Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells10900
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(노면방향표시 정보)에 대한 데이터로 번호, 시군구명, 동명, 리명, 도로명, 교차로명, 경도, 위도 항목정보를 제공합니다.
Author부산광역시
URLhttps://www.data.go.kr/data/15084053/fileData.do

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 (81.3%)Imbalance
도로명 has 5759 (57.6%) missing valuesMissing
교차로명 has 5141 (51.4%) missing valuesMissing
경도 is highly skewed (γ1 = -25.61931954)Skewed
위도 is highly skewed (γ1 = -60.55552854)Skewed
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:56:20.829168
Analysis finished2023-12-12 08:56:23.627565
Duration2.8 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%
Mean10949.096
Minimum1
Maximum21868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:56:24.151873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1082.9
Q15485.5
median10974.5
Q316389.5
95-th percentile20751.1
Maximum21868
Range21867
Interquartile range (IQR)10904

Descriptive statistics

Standard deviation6307.2543
Coefficient of variation (CV)0.57605249
Kurtosis-1.1928088
Mean10949.096
Median Absolute Deviation (MAD)5455
Skewness-0.0060554664
Sum1.0949096 × 108
Variance39781457
MonotonicityNot monotonic
2023-12-12T17:56:24.348580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21424 1
 
< 0.1%
4833 1
 
< 0.1%
6157 1
 
< 0.1%
17490 1
 
< 0.1%
10621 1
 
< 0.1%
2429 1
 
< 0.1%
9748 1
 
< 0.1%
16709 1
 
< 0.1%
5693 1
 
< 0.1%
5790 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
ValueCountFrequency (%)
21868 1
< 0.1%
21865 1
< 0.1%
21862 1
< 0.1%
21861 1
< 0.1%
21860 1
< 0.1%
21859 1
< 0.1%
21855 1
< 0.1%
21854 1
< 0.1%
21853 1
< 0.1%
21850 1
< 0.1%

시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강서구
2021 
해운대구
1286 
기장군
1105 
부산진구
795 
사상구
755 
Other values (11)
4038 

Length

Max length4
Median length3
Mean length3.0351
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사상구
2nd row강서구
3rd row기장군
4th row강서구
5th row강서구

Common Values

ValueCountFrequency (%)
강서구 2021
20.2%
해운대구 1286
12.9%
기장군 1105
11.1%
부산진구 795
 
8.0%
사상구 755
 
7.5%
사하구 715
 
7.1%
북구 658
 
6.6%
연제구 532
 
5.3%
남구 473
 
4.7%
동래구 417
 
4.2%
Other values (6) 1243
12.4%

Length

2023-12-12T17:56:24.542553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 2021
20.2%
해운대구 1286
12.9%
기장군 1105
11.1%
부산진구 795
 
8.0%
사상구 755
 
7.5%
사하구 715
 
7.1%
북구 658
 
6.6%
연제구 532
 
5.3%
남구 473
 
4.7%
동래구 417
 
4.2%
Other values (6) 1243
12.4%

동명
Text

Distinct140
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:56:24.899265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0264
Min length2

Characters and Unicode

Total characters30264
Distinct characters118
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

Unique6 ?
Unique (%)0.1%

Sample

1st row괘법동
2nd row송정동
3rd row정관읍
4th row송정동
5th row신호동
ValueCountFrequency (%)
송정동 580
 
5.8%
우동 428
 
4.3%
정관읍 425
 
4.2%
명지동 350
 
3.5%
연산동 311
 
3.1%
기장읍 296
 
3.0%
대저2동 270
 
2.7%
거제동 221
 
2.2%
화명동 218
 
2.2%
좌동 204
 
2.0%
Other values (130) 6697
67.0%
2023-12-12T17:56:25.441165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9099
30.1%
정 1175
 
3.9%
읍 932
 
3.1%
대 837
 
2.8%
송 802
 
2.7%
장 677
 
2.2%
명 631
 
2.1%
천 531
 
1.8%
연 531
 
1.8%
우 464
 
1.5%
Other values (108) 14585
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29529
97.6%
Decimal Number 735
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9099
30.8%
정 1175
 
