Overview

Dataset statistics

Number of variables12
Number of observations2149
Missing cells3197
Missing cells (%)12.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.1 KiB
Average record size in memory101.1 B

Variable types

Text5
Categorical2
Numeric5

Dataset

Description부산광역시 교통정보서비스센터 교통안전시설물 현황입니다. (관리번호, 시군구명, 동명, 도로명, 교차로명, 경도, 위도 등)
URLhttps://www.data.go.kr/data/15084050/fileData.do

Alerts

구코드 is highly overall correlated with 동코드 and 2 other fieldsHigh correlation
동코드 is highly overall correlated with 구코드 and 3 other fieldsHigh correlation
리코드 is highly overall correlated with 동코드 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
시군구명 is highly overall correlated with 구코드 and 4 other fieldsHigh correlation
리명 is highly overall correlated with 구코드 and 5 other fieldsHigh correlation
리명 is highly imbalanced (83.2%)Imbalance
지번 has 38 (1.8%) missing valuesMissing
도로명 has 1166 (54.3%) missing valuesMissing
리코드 has 1943 (90.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 02:06:40.394274
Analysis finished2023-12-12 02:06:45.428241
Duration5.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

Distinct2142
Distinct (%)100.0%
Missing7
Missing (%)0.3%
Memory size16.9 KiB
2023-12-12T11:06:45.762536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.5270775
Min length5

Characters and Unicode

Total characters11839
Distinct characters14
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

Unique2142 ?
Unique (%)100.0%

Sample

1st row6-020
2nd row11-036
3rd row10-183
4th row10-080
5th row15-115
ValueCountFrequency (%)
15-045 1
 
< 0.1%
13-105 1
 
< 0.1%
13-065 1
 
< 0.1%
13-066 1
 
< 0.1%
13-072 1
 
< 0.1%
13-073 1
 
< 0.1%
13-077 1
 
< 0.1%
13-080 1
 
< 0.1%
13-083 1
 
< 0.1%
13-085 1
 
< 0.1%
Other values (2133) 2133
99.5%
2023-12-12T11:06:46.355196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2493
21.1%
- 2141
18.1%
0 1786
15.1%
2 1161
9.8%
6 738
 
6.2%
5 696
 
5.9%
3 686
 
5.8%
4 587
 
5.0%
9 579
 
4.9%
7 495
 
4.2%
Other values (4) 477
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9695
81.9%
Dash Punctuation 2141
 
18.1%
Other Letter 2
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2493
25.7%
0 1786
18.4%
2 1161
12.0%
6 738
 
7.6%
5 696
 
7.2%
3 686
 
7.1%
4 587
 
6.1%
9 579
 
6.0%
7 495
 
5.1%
8 474
 
4.9%
Other Letter
ValueCountFrequency (%)
월 1
50.0%
일 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 2141
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11837
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2493
21.1%
- 2141
18.1%
0 1786
15.1%
2 1161
9.8%
6 738
 
6.2%
5 696
 
5.9%
3 686
 
5.8%
4 587
 
5.0%
9 579
 
4.9%
7 495
 
4.2%
Other values (2) 475
 
4.0%
Hangul
ValueCountFrequency (%)
월 1
50.0%
일 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11837
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2493
21.1%
- 2141
18.1%
0 1786
15.1%
2 1161
9.8%
6 738
 
6.2%
5 696
 
5.9%
3 686
 
5.8%
4 587
 
5.0%
9 579
 
4.9%
7 495
 
4.2%
Other values (2) 475
 
4.0%
Hangul
ValueCountFrequency (%)
월 1
50.0%
일 1
50.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
강서구
315 
해운대구
235 
기장군
206 
동래구
162 
부산진구
156 
Other values (12)
1075 

Length

Max length4
Median length3
Mean length2.9869707
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동래구
2nd row금정구
3rd row사하구
4th row사하구
5th row사상구

Common Values

ValueCountFrequency (%)
강서구 315
14.7%
해운대구 235
10.9%
기장군 206
9.6%
동래구 162
 
7.5%
부산진구 156
 
7.3%
사하구 155
 
7.2%
사상구 135
 
6.3%
남구 133
 
6.2%
북구 132
 
6.1%
금정구 114
 
5.3%
Other values (7) 406
18.9%

Length

2023-12-12T11:06:46.535070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 315
14.7%
해운대구 235
10.9%
기장군 206
9.6%
동래구 162
 
