Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows100
Duplicate rows (%)1.0%
Total size in memory644.5 KiB
Average record size in memory66.0 B

Variable types

Categorical1
Text4
Numeric2

Dataset

Description부산광역시_교통시설물관리시스템_교통안전시설물정보(안전표지정보)_20220630
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15084056

Alerts

Dataset has 100 (1.0%) duplicate rowsDuplicates
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 경도 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 16:52:06.903775
Analysis finished2023-12-10 16:52:09.295168
Duration2.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강서구
1387 
기장군
1127 
해운대구
904 
부산진구
799 
북구
797 
Other values (11)
4986 

Length

Max length4
Median length3
Mean length2.9539
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사하구
2nd row부산진구
3rd row동래구
4th row부산진구
5th row남구

Common Values

ValueCountFrequency (%)
강서구 1387
13.9%
기장군 1127
11.3%
해운대구 904
9.0%
부산진구 799
 
8.0%
북구 797
 
8.0%
사하구 739
 
7.4%
동래구 641
 
6.4%
남구 570
 
5.7%
금정구 510
 
5.1%
연제구 502
 
5.0%
Other values (6) 2024
20.2%

Length

2023-12-11T01:52:09.741499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 1387
13.9%
기장군 1127
11.3%
해운대구 904
9.0%
부산진구 799
 
8.0%
북구 797
 
8.0%
사하구 739
 
7.4%
동래구 641
 
6.4%
남구 570
 
5.7%
금정구 510
 
5.1%
연제구 502
 
5.0%
Other values (6) 2024
20.2%

동명
Text

Distinct175
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:52:10.177081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9022
Min length1

Characters and Unicode

Total characters29022
Distinct characters123
Distinct categories3 ?
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 row하단동
2nd row양정동
3rd row명륜동
4th row범천동
5th row문현동
ValueCountFrequency (%)
928
 
9.3%
기장읍 294
 
2.9%
연산동 281
 
2.8%
구포동 232
 
2.3%
정관읍 230
 
2.3%
우동 217
 
2.2%
대저2동 210
 
2.1%
거제동 197
 
2.0%
대저1동 183
 
1.8%
대연동 166
 
1.7%
Other values (165) 7062
70.6%
2023-12-11T01:52:10.872627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8477
29.2%
- 928
 
3.2%
대 822
 
2.8%
장 743
 
2.6%
읍 724
 
2.5%
천 634
 
2.2%
정 603
 
2.1%
가 510
 
1.8%
연 501
 
1.7%
안 434
 
1.5%
Other values (113) 14646
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27266
93.9%
Dash Punctuation 928
 
3.2%
Decimal Number 828
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8477
31.1%
대 822
 
3.0%
장 743
 
2.7%
읍 724
 
2.7%
천 634
 
2.3%
정 603
 
2.2%
가 510
 
1.9%
연 501
 
1.8%
안 434
 
1.6%
구 431
 
1.6%
Other values (105) 13387
49.1%
Decimal Number
ValueCountFrequency (%)
2 329
39.7%
1 281
33.9%
3 118
 
14.3%
4 54
 
6.5%
5 30
 
3.6%
6 15
 
1.8%
7 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27266
93.9%
Common 1756
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8477
31.1%
대 822
 
3.0%
장 743
 
2.7%
읍 724
 
2.7%
천 634
 
2.3%
정 603
 
2.2%
가 510
 
1.9%
연 501
 
1.8%
안 434
 
1.6%
구 431
 
1.6%
Other values (105) 13387
49.1%
Common
ValueCountFrequency (%)
- 928
52.8%
2 329
 
18.7%
1 281
 
16.0%
3 118
 
6.7%
4 54
 
3.1%
5 30
 
1.7%
6 15
 
0.9%
7 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27266
93.9%
ASCII 1756
 
6.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8477
31.1%
대 822
 
3.0%
장 743
 
2.7%
읍 724
 
2.7%
천 634
 
2.3%
정 603
 
2.2%
가 510
 
1.9%
연 501
 
1.8%
안 434
 
1.6%
구 431
 
1.6%
Other values (105) 13387
49.1%
ASCII
ValueCountFrequency (%)
- 928
52.8%
2 329
 