4.0%
읍 932
 
3.2%
대 837
 
2.8%
송 802
 
2.7%
장 677
 
2.3%
명 631
 
2.1%
천 531
 
1.8%
연 531
 
1.8%
우 464
 
1.6%
Other values (102) 13850
46.9%
Decimal Number
ValueCountFrequency (%)
2 340
46.3%
1 190
25.9%
3 68
 
9.3%
4 64
 
8.7%
6 46
 
6.3%
5 27
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29529
97.6%
Common 735
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9099
30.8%
정 1175
 
4.0%
읍 932
 
3.2%
대 837
 
2.8%
송 802
 
2.7%
장 677
 
2.3%
명 631
 
2.1%
천 531
 
1.8%
연 531
 
1.8%
우 464
 
1.6%
Other values (102) 13850
46.9%
Common
ValueCountFrequency (%)
2 340
46.3%
1 190
25.9%
3 68
 
9.3%
4 64
 
8.7%
6 46
 
6.3%
5 27
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29529
97.6%
ASCII 735
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9099
30.8%
정 1175
 
4.0%
읍 932
 
3.2%
대 837
 
2.8%
송 802
 
2.7%
장 677
 
2.3%
명 631
 
2.1%
천 531
 
1.8%
연 531
 
1.8%
우 464
 
1.6%
Other values (102) 13850
46.9%
ASCII
ValueCountFrequency (%)
2 340
46.3%
1 190
25.9%
3 68
 
9.3%
4 64
 
8.7%
6 46
 
6.3%
5 27
 
3.7%

리명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8896 
달산리
 
110
용수리
 
75
청강리
 
73
예림리
 
67
Other values (43)
 
779

Length

Max length4
Median length4
Mean length3.8834
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8896
89.0%
달산리 110
 
1.1%
용수리 75
 
0.8%
청강리 73
 
0.7%
예림리 67
 
0.7%
모전리 61
 
0.6%
매학리 51
 
0.5%
시랑리 50
 
0.5%
동부리 50
 
0.5%
안평리 47
 
0.5%
Other values (38) 520
 
5.2%

Length

2023-12-12T17:56:25.634918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8896
89.0%
달산리 110
 
1.1%
용수리 75
 
0.8%
청강리 73
 
0.7%
예림리 67
 
0.7%
모전리 61
 
0.6%
매학리 51
 
0.5%
시랑리 50
 
0.5%
동부리 50
 
0.5%
안평리 47
 
0.5%
Other values (38) 520
 
5.2%

도로명
Text

MISSING 

Distinct1591
Distinct (%)37.5%
Missing5759
Missing (%)57.6%
Memory size156.2 KiB
2023-12-12T17:56:26.063101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length9.2987503
Min length5

Characters and Unicode

Total characters39436
Distinct characters222
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

Unique771 ?
Unique (%)18.2%

Sample

1st row광장로 10
2nd row르노삼성대로 61
3rd row미음국제3로 31
4th row좌수영로 300
5th row르노삼성대로 61
ValueCountFrequency (%)
중앙대로 174
 
2.1%
낙동대로 146
 
1.7%
해운대로 123
 
1.5%
16 113
 
1.3%
9 96
 
1.1%
7 81
 
1.0%
10 80
 
0.9%
35 74
 
0.9%
8 68
 
0.8%
14 68
 
0.8%
Other values (1521) 7458
87.9%
2023-12-12T17:56:26.597865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4240
 
10.8%
로 4070
 
10.3%
1 3157
 
8.0%
2 1820
 
4.6%
3 1757
 
4.5%
대 1660
 
4.2%
길 1638
 
4.2%
번 1486
 
3.8%
4 1434
 
3.6%
5 1366
 
3.5%
Other values (212) 16808
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19588
49.7%
Decimal Number 14934
37.9%
Space Separator 4240
 
10.8%
Dash Punctuation 674
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 4070
20.8%
대 1660
 
8.5%
길 1638
 
8.4%
번 1486
 
7.6%
동 512
 
2.6%
산 497
 
2.5%
중 323
 
1.6%
낙 295
 
1.5%
수 288
 
1.5%
앙 280
 
1.4%
Other values (200) 8539
43.6%
Decimal Number
ValueCountFrequency (%)
1 3157
21.1%
2 1820
12.2%
3 1757
11.8%
4 1434
9.6%
5 1366
9.1%
7 1216
 