7.5%
부산진구 156
 
7.3%
사하구 155
 
7.2%
사상구 135
 
6.3%
남구 133
 
6.2%
북구 132
 
6.1%
금정구 114
 
5.3%
Other values (7) 406
18.9%

동명
Text

Distinct149
Distinct (%)7.0%
Missing13
Missing (%)0.6%
Memory size16.9 KiB
2023-12-12T11:06:47.138700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0557116
Min length2

Characters and Unicode

Total characters6527
Distinct characters116
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

Unique27 ?
Unique (%)1.3%

Sample

1st row온천동
2nd row부곡동
3rd row감천동
4th row구평동
5th row모라동
ValueCountFrequency (%)
정관읍 88
 
4.1%
우동 68
 
3.2%
연산동 66
 
3.1%
송정동 57
 
2.7%
명지동 49
 
2.3%
기장읍 46
 
2.2%
반여동 39
 
1.8%
중동 38
 
1.8%
온천동 37
 
1.7%
대저2동 37
 
1.7%
Other values (139) 1611
75.4%
2023-12-12T11:06:47.742722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1989
30.5%
정 193
 
3.0%
읍 177
 
2.7%
장 163
 
2.5%
대 161
 
2.5%
천 124
 
1.9%
명 124
 
1.9%
연 114
 
1.7%
가 105
 
1.6%
전 102
 
1.6%
Other values (106) 3275
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6382
97.8%
Decimal Number 145
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1989
31.2%
정 193
 
3.0%
읍 177
 
2.8%
장 163
 
2.6%
대 161
 
2.5%
천 124
 
1.9%
명 124
 
1.9%
연 114
 
1.8%
가 105
 
1.6%
전 102
 
1.6%
Other values (100) 3130
49.0%
Decimal Number
ValueCountFrequency (%)
2 63
43.4%
1 36
24.8%
3 24
 
16.6%
4 13
 
9.0%
5 5
 
3.4%
6 4
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6382
97.8%
Common 145
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1989
31.2%
정 193
 
3.0%
읍 177
 
2.8%
장 163
 
2.6%
대 161
 
2.5%
천 124
 
1.9%
명 124
 
1.9%
연 114
 
1.8%
가 105
 
1.6%
전 102
 
1.6%
Other values (100) 3130
49.0%
Common
ValueCountFrequency (%)
2 63
43.4%
1 36
24.8%
3 24
 
16.6%
4 13
 
9.0%
5 5
 
3.4%
6 4
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6382
97.8%
ASCII 145
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1989
31.2%
정 193
 
3.0%
읍 177
 
2.8%
장 163
 
2.6%
대 161
 
2.5%
천 124
 
1.9%
명 124
 
1.9%
연 114
 
1.8%
가 105
 
1.6%
전 102
 
1.6%
Other values (100) 3130
49.0%
ASCII
ValueCountFrequency (%)
2 63
43.4%
1 36
24.8%
3 24
 
16.6%
4 13
 
9.0%
5 5
 
3.4%
6 4
 
2.8%

리명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct45
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
<NA>
1943 
용수리
 
22
모전리
 
19
예림리
 
14
매학리
 
13
Other values (40)
 
138

Length

Max length4
Median length4
Mean length3.9022801
Min length2

Unique

Unique12 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1943
90.4%
용수리 22
 
1.0%
모전리 19
 
0.9%
예림리 14
 
0.7%
매학리 13
 
0.6%
달산리 12
 
0.6%
청강리 10
 
0.5%
반룡리 8
 
0.4%
시랑리 7
 
0.3%
기룡리 7
 
0.3%
Other values (35) 94
 
4.4%

Length

2023-12-12T11:06:47.960830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1943
90.4%
용수리 22
 
1.0%
모전리 19
 
0.9%
예림리 14
 
0.7%
매학리 13
 
0.6%
달산리 12
 
0.6%
청강리 10
 
0.5%
반룡리 8
 
0.4%
시랑리 7
 
0.3%
기룡리 7
 
0.3%
Other values (35) 94
 
4.4%

지번
Text

MISSING 

Distinct1758
Distinct (%)83.3%
Missing38
Missing (%)1.8%
Memory size16.9 KiB
2023-12-12T11:06:48.423552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.0606348
Min length1