18.7%
1 281
 
16.0%
3 118
 
6.7%
4 54
 
3.1%
5 30
 
1.7%
6 15
 
0.9%
7 1
 
0.1%

리명
Text

Distinct57
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-11T01:52:11.215810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.2019202
Min length1

Characters and Unicode

Total characters12018
Distinct characters70
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
8949
89.5%
대라리 79
 
0.8%
모전리 56
 
0.6%
청강리 51
 
0.5%
동부리 49
 
0.5%
달산리 43
 
0.4%
매학리 43
 
0.4%
명례리 40
 
0.4%
용수리 35
 
0.4%
예림리 31
 
0.3%
Other values (47) 623
 
6.2%
2023-12-11T01:52:11.759916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 8949
74.5%
리 1050
 
8.7%
대 106
 
0.9%
천 101
 
0.8%
전 87
 
0.7%
동 80
 
0.7%
라 79
 
0.7%
부 69
 
0.6%
명 61
 
0.5%
산 60
 
0.5%
Other values (60) 1376
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 8949
74.5%
Other Letter 3069
 
25.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 1050
34.2%
대 106
 
3.5%
천 101
 
3.3%
전 87
 
2.8%
동 80
 
2.6%
라 79
 
2.6%
부 69
 
2.2%
명 61
 
2.0%
산 60
 
2.0%
모 56
 
1.8%
Other values (59) 1320
43.0%
Dash Punctuation
ValueCountFrequency (%)
- 8949
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8949
74.5%
Hangul 3069
 
25.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 1050
34.2%
대 106
 
3.5%
천 101
 
3.3%
전 87
 
2.8%
동 80
 
2.6%
라 79
 
2.6%
부 69
 
2.2%
명 61
 
2.0%
산 60
 
2.0%
모 56
 
1.8%
Other values (59) 1320
43.0%
Common
ValueCountFrequency (%)
- 8949
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8949
74.5%
Hangul 3069
 
25.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 8949
100.0%
Hangul
ValueCountFrequency (%)
리 1050
34.2%
대 106
 
3.5%
천 101
 
3.3%
전 87
 
2.8%
동 80
 
2.6%
라 79
 
2.6%
부 69
 
2.2%
명 61
 
2.0%
산 60
 
2.0%
모 56
 
1.8%
Other values (59) 1320
43.0%

도로명주소
Text

Distinct1424
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:52:12.387918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length1
Mean length2.8184
Min length1

Characters and Unicode

Total characters28184
Distinct characters244
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

Unique1059 ?
Unique (%)10.6%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
7871
64.9%
7 69
 
0.6%
11 49
 
0.4%
9 45
 
0.4%
중앙대로 42
 
0.3%
10 40
 
0.3%
해운대로 38
 
0.3%
16 34
 
0.3%
연수로 28
 
0.2%
종합운동장로28번길 26
 
0.2%
Other values (1588) 3891
32.1%
2023-12-11T01:52:13.081960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 8338
29.6%
2133
 
7.6%
로 2032
 
7.2%
1 1675
 
5.9%
길 1044
 
3.7%
2 996
 
3.5%
번 958
 
3.4%
3 835
 
3.0%
4 748
 
2.7%
5 679
 
2.4%
Other values (234) 8746
31.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9896
35.1%
Dash Punctuation 8338
29.6%
Decimal Number 7817
27.7%
Space Separator 2133
 
7.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 2032
20.5%
길 1044
 
10.5%
번 958
 
9.7%
대 620
 
6.3%
동 285
 
2.9%
수 131
 
1.3%
중 129
 
1.3%
천 128
 
1.3%
앙 119
 
1.2%
해 118
 
1.2%
Other values (222) 4332
43.8%
Decimal Number
ValueCountFrequency (%)
1 1675
21.4%
2 996
12.7%
3 835
10.7%
4 748
9.6%
5 679
8.7%
7 633
 
8.1%
6 624
 
8.0%
9 554
 
7.1%
8 549
 
7.0%
0 524
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 8338
100.0%
Space Separator
ValueCountFrequency (%)
2133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18288
64.9%
Hangul 9896
35.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 2032
20.5%
길 1044
 