8.1%
6 1202
 
8.0%
9 1081
 
7.2%
8 980
 
6.6%
0 921
 
6.2%
Space Separator
ValueCountFrequency (%)
4240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19848
50.3%
Hangul 19588
49.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 4070
20.8%
대 1660
 
8.5%
길 1638
 
8.4%
번 1486
 
7.6%
동 512
 
2.6%
산 497
 
2.5%
중 323
 
1.6%
낙 295
 
1.5%
수 288
 
1.5%
앙 280
 
1.4%
Other values (200) 8539
43.6%
Common
ValueCountFrequency (%)
4240
21.4%
1 3157
15.9%
2 1820
9.2%
3 1757
8.9%
4 1434
 
7.2%
5 1366
 
6.9%
7 1216
 
6.1%
6 1202
 
6.1%
9 1081
 
5.4%
8 980
 
4.9%
Other values (2) 1595
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19848
50.3%
Hangul 19588
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4240
21.4%
1 3157
15.9%
2 1820
9.2%
3 1757
8.9%
4 1434
 
7.2%
5 1366
 
6.9%
7 1216
 
6.1%
6 1202
 
6.1%
9 1081
 
5.4%
8 980
 
4.9%
Other values (2) 1595
 
8.0%
Hangul
ValueCountFrequency (%)
로 4070
20.8%
대 1660
 
8.5%
길 1638
 
8.4%
번 1486
 
7.6%
동 512
 
2.6%
산 497
 
2.5%
중 323
 
1.6%
낙 295
 
1.5%
수 288
 
1.5%
앙 280
 
1.4%
Other values (200) 8539
43.6%

교차로명
Text

MISSING 

Distinct1169
Distinct (%)24.1%
Missing5141
Missing (%)51.4%
Memory size156.2 KiB
2023-12-12T17:56:26.868781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.0714139
Min length3

Characters and Unicode

Total characters34360
Distinct characters428
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

Unique223 ?
Unique (%)4.6%

Sample

1st row콘티코(터미널주유소직결)
2nd row녹산1번신호대
3rd row신호공단3-1지점
4th row삼성여객
5th row녹산공단14지점(녹산 21번 신호대)
ValueCountFrequency (%)
앞 71
 
1.3%
주변 33
 
0.6%
명지주거단지 26
 
0.5%
화명동 25
 
0.5%
49호광장(도시가스 23
 
0.4%
올림픽동산 22
 
0.4%
임해단지(3)명지동리 22
 
0.4%
센텀시티 20
 
0.4%
부산은행)메트로프라자 20
 
0.4%
연산로타리 19
 
0.3%
Other values (1262) 5259
94.9%
2023-12-12T17:56:27.429261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 1018
 
3.0%
( 905
 
2.6%
) 901
 
2.6%
지 895
 
2.6%
구 771
 
2.2%
683
 
2.0%
산 659
 
1.9%
동 657
 
1.9%
로 649
 
1.9%
대 600
 
1.7%
Other values (418) 26622
77.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29271
85.2%
Decimal Number 1741
 
5.1%
Open Punctuation 905
 
2.6%
Close Punctuation 901
 
2.6%
Space Separator 683
 
2.0%
Uppercase Letter 635
 
1.8%
Dash Punctuation 99
 
0.3%
Other Punctuation 86
 
0.3%
Other Symbol 27
 
0.1%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 1018
 
3.5%
지 895
 
3.1%
구 771
 
2.6%
산 659
 
2.3%
동 657
 
2.2%
로 649
 
2.2%
대 600
 
2.0%
교 560
 
1.9%
타 497
 
1.7%
거 493
 
1.7%
Other values (377) 22472
76.8%
Uppercase Letter
ValueCountFrequency (%)
P 98
15.4%
B 81
12.8%
C 76
12.0%
I 56
8.8%
A 44
6.9%
T 44
6.9%
L 42
6.6%
E 38
 
6.0%
S 32
 
5.0%
G 26
 
4.1%
Other values (11) 98
15.4%
Decimal Number
ValueCountFrequency (%)
2 434
24.9%
1 407
23.4%
3 227
13.0%
4 186
10.7%
6 128
 
7.4%
5 78
 
4.5%
0 73
 
4.2%
9 72
 
4.1%
8 69
 
4.0%
7 67
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 41
47.7%
. 29
33.7%
: 16
 