Characters and Unicode

Total characters10683
Distinct characters39
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1533 ?
Unique (%)72.6%

Sample

1st row1470
2nd row111-8
3rd row789
4th row163-2
5th row75
ValueCountFrequency (%)
174 12
 
0.6%
462 9
 
0.4%
960 8
 
0.4%
02월 8
 
0.4%
01월 8
 
0.4%
185 8
 
0.4%
1486 8
 
0.4%
1488 6
 
0.3%
1499 6
 
0.3%
572-1 6
 
0.3%
Other values (1740) 2082
96.3%
2023-12-12T11:06:49.134632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1805
16.9%
- 1445
13.5%
2 1052
9.8%
3 907
8.5%
4 832
7.8%
5 812
7.6%
7 725
6.8%
6 694
 
6.5%
0 642
 
6.0%
8 624
 
5.8%
Other values (29) 1145
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8667
81.1%
Dash Punctuation 1445
 
13.5%
Lowercase Letter 250
 
2.3%
Other Letter 146
 
1.4%
Uppercase Letter 125
 
1.2%
Space Separator 50
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 45
18.0%
n 36
14.4%
e 33
13.2%
b 28
11.2%
r 24
9.6%
u 21
8.4%
p 16
 
6.4%
c 9
 
3.6%
g 9
 
3.6%
y 7
 
2.8%
Other values (4) 22
8.8%
Decimal Number
ValueCountFrequency (%)
1 1805
20.8%
2 1052
12.1%
3 907
10.5%
4 832
9.6%
5 812
9.4%
7 725
8.4%
6 694
 
8.0%
0 642
 
7.4%
8 624
 
7.2%
9 574
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
J 40
32.0%
F 28
22.4%
A 23
18.4%
M 17
13.6%
N 6
 
4.8%
O 6
 
4.8%
D 3
 
2.4%
S 2
 
1.6%
Other Letter
ValueCountFrequency (%)
산 50
34.2%
월 47
32.2%
일 47
32.2%
번 1
 
0.7%
지 1
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 1445
100.0%
Space Separator
ValueCountFrequency (%)
50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10162
95.1%
Latin 375
 
3.5%
Hangul 146
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 45
12.0%
J 40
10.7%
n 36
9.6%
e 33
8.8%
F 28
 
7.5%
b 28
 
7.5%
r 24
 
6.4%
A 23
 
6.1%
u 21
 
5.6%
M 17
 
4.5%
Other values (12) 80
21.3%
Common
ValueCountFrequency (%)
1 1805
17.8%
- 1445
14.2%
2 1052
10.4%
3 907
8.9%
4 832
8.2%
5 812
8.0%
7 725
7.1%
6 694
 
6.8%
0 642
 
6.3%
8 624
 
6.1%
Other values (2) 624
 
6.1%
Hangul
ValueCountFrequency (%)
산 50
34.2%
월 47
32.2%
일 47
32.2%
번 1
 
0.7%
지 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10537
98.6%
Hangul 146
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1805
17.1%
- 1445
13.7%
2 1052
10.0%
3 907
8.6%
4 832
7.9%
5 812
7.7%
7 725
6.9%
6 694
 
6.6%
0 642
 
6.1%
8 624
 
5.9%
Other values (24) 999
9.5%
Hangul
ValueCountFrequency (%)
산 50
34.2%
월 47
32.2%
일 47
32.2%
번 1
 
0.7%
지 1
 
0.7%

도로명
Text

MISSING 

Distinct826
Distinct (%)84.0%
Missing1166
Missing (%)54.3%
Memory size16.9 KiB
2023-12-12T11:06:49.508592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.7253306
Min length5

Characters and Unicode

Total characters9560
Distinct characters218
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

Unique730 ?
Unique (%)74.3%

Sample

1st row아시아드대로 232
2nd row모라로192번길 50
3rd row모라로110번길 73
4th row모라로 90-7
5th row낙동대로1384번길 43
ValueCountFrequency (%)
14 21
 
