Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells14421
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Numeric3
Categorical1
Text4

Dataset

Description부산광역시_교통시설물관리시스템_교통안전시설물정보(철주정보)에 대한 데이터로 번호, 시군구명, 동명, 리명, 도로명, 교차로명, 위도, 경도 항목정보를 제공합니다.
Author부산광역시
URLhttps://www.data.go.kr/data/15084058/fileData.do

Alerts

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
리명 has 8964 (89.6%) missing valuesMissing
도로명 has 5356 (53.6%) missing valuesMissing
교차로명 has 101 (1.0%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:45:14.666906
Analysis finished2023-12-12 14:45:17.468948
Duration2.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7030.5819
Minimum1
Maximum14050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:45:17.536570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile713.95
Q13517.75
median7029.5
Q310519.5
95-th percentile13325.05
Maximum14050
Range14049
Interquartile range (IQR)7001.75

Descriptive statistics

Standard deviation4044.6626
Coefficient of variation (CV)0.57529556
Kurtosis-1.1999849
Mean7030.5819
Median Absolute Deviation (MAD)3501.5
Skewness-0.0042236879
Sum70305819
Variance16359295
MonotonicityNot monotonic
2023-12-12T23:45:17.666961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9549 1
 
< 0.1%
1189 1
 
< 0.1%
4210 1
 
< 0.1%
2508 1
 
< 0.1%
6759 1
 
< 0.1%
11669 1
 
< 0.1%
1796 1
 
< 0.1%
8630 1
 
< 0.1%
1390 1
 
< 0.1%
10757 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
14050 1
< 0.1%
14047 1
< 0.1%
14046 1
< 0.1%
14043 1
< 0.1%
14042 1
< 0.1%
14040 1
< 0.1%
14038 1
< 0.1%
14036 1
< 0.1%
14034 1
< 0.1%
14033 1
< 0.1%

시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강서구
1390 
해운대구
1122 
기장군
1042 
부산진구
877 
사하구
742 
Other values (11)
4827 

Length

Max length4
Median length3
Mean length3.0012
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기장군
2nd row강서구
3rd row영도구
4th row동래구
5th row금정구

Common Values

ValueCountFrequency (%)
강서구 1390
13.9%
해운대구 1122
11.2%
기장군 1042
10.4%
부산진구 877
8.8%
사하구 742
 
7.4%
남구 626
 
6.3%
동래구 613
 
6.1%
북구 610
 
6.1%
사상구 586
 
5.9%
금정구 491
 
4.9%
Other values (6) 1901
19.0%

Length

2023-12-12T23:45:17.810788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 1390
13.9%
해운대구 1122
11.2%
기장군 1042
10.4%
부산진구 877
8.8%
사하구 742
 
7.4%
남구 626
 
6.3%
동래구 613
 
6.1%
북구 610
 
6.1%
사상구 586
 
5.9%
금정구 491
 
4.9%
Other values (6) 1901
19.0%

동명
Text

Distinct164
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T23:45:18.089627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0663
Min length1

Characters and Unicode

Total characters30663
Distinct characters120
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 row정관읍
2nd row신호동
3rd row봉래동1가
4th row안락동
5th row장전동
ValueCountFrequency (%)
정관읍 408
 
4.1%
우동 347
 
3.5%
송정동 330
 
3.3%
기장읍 240
 
2.4%
연산동 237
 
2.4%
장안읍 231
 
2.3%
거제동 209
 
2.1%
용호동 182
 
1.8%
반여동 173
 
1.7%
다대동 171
 
1.7%
Other values (154) 7472
74.7%
2023-12-12T23:45:18.480585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9210
30.0%
정 943
 
3.1%
읍 912
 
3.0%
장 819
 
2.7%
대 784
 
2.6%
송 567
 
1.8%
가 562
 
1.8%
천 541
 
1.8%
전 522
 
1.7%
안 499
 
1.6%
Other values (110) 15304
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29924
97.6%
Decimal Number 729
 