18.6%
Open Punctuation
ValueCountFrequency (%)
( 905
100.0%
Close Punctuation
ValueCountFrequency (%)
) 901
100.0%
Space Separator
ValueCountFrequency (%)
683
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 27
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29298
85.3%
Common 4421
 
12.9%
Latin 641
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 1018
 
3.5%
지 895
 
3.1%
구 771
 
2.6%
산 659
 
2.2%
동 657
 
2.2%
로 649
 
2.2%
대 600
 
2.0%
교 560
 
1.9%
타 497
 
1.7%
거 493
 
1.7%
Other values (378) 22499
76.8%
Latin
ValueCountFrequency (%)
P 98
15.3%
B 81
12.6%
C 76
11.9%
I 56
8.7%
A 44
6.9%
T 44
6.9%
L 42
6.6%
E 38
 
5.9%
S 32
 
5.0%
G 26
 
4.1%
Other values (12) 104
16.2%
Common
ValueCountFrequency (%)
( 905
20.5%
) 901
20.4%
683
15.4%
2 434
9.8%
1 407
9.2%
3 227
 
5.1%
4 186
 
4.2%
6 128
 
2.9%
- 99
 
2.2%
5 78
 
1.8%
Other values (8) 373
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29271
85.2%
ASCII 5062
 
14.7%
None 27
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 1018
 
3.5%
지 895
 
3.1%
구 771
 
2.6%
산 659
 
2.3%
동 657
 
2.2%
로 649
 
2.2%
대 600
 
2.0%
교 560
 
1.9%
타 497
 
1.7%
거 493
 
1.7%
Other values (377) 22472
76.8%
ASCII
ValueCountFrequency (%)
( 905
17.9%
) 901
17.8%
683
13.5%
2 434
8.6%
1 407
8.0%
3 227
 
4.5%
4 186
 
3.7%
6 128
 
2.5%
- 99
 
2.0%
P 98
 
1.9%
Other values (30) 994
19.6%
None
ValueCountFrequency (%)
㈜ 27
100.0%

경도
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9965
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.0418
Minimum121.64673
Maximum129.30304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:56:27.618415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121.64673
5-th percentile128.85418
Q1128.97409
median129.05341
Q3129.11629
95-th percentile129.21337
Maximum129.30304
Range7.6563154
Interquartile range (IQR)0.14219893

Descriptive statistics

Standard deviation0.14677033
Coefficient of variation (CV)0.0011373859
Kurtosis1287.1489
Mean129.0418
Median Absolute Deviation (MAD)0.0712814
Skewness-25.61932
Sum1290418
Variance0.021541529
MonotonicityNot monotonic
2023-12-12T17:56:27.827763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.8394287 2
 
< 0.1%
128.8395205 2
 
< 0.1%
129.1192339 2
 
< 0.1%
128.8393639 2
 
< 0.1%
129.1117412 2
 
< 0.1%
129.1616433 2
 
< 0.1%
128.9724843 2
 
< 0.1%
128.8460973 2
 
< 0.1%
128.8626184 2
 
< 0.1%
129.1763255 2
 
< 0.1%
Other values (9955) 9980
99.8%
ValueCountFrequency (%)
121.6467255 1
< 0.1%
121.6476386 1
< 0.1%
128.8107704 1
< 0.1%
128.8110354 1
< 0.1%
128.8113928 1
< 0.1%
128.8115375 1
< 0.1%
128.8115386 1
< 0.1%
128.8124987 1
< 0.1%
128.8129721 1
< 0.1%
128.8153223 1
< 0.1%
ValueCountFrequency (%)
129.3030409 1
< 0.1%
129.3029967 1
< 0.1%
129.3029865 1
< 0.1%
129.3029603 1
< 0.1%
129.3026504 1
< 0.1%
129.30265 1
< 0.1%
129.3026325 1
< 0.1%
129.3023298 1
< 0.1%
129.3021414 1
< 0.1%
129.2930772 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9992
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.169875
Minimum21.581028
Maximum35.374892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:56:28.049166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.581028
5-th percentile35.086881
Q135.127728
median35.167462
Q335.204338
95-th percentile35.318324
Maximum35.374892
Range13.793865
Interquartile range (IQR)0.07661041