1.1%
10 21
 
1.1%
30 20
 
1.0%
11 19
 
1.0%
35 19
 
1.0%
9 19
 
1.0%
16 18
 
0.9%
8 17
 
0.9%
아시아드대로 15
 
0.8%
신선로 15
 
0.8%
Other values (1059) 1781
90.6%
2023-12-12T11:06:49.983622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
982
 
10.3%
로 949
 
9.9%
1 789
 
8.3%
2 521
 
5.4%
길 498
 
5.2%
번 476
 
5.0%
3 446
 
4.7%
4 363
 
3.8%
5 319
 
3.3%
6 274
 
2.9%
Other values (208) 3943
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4630
48.4%
Decimal Number 3715
38.9%
Space Separator 982
 
10.3%
Dash Punctuation 233
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 949
20.5%
길 498
 
10.8%
번 476
 
10.3%
대 249
 
5.4%
동 97
 
2.1%
산 84
 
1.8%
수 61
 
1.3%
신 57
 
1.2%
해 51
 
1.1%
천 48
 
1.0%
Other values (196) 2060
44.5%
Decimal Number
ValueCountFrequency (%)
1 789
21.2%
2 521
14.0%
3 446
12.0%
4 363
9.8%
5 319
8.6%
6 274
 
7.4%
7 271
 
7.3%
0 252
 
6.8%
9 243
 
6.5%
8 237
 
6.4%
Space Separator
ValueCountFrequency (%)
982
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 233
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4930
51.6%
Hangul 4630
48.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 949
20.5%
길 498
 
10.8%
번 476
 
10.3%
대 249
 
5.4%
동 97
 
2.1%
산 84
 
1.8%
수 61
 
1.3%
신 57
 
1.2%
해 51
 
1.1%
천 48
 
1.0%
Other values (196) 2060
44.5%
Common
ValueCountFrequency (%)
982
19.9%
1 789
16.0%
2 521
10.6%
3 446
9.0%
4 363
 
7.4%
5 319
 
6.5%
6 274
 
5.6%
7 271
 
5.5%
0 252
 
5.1%
9 243
 
4.9%
Other values (2) 470
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4930
51.6%
Hangul 4630
48.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
982
19.9%
1 789
16.0%
2 521
10.6%
3 446
9.0%
4 363
 
7.4%
5 319
 
6.5%
6 274
 
5.6%
7 271
 
5.5%
0 252
 
5.1%
9 243
 
4.9%
Other values (2) 470
9.5%
Hangul
ValueCountFrequency (%)
로 949
20.5%
길 498
 
10.8%
번 476
 
10.3%
대 249
 
5.4%
동 97
 
2.1%
산 84
 
1.8%
수 61
 
1.3%
신 57
 
1.2%
해 51
 
1.1%
천 48
 
1.0%
Other values (196) 2060
44.5%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean26383.427
Minimum26110
Maximum26710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:06:50.112423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26110
5-th percentile26170
Q126260
median26380
Q326440
95-th percentile26710
Maximum26710
Range600
Interquartile range (IQR)180

Descriptive statistics

Standard deviation148.78757
Coefficient of variation (CV)0.005639433
Kurtosis0.054089793
Mean26383.427
Median Absolute Deviation (MAD)90
Skewness0.5812757
Sum56592450
Variance22137.74
MonotonicityNot monotonic
2023-12-12T11:06:50.246508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
26440 315
14.7%
26350 235
10.9%
26710 206
9.6%
26260 162
7.5%
26230 156
 
7.3%
26380 155
 
7.2%
26530 135
 
6.3%
26290 133
 
6.2%
26320 132
 
6.1%
26410 114
 
5.3%
Other values (6) 402
18.7%
ValueCountFrequency (%)
26110 37
 
1.7%
26140 69
 
3.2%
26170 52
 
2.4%
26200 74
 
3.4%
26230 156
7.3%
26260 162
7.5%
26290 133
6.2%
26320 132
6.1%
26350 235
10.9%
26380 155
7.2%
ValueCountFrequency (%)
26710 206
9.6%
26530 135
6.3%
26500 69
 