2.4%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9210
30.8%
정 943
 
3.2%
읍 912
 
3.0%
장 819
 
2.7%
대 784
 
2.6%
송 567
 
1.9%
가 562
 
1.9%
천 541
 
1.8%
전 522
 
1.7%
안 499
 
1.7%
Other values (103) 14565
48.7%
Decimal Number
ValueCountFrequency (%)
2 307
42.1%
1 203
27.8%
3 129
17.7%
4 49
 
6.7%
5 24
 
3.3%
6 17
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29924
97.6%
Common 739
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9210
30.8%
정 943
 
3.2%
읍 912
 
3.0%
장 819
 
2.7%
대 784
 
2.6%
송 567
 
1.9%
가 562
 
1.9%
천 541
 
1.8%
전 522
 
1.7%
안 499
 
1.7%
Other values (103) 14565
48.7%
Common
ValueCountFrequency (%)
2 307
41.5%
1 203
27.5%
3 129
17.5%
4 49
 
6.6%
5 24
 
3.2%
6 17
 
2.3%
- 10
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29924
97.6%
ASCII 739
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9210
30.8%
정 943
 
3.2%
읍 912
 
3.0%
장 819
 
2.7%
대 784
 
2.6%
송 567
 
1.9%
가 562
 
1.9%
천 541
 
1.8%
전 522
 
1.7%
안 499
 
1.7%
Other values (103) 14565
48.7%
ASCII
ValueCountFrequency (%)
2 307
41.5%
1 203
27.5%
3 129
17.5%
4 49
 
6.6%
5 24
 
3.2%
6 17
 
2.3%
- 10
 
1.4%

리명
Text

MISSING 

Distinct54
Distinct (%)5.2%
Missing8964
Missing (%)89.6%
Memory size156.2 KiB
2023-12-12T23:45:18.671316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9749035
Min length2

Characters and Unicode

Total characters3082
Distinct characters67
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

Unique6 ?
Unique (%)0.6%

Sample

1st row모전리
2nd row대라리
3rd row청강리
4th row동백리
5th row와여리
ValueCountFrequency (%)
모전리 92
 
8.9%
용수리 77
 
7.4%
달산리 65
 
6.3%
반룡리 64
 
6.2%
매학리 58
 
5.6%
예림리 55
 
5.3%
대라리 45
 
4.3%
청강리 44
 
4.2%
기룡리 38
 
3.7%
명례리 38
 
3.7%
Other values (44) 460
44.4%
2023-12-12T23:45:18.953232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 1036
33.6%
룡 102
 
3.3%
전 101
 
3.3%
모 92
 
3.0%
용 86
 
2.8%
산 81
 
2.6%
수 77
 
2.5%
천 75
 
2.4%
명 69
 
2.2%
달 65
 
2.1%
Other values (57) 1298
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3082
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 1036
33.6%
룡 102
 
3.3%
전 101
 
3.3%
모 92
 
3.0%
용 86
 
2.8%
산 81
 
2.6%
수 77
 
2.5%
천 75
 
2.4%
명 69
 
2.2%
달 65
 
2.1%
Other values (57) 1298
42.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3082
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 1036
33.6%
룡 102
 
3.3%
전 101
 
3.3%
모 92
 
3.0%
용 86
 
2.8%
산 81
 
2.6%
수 77
 
2.5%
천 75
 
2.4%
명 69
 
2.2%
달 65
 
2.1%
Other values (57) 1298
42.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3082
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 1036
33.6%
룡 102
 
3.3%
전 101
 
3.3%
모 92
 
3.0%
용 86
 
2.8%
산 81
 
2.6%
수 77
 
2.5%
천 75
 
2.4%
명 69
 
2.2%
달 65
 
2.1%
Other values (57) 1298
42.1%

도로명
Text

MISSING 

Distinct2389
Distinct (%)51.4%
Missing5356
Missing (%)53.6%
Memory size156.2 KiB
2023-12-12T23:45:19.210909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.2112403
Min length5

