5910.0000.0001.0000.734
리명0.7091.0001.0000.7341.000

Missing values

2023-12-11T01:10:11.397242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:10:11.729809image/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.
2023-12-11T01:10:11.824622image/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
1775817759해운대구우동<NA><NA>승당삼거리129.14268935.164386
94659466강서구대저1동<NA><NA>대저사거리128.97385435.214692
1864818649해운대구중동<NA><NA><NA>129.17707935.162519
1161811619부산진구개금동<NA><NA><NA>129.02159935.15469
62216222강서구대저2동<NA><NA><NA>128.96015735.16976
1719417195해운대구중동<NA><NA>동백초교(6)129.18009635.164695
95159516강서구강동동<NA><NA><NA>128.94437535.215669
86998700강서구대저2동<NA><NA>순복음강변교회128.94798835.13893
1515515156동래구수안동<NA>명륜로 54동래소방서129.08482735.199306
53245325중구부평동2가<NA>자갈치로15번길 6-1자갈치시장129.02538735.097426
번호시군구명동명리명도로명교차로명경도위도
1969219693기장군정관읍두명리<NA>(덕산마을)정관백운공원삼거리129.15850835.350481
66346635강서구명지동<NA><NA><NA>128.91181835.087002
1804218043해운대구반여동<NA><NA><NA>129.12640635.219445
29392940연제구연산동<NA>연수로 180한독아파트129.08965535.174296
1876218763해운대구재송동<NA><NA>광안대로요금소129.11993535.179862
19221923사하구괴정동<NA>괴정로 233평화의원(서약국)128.98615935.096855
1961719618기장군정관읍용수리<NA><NA>129.16828335.329171
1345313454금정구구서동<NA>무학송로 136<NA>129.09328535.24379
36143615연제구거제동<NA><NA><NA>129.05989235.188151
1671616717해운대구좌동<NA><NA><NA>129.17619835.181168