10.5%
번 958
 
9.7%
대 620
 
6.3%
동 285
 
2.9%
수 131
 
1.3%
중 129
 
1.3%
천 128
 
1.3%
앙 119
 
1.2%
해 118
 
1.2%
Other values (222) 4332
43.8%
Common
ValueCountFrequency (%)
- 8338
45.6%
2133
 
11.7%
1 1675
 
9.2%
2 996
 
5.4%
3 835
 
4.6%
4 748
 
4.1%
5 679
 
3.7%
7 633
 
3.5%
6 624
 
3.4%
9 554
 
3.0%
Other values (2) 1073
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18288
64.9%
Hangul 9896
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 8338
45.6%
2133
 
11.7%
1 1675
 
9.2%
2 996
 
5.4%
3 835
 
4.6%
4 748
 
4.1%
5 679
 
3.7%
7 633
 
3.5%
6 624
 
3.4%
9 554
 
3.0%
Other values (2) 1073
 
5.9%
Hangul
ValueCountFrequency (%)
로 2032
20.5%
길 1044
 
10.5%
번 958
 
9.7%
대 620
 
6.3%
동 285
 
2.9%
수 131
 
1.3%
중 129
 
1.3%
천 128
 
1.3%
앙 119
 
1.2%
해 118
 
1.2%
Other values (222) 4332
43.8%

교차로명
Text

Distinct1760
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T01:52:13.379856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length1
Mean length3.0814
Min length1

Characters and Unicode

Total characters30814
Distinct characters522
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

Unique947 ?
Unique (%)9.5%

Sample

1st row하구언지하도
2nd row-
3rd row-
4th row철도공작창
5th row-
ValueCountFrequency (%)
6699
62.5%
앞 115
 
1.1%
아시아드코오롱하늘채 28
 
0.3%
옆 20
 
0.2%
서부산유통단지 18
 
0.2%
입구 18
 
0.2%
명지주거단지 18
 
0.2%
후문 18
 
0.2%
화전지구산업단지 17
 
0.2%
주변 16
 
0.1%
Other values (1944) 3743
34.9%
2023-12-11T01:52:13.950704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 6742
 
21.9%
711
 
2.3%
교 626
 
2.0%
동 610
 
2.0%
( 532
 
1.7%
) 528
 
1.7%
지 489
 
1.6%
구 476
 
1.5%
아 440
 
1.4%
대 435
 
1.4%
Other values (512) 19225
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20611
66.9%
Dash Punctuation 6742
 
21.9%
Decimal Number 1231
 
4.0%
Space Separator 711
 
2.3%
Open Punctuation 532
 
1.7%
Close Punctuation 528
 
1.7%
Uppercase Letter 365
 
1.2%
Other Punctuation 61
 
0.2%
Lowercase Letter 20
 
0.1%
Other Symbol 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 626
 
3.0%
동 610
 
3.0%
지 489
 
2.4%
구 476
 
2.3%
아 440
 
2.1%
대 435
 
2.1%
리 433
 
2.1%
트 406
 
2.0%
산 380
 
1.8%
파 367
 
1.8%
Other values (461) 15949
77.4%
Uppercase Letter
ValueCountFrequency (%)
P 62
17.0%
B 47
12.9%
A 41
11.2%
E 32
8.8%
C 31
8.5%
T 30
8.2%
I 27
7.4%
L 17
 
4.7%
K 16
 
4.4%
G 16
 
4.4%
Other values (11) 46
12.6%
Decimal Number
ValueCountFrequency (%)
1 330
26.8%
2 280
22.7%
3 142
11.5%
0 117
 
9.5%
4 99
 
8.0%
5 62
 
5.0%
6 58
 
4.7%
7 53
 
4.3%
8 50
 
4.1%
9 40
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
e 6
30.0%
k 3
15.0%
n 2
 
10.0%
c 2
 
10.0%
s 2
 
10.0%
g 1
 
5.0%
a 1
 
5.0%
t 1
 
5.0%
r 1
 
5.0%
o 1
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 34
55.7%
. 20
32.8%
: 5
 