Characters and Unicode

Total characters42777
Distinct characters261
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1519 ?
Unique (%)32.7%

Sample

1st row르노삼성대로 61
2nd row수림로81번길 1
3rd row충무대로 181
4th row복지로21번길 6
5th row청학북로 76-1
ValueCountFrequency (%)
중앙대로 119
 
1.3%
9 104
 
1.1%
16 94
 
1.0%
10 85
 
0.9%
11 85
 
0.9%
해운대로 77
 
0.8%
14 72
 
0.8%
8 70
 
0.8%
7 69
 
0.7%
12 62
 
0.7%
Other values (2226) 8451
91.0%
2023-12-12T23:45:19.575055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4644
 
10.9%
로 4486
 
10.5%
1 3549
 
8.3%
2 2169
 
5.1%
3 1834
 
4.3%
길 1833
 
4.3%
번 1724
 
4.0%
4 1535
 
3.6%
5 1375
 
3.2%
7 1335
 
3.1%
Other values (251) 18293
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20810
48.6%
Decimal Number 16318
38.1%
Space Separator 4644
 
10.9%
Dash Punctuation 977
 
2.3%
Uppercase Letter 28
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 4486
21.6%
길 1833
 
8.8%
번 1724
 
8.3%
대 1251
 
6.0%
산 456
 
2.2%
동 399
 
1.9%
수 260
 
1.2%
신 246
 
1.2%
중 243
 
1.2%
천 236
 
1.1%
Other values (235) 9676
46.5%
Decimal Number
ValueCountFrequency (%)
1 3549
21.7%
2 2169
13.3%
3 1834
11.2%
4 1535
9.4%
5 1375
 
8.4%
7 1335
 
8.2%
6 1291
 
7.9%
9 1117
 
6.8%
8 1093
 
6.7%
0 1020
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
C 7
25.0%
A 7
25.0%
P 7
25.0%
E 7
25.0%
Space Separator
ValueCountFrequency (%)
4644
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 977
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21939
51.3%
Hangul 20810
48.6%
Latin 28
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 4486
21.6%
길 1833
 
8.8%
번 1724
 
8.3%
대 1251
 
6.0%
산 456
 
2.2%
동 399
 
1.9%
수 260
 
1.2%
신 246
 
1.2%
중 243
 
1.2%
천 236
 
1.1%
Other values (235) 9676
46.5%
Common
ValueCountFrequency (%)
4644
21.2%
1 3549
16.2%
2 2169
9.9%
3 1834
 
8.4%
4 1535
 
7.0%
5 1375
 
6.3%
7 1335
 
6.1%
6 1291
 
5.9%
9 1117
 
5.1%
8 1093
 
5.0%
Other values (2) 1997
9.1%
Latin
ValueCountFrequency (%)
C 7
25.0%
A 7
25.0%
P 7
25.0%
E 7
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21967
51.4%
Hangul 20810
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4644
21.1%
1 3549
16.2%
2 2169
9.9%
3 1834
 
8.3%
4 1535
 
7.0%
5 1375
 
6.3%
7 1335
 
6.1%
6 1291
 
5.9%
9 1117
 
5.1%
8 1093
 
5.0%
Other values (6) 2025
9.2%
Hangul
ValueCountFrequency (%)
로 4486
21.6%
길 1833
 
8.8%
번 1724
 
8.3%
대 1251
 
6.0%
산 456
 
2.2%
동 399
 
1.9%
수 260
 
1.2%
신 246
 
1.2%
중 243
 
1.2%
천 236
 
1.1%
Other values (235) 9676
46.5%

교차로명
Text

MISSING 

Distinct3305
Distinct (%)33.4%
Missing101
Missing (%)1.0%
Memory size156.2 KiB
2023-12-12T23:45:19.824170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length7.8663501
Min length2