3.2%
26470 101
 
4.7%
26440 315
14.7%
26410 114
 
5.3%
26380 155
7.2%
26350 235
10.9%
26320 132
6.1%
26290 133
6.2%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct150
Distinct (%)7.0%
Missing13
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean26383183
Minimum26110101
Maximum26710330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:06:50.380198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26110101
5-th percentile26170101
Q126260109
median26380102
Q326440117
95-th percentile26710256
Maximum26710330
Range600229
Interquartile range (IQR)180008

Descriptive statistics

Standard deviation148751.85
Coefficient of variation (CV)0.005638131
Kurtosis0.057686463
Mean26383183
Median Absolute Deviation (MAD)89996
Skewness0.58280634
Sum5.635448 × 1010
Variance2.2127112 × 1010
MonotonicityNot monotonic
2023-12-12T11:06:50.551146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26710256 88
 
4.1%
26350105 68
 
3.2%
26470102 66
 
3.1%
26440104 49
 
2.3%
26440109 49
 
2.3%
26710250 46
 
2.1%
26350103 39
 
1.8%
26350106 38
 
1.8%
26260108 37
 
1.7%
26440102 37
 
1.7%
Other values (140) 1619
75.3%
ValueCountFrequency (%)
26110101 5
0.2%
26110103 2
 
0.1%
26110106 1
 
< 0.1%
26110107 6
0.3%
26110108 1
 
< 0.1%
26110109 2
 
0.1%
26110115 1
 
< 0.1%
26110117 3
0.1%
26110120 3
0.1%
26110121 2
 
0.1%
ValueCountFrequency (%)
26710330 15
 
0.7%
26710310 19
 
0.9%
26710256 88
4.1%
26710253 36
1.7%
26710250 46
2.1%
26530108 14
 
0.7%
26530107 24
 
1.1%
26530106 15
 
0.7%
26530105 20
 
0.9%
26530104 13
 
0.6%

리코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)21.4%
Missing1943
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean2.6710264 × 109
Minimum2.671025 × 109
Maximum2.671033 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:06:50.710345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.671025 × 109
5-th percentile2.671025 × 109
Q12.6710253 × 109
median2.6710256 × 109
Q32.6710256 × 109
95-th percentile2.671033 × 109
Maximum2.671033 × 109
Range8009
Interquartile range (IQR)306.25

Descriptive statistics

Standard deviation2407.396
Coefficient of variation (CV)9.0129995 × 10-7
Kurtosis2.4473302
Mean2.6710264 × 109
Median Absolute Deviation (MAD)291.5
Skewness2.0377437
Sum5.5023144 × 1011
Variance5795555.3
MonotonicityNot monotonic
2023-12-12T11:06:50.891527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2671025628 22
 
1.0%
2671025627 19
 
0.9%
2671025630 14
 
0.7%
2671025622 13
 
0.6%
2671025625 12
 
0.6%
2671025027 10
 
0.5%
2671025331 8
 
0.4%
2671025328 7
 
0.3%
2671025033 7
 
0.3%
2671025334 6
 
0.3%
Other values (34) 88
 
4.1%
(Missing) 1943
90.4%
ValueCountFrequency (%)
2671025021 4
 
0.2%
2671025022 1
 
< 0.1%
2671025023 2
 
0.1%
2671025025 4
 
0.2%
2671025026 6
0.3%
2671025027 10
0.5%
2671025029 3
 
0.1%
2671025030 4
 
0.2%
2671025031 3
 
0.1%
2671025032 1
 
< 0.1%
ValueCountFrequency (%)
2671033030 5
0.2%
2671033029 4
0.2%
2671033024 2
 
0.1%
2671033021 4
0.2%
2671031032 3
0.1%
2671031031 1
 
< 0.1%
2671031030 1
 
< 0.1%
2671031029 1
 
< 0.1%
2671031027 1
 
< 0.1%
2671031026 1
 
< 0.1%

교차로명
Text

Distinct2124
Distinct (%)99.4%
Missing13
Missing (%)0.6%
Memory size16.9 KiB
2023-12-12T11:06:51.236580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length7.8497191
Min length3

Characters and Unicode

Total characters16767
Distinct characters512
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2114 ?
Unique (%)99.0%

Sample

1st row동래광혜병원
2nd row장전교
3rd row은광LPG충전소
4th row삼일냉장
5th row모동초교
ValueCountFrequency (%)
앞 38
 