8.2%
' 2
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 6742
100.0%
Space Separator
ValueCountFrequency (%)
711
100.0%
Open Punctuation
ValueCountFrequency (%)
( 532
100.0%
Close Punctuation
ValueCountFrequency (%)
) 528
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 10
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20621
66.9%
Common 9808
31.8%
Latin 385
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 626
 
3.0%
동 610
 
3.0%
지 489
 
2.4%
구 476
 
2.3%
아 440
 
2.1%
대 435
 
2.1%
리 433
 
2.1%
트 406
 
2.0%
산 380
 
1.8%
파 367
 
1.8%
Other values (462) 15959
77.4%
Latin
ValueCountFrequency (%)
P 62
16.1%
B 47
12.2%
A 41
10.6%
E 32
8.3%
C 31
8.1%
T 30
7.8%
I 27
7.0%
L 17
 
4.4%
K 16
 
4.2%
G 16
 
4.2%
Other values (21) 66
17.1%
Common
ValueCountFrequency (%)
- 6742
68.7%
711
 
7.2%
( 532
 
5.4%
) 528
 
5.4%
1 330
 
3.4%
2 280
 
2.9%
3 142
 
1.4%
0 117
 
1.2%
4 99
 
1.0%
5 62
 
0.6%
Other values (9) 265
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20611
66.9%
ASCII 10193
33.1%
None 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 6742
66.1%
711
 
7.0%
( 532
 
5.2%
) 528
 
5.2%
1 330
 
3.2%
2 280
 
2.7%
3 142
 
1.4%
0 117
 
1.1%
4 99
 
1.0%
P 62
 
0.6%
Other values (40) 650
 
6.4%
Hangul
ValueCountFrequency (%)
교 626
 
3.0%
동 610
 
3.0%
지 489
 
2.4%
구 476
 
2.3%
아 440
 
2.1%
대 435
 
2.1%
리 433
 
2.1%
트 406
 
2.0%
산 380
 
1.8%
파 367
 
1.8%
Other values (461) 15949
77.4%
None
ValueCountFrequency (%)
㈜ 10
100.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9858
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.05579
Minimum128.80143
Maximum129.2954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:52:14.253778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.80143
5-th percentile128.88396
Q1128.99708
median129.06268
Q3129.11094
95-th percentile129.21703
Maximum129.2954
Range0.493972
Interquartile range (IQR)0.1138597

Descriptive statistics

Standard deviation0.093447691
Coefficient of variation (CV)0.00072408755
Kurtosis0.0031274374
Mean129.05579
Median Absolute Deviation (MAD)0.05493105
Skewness-0.13611794
Sum1290557.9
Variance0.0087324709
MonotonicityNot monotonic
2023-12-11T01:52:14.525008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1711161 3
 
< 0.1%
128.9980333 3
 
< 0.1%
129.1967976 3
 
< 0.1%
129.0898581 3
 
< 0.1%
129.0057834 3
 
< 0.1%
129.2102155 3
 
< 0.1%
129.0615914 3
 
< 0.1%
129.0146423 3
 
< 0.1%
129.1822503 2
 
< 0.1%
129.1531256 2
 
< 0.1%
Other values (9848) 9972
99.7%
ValueCountFrequency (%)
128.8014297 1
< 0.1%
128.8040515 1
< 0.1%
128.8042766 1
< 0.1%
128.8059772 1
< 0.1%
128.8066527 1
< 0.1%
128.8070701 1
< 0.1%
128.8074689 1
< 0.1%
128.8099938 1
< 0.1%
128.8113095 1
< 0.1%
128.8113435 1
< 0.1%
ValueCountFrequency (%)
129.2954017 1
< 0.1%
129.292531 1
< 0.1%
129.2854862 1
< 0.1%
129.2854221 1
< 0.1%
129.2852258 1
< 0.1%
129.2849582 1
< 0.1%
129.2849553 1
< 0.1%
129.2847046 1
< 0.1%
129.2844659 1
< 0.1%
129.2844065 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9879
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.174912
Minimum35.008409
Maximum35.384665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T01:52:14.785057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.008409
5-th percentile35.080694
Q135.128276
median35.172149
Q335.210746
95-th percentile35.317553
Maximum35.384665
Range0.37625591
Interquartile range (IQR)0.082470113