Characters and Unicode

Total characters77869
Distinct characters587
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1326 ?
Unique (%)13.4%

Sample

1st row정관신도시( 더샵5)
2nd row삼성자동차주차장
3rd row부산대교위
4th row안락주공205동
5th row대진정보통신고교
ValueCountFrequency (%)
앞 256
 
2.0%
정관신도시 163
 
1.3%
화전지구산업단지 132
 
1.1%
명지주거단지 68
 
0.5%
서부산유통단지 60
 
0.5%
입구 54
 
0.4%
주변 48
 
0.4%
옆 36
 
0.3%
장안일반산업단지 34
 
0.3%
후문 31
 
0.2%
Other values (3568) 11660
93.0%
2023-12-12T23:45:20.188127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2822
 
3.6%
지 2522
 
3.2%
) 1795
 
2.3%
( 1790
 
2.3%
구 1774
 
2.3%
동 1724
 
2.2%
교 1525
 
2.0%
산 1389
 
1.8%
리 1370
 
1.8%
1 1340
 
1.7%
Other values (577) 59818
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64946
83.4%
Decimal Number 4577
 
5.9%
Space Separator 2822
 
3.6%
Close Punctuation 1795
 
2.3%
Open Punctuation 1790
 
2.3%
Uppercase Letter 1317
 
1.7%
Dash Punctuation 373
 
0.5%
Other Punctuation 153
 
0.2%
Lowercase Letter 46
 
0.1%
Math Symbol 30
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 2522
 
3.9%
구 1774
 
2.7%
동 1724
 
2.7%
교 1525
 
2.3%
산 1389
 
2.1%
리 1370
 
2.1%
대 1241
 
1.9%
아 1139
 
1.8%
트 1126
 
1.7%
장 1045
 
1.6%
Other values (518) 50091
77.1%
Uppercase Letter
ValueCountFrequency (%)
P 181
13.7%
B 142
10.8%
L 111
8.4%
A 103
 
7.8%
G 100
 
7.6%
C 100
 
7.6%
E 89
 
6.8%
S 88
 
6.7%
I 78
 
5.9%
T 63
 
4.8%
Other values (14) 262
19.9%
Decimal Number
ValueCountFrequency (%)
1 1340
29.3%
2 1038
22.7%
3 527
 
11.5%
4 419
 
9.2%
0 343
 
7.5%
8 206
 
4.5%
6 204
 
4.5%
5 190
 
4.2%
7 165
 
3.6%
9 145
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
e 8
17.4%
k 8
17.4%
r 6
13.0%
a 6
13.0%
n 4
8.7%
s 4
8.7%
t 4
8.7%
c 2
 
4.3%
g 2
 
4.3%
o 2
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 96
62.7%
. 24
 
15.7%
: 13
 
8.5%
" 8
 
5.2%
/ 6
 
3.9%
@ 3
 
2.0%
' 3
 
2.0%
Math Symbol
ValueCountFrequency (%)
~ 26
86.7%
> 3
 
10.0%
→ 1
 
3.3%
Space Separator
ValueCountFrequency (%)
2822
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1795
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1790
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 373
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64966
83.4%
Common 11540
 
14.8%
Latin 1363
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 2522
 
3.9%
구 1774
 
2.7%
동 1724
 
2.7%
교 1525
 
2.3%
산 1389
 
2.1%
리 1370
 
2.1%
대 1241
 
1.9%
아 1139
 
1.8%
트 1126
 
1.7%
장 1045
 
1.6%
Other values (519) 50111
77.1%
Latin
ValueCountFrequency (%)
P 181
13.3%
B 142
10.4%
L 111
 
8.1%
A 103
 
7.6%
G 100
 
7.3%
C 100
 
7.3%
E 89
 
6.5%
S 88
 
6.5%
I 78
 
5.7%
T 63
 
4.6%
Other values (24) 308
22.6%
Common
ValueCountFrequency (%)
2822
24.5%
) 1795
15.6%
( 1790
15.5%
1 1340
11.6%
2 1038
 