1.5%
명지주거단지 23
 
0.9%
화전지구산업단지 22
 
0.8%
입구 15
 
0.6%
주변 13
 
0.5%
서부산유통단지 8
 
0.3%
정관 6
 
0.2%
주차장 6
 
0.2%
북컨 6
 
0.2%
신항만 6
 
0.2%
Other values (2258) 2465
94.5%
2023-12-12T11:06:51.766346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 549
 
3.3%
480
 
2.9%
동 411
 
2.5%
( 409
 
2.4%
) 394
 
2.3%
구 382
 
2.3%
교 360
 
2.1%
산 286
 
1.7%
1 271
 
1.6%
대 255
 
1.5%
Other values (502) 12970
77.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14008
83.5%
Decimal Number 965
 
5.8%
Space Separator 480
 
2.9%
Open Punctuation 409
 
2.4%
Close Punctuation 394
 
2.3%
Uppercase Letter 311
 
1.9%
Other Punctuation 87
 
0.5%
Dash Punctuation 86
 
0.5%
Lowercase Letter 8
 
< 0.1%
Other Symbol 7
 
< 0.1%
Other values (2) 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 549
 
3.9%
동 411
 
2.9%
구 382
 
2.7%
교 360
 
2.6%
산 286
 
2.0%
대 255
 
1.8%
리 245
 
1.7%
아 243
 
1.7%
트 231
 
1.6%
점 230
 
1.6%
Other values (447) 10816
77.2%
Uppercase Letter
ValueCountFrequency (%)
P 43
13.8%
B 29
9.3%
L 28
9.0%
A 27
8.7%
C 26
8.4%
I 25
8.0%
S 22
 
7.1%
G 21
 
6.8%
T 18
 
5.8%
E 16
 
5.1%
Other values (13) 56
18.0%
Decimal Number
ValueCountFrequency (%)
1 271
28.1%
2 218
22.6%
3 120
12.4%
4 81
 
8.4%
0 74
 
7.7%
5 45
 
4.7%
8 44
 
4.6%
6 40
 
4.1%
7 39
 
4.0%
9 33
 
3.4%
Other Punctuation
ValueCountFrequency (%)
# 38
43.7%
, 22
25.3%
' 13
 
14.9%
. 7
 
8.0%
: 3
 
3.4%
" 2
 
2.3%
/ 1
 
1.1%
@ 1
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
e 2
25.0%
t 2
25.0%
s 1
12.5%
r 1
12.5%
a 1
12.5%
k 1
12.5%
Math Symbol
ValueCountFrequency (%)
~ 6
85.7%
→ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
480
100.0%
Open Punctuation
ValueCountFrequency (%)
( 409
100.0%
Close Punctuation
ValueCountFrequency (%)
) 394
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14015
83.6%
Common 2433
 
14.5%
Latin 319
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 549
 
3.9%
동 411
 
2.9%
구 382
 
2.7%
교 360
 
2.6%
산 286
 
2.0%
대 255
 
1.8%
리 245
 
1.7%
아 243
 
1.7%
트 231
 
1.6%
점 230
 
1.6%
Other values (448) 10823
77.2%
Latin
ValueCountFrequency (%)
P 43
13.5%
B 29
9.1%
L 28
8.8%
A 27
8.5%
C 26
8.2%
I 25
 
7.8%
S 22
 
6.9%
G 21
 
6.6%
T 18
 
5.6%
E 16
 
5.0%
Other values (19) 64
20.1%
Common
ValueCountFrequency (%)
480
19.7%
( 409
16.8%
) 394
16.2%
1 271
11.1%
2 218
9.0%
3 120
 
4.9%
- 86
 
3.5%
4 81
 
3.3%
0 74
 
3.0%
5 45
 
1.8%
Other values (15) 255
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14008
83.5%
ASCII 2751
 
16.4%
None 7
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 549
 
3.9%
동 411
 
2.9%
구 382
 
2.7%
교 360
 
2.6%
산 286
 
2.0%
대 255
 
1.8%
리 245
 
1.7%
아 243
 
1.7%
트 231
 
1.6%
점 230
 
1.6%
Other values (447) 10816
77.2%
ASCII
ValueCountFrequency (%)
480
17.4%
( 409
14.9%
) 394
14.3%
1 271
9.9%
2 218
 