Descriptive statistics

Standard deviation0.065781138
Coefficient of variation (CV)0.0018701152
Kurtosis0.37542384
Mean35.174912
Median Absolute Deviation (MAD)0.04046989
Skewness0.5485869
Sum351749.12
Variance0.0043271581
MonotonicityNot monotonic
2023-12-11T01:52:15.054970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.17729694 3
 
< 0.1%
35.20172235 3
 
< 0.1%
35.16677221 3
 
< 0.1%
35.11296578 3
 
< 0.1%
35.18591721 3
 
< 0.1%
35.30347145 2
 
< 0.1%
35.09803967 2
 
< 0.1%
35.10956369 2
 
< 0.1%
35.17638726 2
 
< 0.1%
35.10698775 2
 
< 0.1%
Other values (9869) 9975
99.8%
ValueCountFrequency (%)
35.008409 1
< 0.1%
35.01008756 1
< 0.1%
35.01206222 1
< 0.1%
35.0125878 1
< 0.1%
35.01260224 1
< 0.1%
35.01264861 1
< 0.1%
35.01267015 1
< 0.1%
35.01267443 1
< 0.1%
35.01274803 1
< 0.1%
35.01307338 1
< 0.1%
ValueCountFrequency (%)
35.38466491 1
< 0.1%
35.38393338 1
< 0.1%
35.38180664 1
< 0.1%
35.38112092 1
< 0.1%
35.37768628 1
< 0.1%
35.37766147 1
< 0.1%
35.3770543 1
< 0.1%
35.37699583 1
< 0.1%
35.37694288 1
< 0.1%
35.37693976 1
< 0.1%

Interactions

2023-12-11T01:52:08.446059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:08.120415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:08.651863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:52:08.279876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:52:15.246213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명리명경도위도
시군구명1.0000.6610.8860.839
리명0.6611.0000.8440.876
경도0.8860.8441.0000.798
위도0.8390.8760.7981.000
2023-12-11T01:52:15.404169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도시군구명
경도1.0000.5240.617
위도0.5241.0000.531
시군구명0.6170.5311.000

Missing values

2023-12-11T01:52:08.964976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:52:09.187323image/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.

Sample

시군구명동명리명도로명주소교차로명경도위도
9614사하구하단동--하구언지하도128.95869935.106079
54091부산진구양정동---129.07152635.170529
49740동래구명륜동---129.08461335.214135
25436부산진구범천동--철도공작창129.05482935.151874
46790남구문현동---129.06833435.143731
49244부산진구연지동---129.05355235.17264
24184기장군일광면횡계리--129.23046635.272002
11891사하구다대동--다대롯데캐슬 201동(C지점)128.96481735.059274
33985강서구대저2동---128.95979735.166339
37722북구덕천동---129.00643235.211943
시군구명동명리명도로명주소교차로명경도위도
10025사하구하단동---128.94552935.097954
32692강서구대저1동---128.97419835.212089
9514사하구하단동-제석로 41사하라주유소128.97226235.104303
455기장군철마면와여리-철마초등학교129.14987435.274679
52668북구만덕동---129.04463435.209772
57050연제구거제동---129.0827935.195163
41756영도구봉래동3가--영도아람마트129.04491135.096198
18166북구구포동-시랑로 148-11-129.01115935.195456
55848사하구구평동---128.99035635.079583
12992중구부평동3가--부평로타리129.02492535.099973

Duplicate rows

Most frequently occurring

시군구명동명리명도로명주소교차로명경도위도# duplicates
41북구구포동---129.00578335.2017223
46사상구모라동---128.99803335.1859173
58서구----129.01464235.1129663
92해운대구송정동--제주갈치129.19679835.1772973
99해운대구중동-좌동로10번길 41동일아파트옆129.17111635.1667723
0강서구대저2동--공항파출소128.95346335.1814662
1강서구대저2동--정관마을(공항외각도로)128.9348835.1671082
2금정구금사동--누가정형외과129.11372835.2231432
3금정구금사동-공단서로 30고용노동부129.11107935.2162952
4금정구남산동-범어천로 37-8스마일약국사거리129.08985835.269632