Descriptive statistics

Standard deviation0.20235546
Coefficient of variation (CV)0.0057536588
Kurtosis4066.7814
Mean35.169875
Median Absolute Deviation (MAD)0.03810617
Skewness-60.555529
Sum351698.75
Variance0.040947732
MonotonicityNot monotonic
2023-12-12T17:56:28.239655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.13391298 2
 
< 0.1%
35.22047795 2
 
< 0.1%
35.13382904 2
 
< 0.1%
35.07997356 2
 
< 0.1%
35.08947316 2
 
< 0.1%
35.3208908 2
 
< 0.1%
35.20461756 2
 
< 0.1%
35.23586265 2
 
< 0.1%
35.34005485 1
 
< 0.1%
35.14819066 1
 
< 0.1%
Other values (9982) 9982
99.8%
ValueCountFrequency (%)
21.58102756 1
< 0.1%
21.58160919 1
< 0.1%
35.02273309 1
< 0.1%
35.02298247 1
< 0.1%
35.02314067 1
< 0.1%
35.03028431 1
< 0.1%
35.03037981 1
< 0.1%
35.03075302 1
< 0.1%
35.03171727 1
< 0.1%
35.03173574 1
< 0.1%
ValueCountFrequency (%)
35.37489213 1
< 0.1%
35.37287032 1
< 0.1%
35.371856 1
< 0.1%
35.37122973 1
< 0.1%
35.36634264 1
< 0.1%
35.36594499 1
< 0.1%
35.35901704 1
< 0.1%
35.35897251 1
< 0.1%
35.35856533 1
< 0.1%
35.35851212 1
< 0.1%

Interactions

2023-12-12T17:56:22.876093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:22.126104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:22.509381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:22.989649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:22.266175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:22.628152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:23.105044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:22.393551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:22.752019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:56:28.348317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호시군구명리명경도위도
번호1.0000.8740.9110.0390.039
시군구명0.8741.0000.7550.0000.000
리명0.9110.7551.000NaNNaN
경도0.0390.000NaN1.0000.924
위도0.0390.000NaN0.9241.000
2023-12-12T17:56:28.462244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
리명시군구명
리명1.0000.634
시군구명0.6341.000
2023-12-12T17:56:28.598086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호경도위도시군구명리명
번호1.0000.5680.4200.5910.701
경도0.5681.0000.5980.0001.000
위도0.4200.5981.0000.0001.000
시군구명0.5910.0000.0001.0000.634
리명0.7011.0001.0000.6341.000

Missing values

2023-12-12T17:56:23.260948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:56:23.409859image/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.
2023-12-12T17:56:23.542984image/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
2142321424사상구괘법동<NA>광장로 10콘티코(터미널주유소직결)128.97863735.163023
76547655강서구송정동<NA><NA><NA>128.83922135.083732
2003220033기장군정관읍달산리<NA><NA>129.18292935.316537
84178418강서구송정동<NA>르노삼성대로 61녹산1번신호대128.86967635.089006
85238524강서구신호동<NA><NA>신호공단3-1지점128.87305635.087253
2174921750강서구명지동<NA>미음국제3로 31<NA>128.91707735.092702
22442245사하구다대동<NA><NA><NA>128.95665535.057707
1633116332연제구연산동<NA>좌수영로 300삼성여객129.10779535.187862
78677868강서구송정동<NA>르노삼성대로 61녹산공단14지점(녹산 21번 신호대)128.86851435.093629
48474848부산진구가야동<NA><NA>가야공원입구129.02977335.153702
번호시군구명동명리명도로명교차로명경도위도
18471848사하구신평동<NA>신산로 54우주염색(염색공단)128.96025735.089847
82618262강서구송정동<NA>녹산산단231로 47<NA>128.84170435.093434
1077910780부산진구개금동<NA><NA><NA>129.02898635.158338
1150511506남구문현동<NA><NA><NA>129.06943935.136839
2127121272강서구구랑동<NA>미음산단1로 56<NA>128.85840735.123751
13121313사하구하단동<NA><NA><NA>128.93710835.109561
1254812549부산진구개금동<NA><NA><NA>129.02878135.16588
1846718468중구중앙동4가<NA><NA><NA>129.04064835.109359
1004610047북구화명동<NA><NA>화명주공아파트정문129.00823435.220314
60296030연제구연산동<NA><NA><NA>129.08934435.185604