7.9%
3 120
 
4.4%
- 86
 
3.1%
4 81
 
2.9%
0 74
 
2.7%
5 45
 
1.6%
Other values (43) 573
20.8%
None
ValueCountFrequency (%)
㈜ 7
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct2146
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.05077
Minimum128.80937
Maximum129.28232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:06:51.958880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.80937
5-th percentile128.86443
Q1128.99005
median129.06417
Q3129.11115
95-th percentile129.20642
Maximum129.28232
Range0.4729464
Interquartile range (IQR)0.1211017

Descriptive statistics

Standard deviation0.095690188
Coefficient of variation (CV)0.00074149255
Kurtosis-0.11593669
Mean129.05077
Median Absolute Deviation (MAD)0.0564571
Skewness-0.29065555
Sum277330.11
Variance0.0091566121
MonotonicityNot monotonic
2023-12-12T11:06:52.155123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1071612 2
 
0.1%
129.1135287 2
 
0.1%
129.0619358 2
 
0.1%
129.071739 1
 
< 0.1%
129.105428 1
 
< 0.1%
129.082994 1
 
< 0.1%
129.065525 1
 
< 0.1%
129.0853207 1
 
< 0.1%
129.0938835 1
 
< 0.1%
129.0774001 1
 
< 0.1%
Other values (2136) 2136
99.4%
ValueCountFrequency (%)
128.809375 1
< 0.1%
128.8112685 1
< 0.1%
128.8117075 1
< 0.1%
128.8149999 1
< 0.1%
128.8182649 1
< 0.1%
128.8182668 1
< 0.1%
128.8182794 1
< 0.1%
128.8194527 1
< 0.1%
128.8206098 1
< 0.1%
128.8220542 1
< 0.1%
ValueCountFrequency (%)
129.2823214 1
< 0.1%
129.2817077 1
< 0.1%
129.2807305 1
< 0.1%
129.279762 1
< 0.1%
129.2791503 1
< 0.1%
129.2782055 1
< 0.1%
129.2760704 1
< 0.1%
129.275274 1
< 0.1%
129.2667657 1
< 0.1%
129.2631116 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct2148
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.17005
Minimum35.032874
Maximum35.385025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.0 KiB
2023-12-12T11:06:52.355294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.032874
5-th percentile35.08079
Q135.117454
median35.16597
Q335.205889
95-th percentile35.319773
Maximum35.385025
Range0.35215071
Interquartile range (IQR)0.08843576

Descriptive statistics

Standard deviation0.066157117
Coefficient of variation (CV)0.001881064
Kurtosis0.26572092
Mean35.17005
Median Absolute Deviation (MAD)0.04321138
Skewness0.66500234
Sum75580.438
Variance0.0043767641
MonotonicityNot monotonic
2023-12-12T11:06:52.574749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.08897272 2
 
0.1%
35.20702383 1
 
< 0.1%
35.18796467 1
 
< 0.1%
35.18499765 1
 
< 0.1%
35.18269448 1
 
< 0.1%
35.17996458 1
 
< 0.1%
35.17774196 1
 
< 0.1%
35.18543832 1
 
< 0.1%
35.18460778 1
 
< 0.1%
35.19174222 1
 
< 0.1%
Other values (2138) 2138
99.5%
ValueCountFrequency (%)
35.03287393 1
< 0.1%
35.04822271 1
< 0.1%
35.04864736 1
< 0.1%
35.04940199 1
< 0.1%
35.0500333 1
< 0.1%
35.05037964 1
< 0.1%
35.05138362 1
< 0.1%
35.05148706 1
< 0.1%
35.052341 1
< 0.1%
35.05274436 1
< 0.1%
ValueCountFrequency (%)
35.38502464 1
< 0.1%
35.37438438 1
< 0.1%
35.3718886 1
< 0.1%
35.37054625 1
< 0.1%
35.36952996 1
< 0.1%
35.36716722 1
< 0.1%
35.36708401 1
< 0.1%
35.35792011 1
< 0.1%
35.35778279 1
< 0.1%
35.35519333 1
< 0.1%