9.0%
3 527
 
4.6%
4 419
 
3.6%
- 373
 
3.2%
0 343
 
3.0%
8 206
 
1.8%
Other values (14) 887
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64943
83.4%
ASCII 12902
 
16.6%
None 20
 
< 0.1%
Compat Jamo 3
 
< 0.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2822
21.9%
) 1795
13.9%
( 1790
13.9%
1 1340
10.4%
2 1038
 
8.0%
3 527
 
4.1%
4 419
 
3.2%
- 373
 
2.9%
0 343
 
2.7%
8 206
 
1.6%
Other values (47) 2249
17.4%
Hangul
ValueCountFrequency (%)
지 2522
 
3.9%
구 1774
 
2.7%
동 1724
 
2.7%
교 1525
 
2.3%
산 1389
 
2.1%
리 1370
 
2.1%
대 1241
 
1.9%
아 1139
 
1.8%
트 1126
 
1.7%
장 1045
 
1.6%
Other values (517) 50088
77.1%
None
ValueCountFrequency (%)
㈜ 20
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 3
100.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9980
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.05281
Minimum128.80933
Maximum129.29285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:45:20.330769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.80933
5-th percentile128.86127
Q1128.99306
median129.06298
Q3129.11339
95-th percentile129.21244
Maximum129.29285
Range0.4835234
Interquartile range (IQR)0.12032752

Descriptive statistics

Standard deviation0.09632809
Coefficient of variation (CV)0.00074642383
Kurtosis-0.086845182
Mean129.05281
Median Absolute Deviation (MAD)0.05689055
Skewness-0.2779883
Sum1290528.1
Variance0.0092791009
MonotonicityNot monotonic
2023-12-12T23:45:20.467261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.082594 2
 
< 0.1%
129.122912 2
 
< 0.1%
129.0198563 2
 
< 0.1%
128.9982203 2
 
< 0.1%
129.1252413 2
 
< 0.1%
129.0088501 2
 
< 0.1%
129.0223803 2
 
< 0.1%
129.1110769 2
 
< 0.1%
129.0839208 2
 
< 0.1%
129.023938 2
 
< 0.1%
Other values (9970) 9980
99.8%
ValueCountFrequency (%)
128.8093305 1
< 0.1%
128.8111163 1
< 0.1%
128.811201 1
< 0.1%
128.8112184 1
< 0.1%
128.8113454 1
< 0.1%
128.8115862 1
< 0.1%
128.814751 1
< 0.1%
128.8149081 1
< 0.1%
128.8150497 1
< 0.1%
128.8151915 1
< 0.1%
ValueCountFrequency (%)
129.2928539 1
< 0.1%
129.2850185 1
< 0.1%
129.2848969 1
< 0.1%
129.2848634 1
< 0.1%
129.2830822 1
< 0.1%
129.282958 1
< 0.1%
129.2829247 1
< 0.1%
129.2828849 1
< 0.1%
129.2828829 1
< 0.1%
129.2817713 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.170248
Minimum35.032474
Maximum35.385072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:45:20.601880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.032474
5-th percentile35.080668
Q135.11739
median35.165235
Q335.205646
95-th percentile35.321631
Maximum35.385072
Range0.35259785
Interquartile range (IQR)0.088255723

Descriptive statistics

Standard deviation0.067133022
Coefficient of variation (CV)0.0019088015
Kurtosis0.37263055
Mean35.170248
Median Absolute Deviation (MAD)0.04380284
Skewness0.72437882
Sum351702.48
Variance0.0045068426
MonotonicityNot monotonic
2023-12-12T23:45:20.799920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.23991916 2
 