Interactions

2023-12-12T11:06:44.329029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:41.616651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:42.372384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:43.101766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:43.816639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:44.432892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:41.769970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:42.518810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:43.221845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:43.937978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:44.541188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:41.924354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:42.673977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:43.354645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:44.036151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:44.670121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:42.071378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:42.840242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:43.536569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:44.141912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:44.776546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:42.212982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:42.989215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:43.677518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:06:44.225722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:06:52.724662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명리명구코드동코드리코드경도위도
시군구명1.0000.8361.0001.0000.0780.8940.856
리명0.8361.0000.8360.8051.0001.0000.990
구코드1.0000.8361.0001.0000.0780.8170.738
동코드1.0000.8051.0001.0000.0780.8200.739
리코드0.0781.0000.0780.0781.0000.3190.853
경도0.8941.0000.8170.8200.3191.0000.769
위도0.8560.9900.7380.7390.8530.7691.000
2023-12-12T11:06:52.855661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
리명시군구명
리명1.0000.623
시군구명0.6231.000
2023-12-12T11:06:52.969195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구코드동코드리코드경도위도시군구명리명
구코드1.0000.996-0.0290.0650.3120.9980.623
동코드0.9961.0000.8610.0610.2960.9980.625
리코드-0.0290.8611.000-0.4490.2200.1310.893
경도0.0650.061-0.4491.0000.5710.6320.879
위도0.3120.2960.2200.5711.0000.5570.839
시군구명0.9980.9980.1310.6320.5571.0000.623
리명0.6230.6250.8930.8790.8390.6231.000

Missing values

2023-12-12T11:06:44.956815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:06:45.142874image/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.
2023-12-12T11:06:45.314872image/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

관리번호시군구명동명리명지번도로명구코드동코드리코드교차로명경도위도
06-020동래구온천동<NA>1470아시아드대로 2322626026260108<NA>동래광혜병원129.07173935.207024
111-036금정구부곡동<NA>111-8<NA>2641026410109<NA>장전교129.0879235.240489
210-183사하구감천동<NA>789<NA>2638026380108<NA>은광LPG충전소128.99398135.089724
310-080사하구구평동<NA>163-2<NA>2638026380107<NA>삼일냉장128.98715235.082559
415-115사상구모라동<NA>75모라로192번길 502653026530102<NA>모동초교129.00166335.184615
515-114사상구모라동<NA>552모라로110번길 732653026530102<NA>모산초교128.99588135.184413
615-113사상구모라동<NA>1285모라로 90-72653026530102<NA>신모라사거리128.99112735.186138
715-111사상구삼락동<NA>363-2낙동대로1384번길 432653026530101<NA>삼락초교128.97949435.182467
85-143부산진구양정동<NA>155<NA>2623026230101<NA>동호여상129.0768935.175101
915-112사상구모라동<NA>340-5낙동대로1412번길 842653026530102<NA>(주)농심128.98250935.185975
관리번호시군구명동명리명지번도로명구코드동코드리코드교차로명경도위도
21396-187동래구사직동<NA>1058아시아드대로 134번길 142626026260109<NA>쌍용 플래티넘 사직아시아드 차량진129.06664435.19649
214013-210연제구연산동<NA>1310-41중앙천로 812647026470102<NA>물꽁아구찜앞129.07972735.181082
21416-1550동래구온천동<NA>1550번지<NA>2626026260108<NA>동래아시아드 이편한세상129.06673435.202452
2142<NA>부산진구전포동<NA>908동성로502623026230102<NA>서면아이파크 입구129.0735.1548
2143<NA>북구화명동<NA>2124산성로27-122632026320102<NA>화명푸르지오헤리센트129.016735.2398
2144<NA>북구화명동<NA>417-2산성로48번길2632026320102<NA>화명푸르지오헤리센트129.01835.2389
2145<NA>동래구온천동<NA>1850온천장로65번길 92626026260108<NA><NA>129.081435.2176
2146<NA>동래구온천동<NA>186-60차밭골로20번길112626026260108<NA><NA>129.079835.2168
2147<NA>강서구명지동<NA>3268<NA>2644026440104<NA><NA>128.899435.0895
2148<NA>수영구민락동<NA>181-78광안해변로294번길2650026500103<NA><NA>129.126635.1542