< 0.1%
35.1665757 2
 
< 0.1%
35.15062826 2
 
< 0.1%
35.18805942 1
 
< 0.1%
35.07539821 1
 
< 0.1%
35.15362965 1
 
< 0.1%
35.19238274 1
 
< 0.1%
35.36719345 1
 
< 0.1%
35.1564749 1
 
< 0.1%
35.21558604 1
 
< 0.1%
Other values (9987) 9987
99.9%
ValueCountFrequency (%)
35.03247429 1
< 0.1%
35.03280851 1
< 0.1%
35.04758502 1
< 0.1%
35.04775203 1
< 0.1%
35.04776258 1
< 0.1%
35.047766 1
< 0.1%
35.04795279 1
< 0.1%
35.04797308 1
< 0.1%
35.04802881 1
< 0.1%
35.04824949 1
< 0.1%
ValueCountFrequency (%)
35.38507214 1
< 0.1%
35.38498235 1
< 0.1%
35.38469638 1
< 0.1%
35.37706037 1
< 0.1%
35.3764432 1
< 0.1%
35.37640303 1
< 0.1%
35.37620704 1
< 0.1%
35.37597287 1
< 0.1%
35.37591879 1
< 0.1%
35.3758582 1
< 0.1%

Interactions

2023-12-12T23:45:16.466683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:45:15.669633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:45:16.062731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:45:16.594024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:45:15.800744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:45:16.197123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:45:16.728982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:45:15.935010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:45:16.328865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:45:20.888702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호시군구명리명경도위도
번호1.0000.7520.8020.6410.588
시군구명0.7521.0000.6520.8930.859
리명0.8020.6521.0000.9970.998
경도0.6410.8930.9971.0000.774
위도0.5880.8590.9980.7741.000
2023-12-12T23:45:20.980960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호경도위도시군구명
번호1.0000.1220.1490.415
경도0.1221.0000.5810.631
위도0.1490.5811.0000.563
시군구명0.4150.6310.5631.000

Missing values

2023-12-12T23:45:16.889618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:45:17.015427image/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.
2023-12-12T23:45:17.412833image/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
95489549기장군정관읍모전리<NA>정관신도시( 더샵5)129.16112735.337362
51705171강서구신호동<NA>르노삼성대로 61삼성자동차주차장128.87274435.089132
95819582영도구봉래동1가<NA><NA>부산대교위129.03923335.095195
72867287동래구안락동<NA><NA>안락주공205동129.10885735.194212
54615462금정구장전동<NA>수림로81번길 1대진정보통신고교129.08292135.23992
28572858서구남부민동<NA>충무대로 181대진타워129.02454535.086346
36913692부산진구개금동<NA>복지로21번길 6대성예식장(개금1파출소)129.02034835.150247
1075910760강서구화전동<NA><NA>(신호공단진입로입구)화전지구-추가2)128.86898435.101631
44444445영도구청학동<NA>청학북로 76-1신한기공사129.06371335.095578
1024810249강서구대저2동<NA><NA>용두마을 입구128.95380135.181847
번호시군구명동명리명도로명교차로명경도위도
1103011031강서구신호동<NA>신호산단4로 27신호지방산업단지(1)128.87617635.087355
94509451영도구청학동<NA><NA>롯데낙천대곡각지129.06265935.089083
61316132해운대구좌동<NA>대천로 56한일아파트(29)129.17002135.172038
1152711528북구화명동<NA><NA>화명주공아파트정문129.00801835.220825
1303713038해운대구우동<NA><NA>우1동 아림비치맨션2129.15742735.164978
77217722동래구안락동<NA>안락로125번가길 14-2충렬초교입구129.11069835.199127
1119211193영도구영선동1가<NA><NA>소방PB129.04389735.091575
112113북구금곡동<NA><NA>조달청부산지청129.01042635.252924
71427143해운대구좌동<NA>해운대로 875부일여객자동차㈜ 앞129.18393235.174565
1350713508북구화명동<NA>화명신도시로 127화명10지점129.00968735.2361