Overview

Dataset statistics

Number of variables12
Number of observations3815
Missing cells333
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory380.1 KiB
Average record size in memory102.0 B

Variable types

Numeric6
Categorical3
Text3

Dataset

Description부산광역시 횡단보도 위치 정보에 대한 데이터로 순번, 행정구역(구), 행정구역(동), 행정구역(리), 지번, 교차로, 신호등존재유무, 가로길이, 세로길이, 면적, 경도, 위도 항목정보를 제공합니다.
Author부산광역시
URLhttps://www.data.go.kr/data/3072152/fileData.do

Alerts

신호등존재유무 has constant value ""Constant
순번 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
가로길이 is highly overall correlated with 면적High correlation
세로길이 is highly overall correlated with 면적High correlation
면적 is highly overall correlated with 가로길이 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
행정구역(구) is highly overall correlated with 순번 and 2 other fieldsHigh correlation
행정구역(리) is highly overall correlated with 위도High correlation
행정구역(리) is highly imbalanced (84.9%)Imbalance
교차로 has 166 (4.4%) missing valuesMissing
가로길이 has 51 (1.3%) missing valuesMissing
세로길이 has 51 (1.3%) missing valuesMissing
면적 has 51 (1.3%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:36:14.332356
Analysis finished2023-12-12 22:36:20.025670
Duration5.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3815
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1908
Minimum1
Maximum3815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-13T07:36:20.100402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile191.7
Q1954.5
median1908
Q32861.5
95-th percentile3624.3
Maximum3815
Range3814
Interquartile range (IQR)1907

Descriptive statistics

Standard deviation1101.44
Coefficient of variation (CV)0.57727462
Kurtosis-1.2
Mean1908
Median Absolute Deviation (MAD)954
Skewness0
Sum7279020
Variance1213170
MonotonicityStrictly increasing
2023-12-13T07:36:20.272428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2549 1
 
< 0.1%
2537 1
 
< 0.1%
2538 1
 
< 0.1%
2539 1
 
< 0.1%
2540 1
 
< 0.1%
2541 1
 
< 0.1%
2542 1
 
< 0.1%
2543 1
 
< 0.1%
2544 1
 
< 0.1%
Other values (3805) 3805
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3815 1
< 0.1%
3814 1
< 0.1%
3813 1
< 0.1%
3812 1
< 0.1%
3811 1
< 0.1%
3810 1
< 0.1%
3809 1
< 0.1%
3808 1
< 0.1%
3807 1
< 0.1%
3806 1
< 0.1%

행정구역(구)
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
강서구
559 
해운대구
503 
기장군
331 
사하구
310 
부산진구
309 
Other values (11)
1803 

Length

Max length4
Median length3
Mean length3.0117955
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해운대구
2nd row해운대구
3rd row사하구
4th row해운대구
5th row기장군

Common Values

ValueCountFrequency (%)
강서구 559
14.7%
해운대구 503
13.2%
기장군 331
8.7%
사하구 310
8.1%
부산진구 309
8.1%
북구 259
 
6.8%
남구 237
 
6.2%
사상구 232
 
6.1%
동래구 224
 
5.9%
금정구 172
 
4.5%
Other values (6) 679
17.8%

Length

2023-12-13T07:36:20.442422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서구 559
14.7%
해운대구 503
13.2%
기장군 331
8.7%
사하구 310
8.1%
부산진구 309
8.1%
북구 259
 
6.8%
남구 237
 
6.2%
사상구 232
 
6.1%
동래구 224
 
5.9%
금정구 172
 
4.5%
Other values (6) 679
17.8%

행정구역(동)
Text

Distinct147
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
2023-12-13T07:36:20.801551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0516383
Min length2

Characters and Unicode

Total characters11642
Distinct characters115
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

Unique13 ?
Unique (%)0.3%

Sample

1st row중동
2nd row중동
3rd row신평동
4th row중동
5th row장안읍
ValueCountFrequency (%)
송정동 165
 
4.3%
정관읍 162
 
4.2%
우동 150
 
3.9%
연산동 105
 
2.8%
좌동 99
 
2.6%
명지동 90
 
2.4%
화명동 80
 
2.1%
기장읍 79
 
2.1%
반여동 76
 
2.0%
용호동 74
 
1.9%
Other values (137) 2735
71.7%
2023-12-13T07:36:21.311576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 3571
30.7%
정 407
 
3.5%
읍 307
 
2.6%
장 278
 
2.4%
대 278
 
2.4%
송 253
 
2.2%
명 227
 
1.9%
가 215
 
1.8%
천 210
 
1.8%
전 205
 
1.8%
Other values (105) 5691
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11362
97.6%
Decimal Number 280
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3571
31.4%
정 407
 
3.6%
읍 307
 
2.7%
장 278
 
2.4%
대 278
 
2.4%
송 253
 
2.2%
명 227
 
2.0%
가 215
 
1.9%
천 210
 
1.8%
전 205
 
1.8%
Other values (99) 5411
47.6%
Decimal Number
ValueCountFrequency (%)
2 114
40.7%
1 79
28.2%
3 58
20.7%
4 18
 
6.4%
5 7
 
2.5%
6 4
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11362
97.6%
Common 280
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3571
31.4%
정 407
 
3.6%
읍 307
 
2.7%
장 278
 
2.4%
대 278
 
2.4%
송 253
 
2.2%
명 227
 
2.0%
가 215
 
1.9%
천 210
 
1.8%
전 205
 
1.8%
Other values (99) 5411
47.6%
Common
ValueCountFrequency (%)
2 114
40.7%
1 79
28.2%
3 58
20.7%
4 18
 
6.4%
5 7
 
2.5%
6 4
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11362
97.6%
ASCII 280
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 3571
31.4%
정 407
 
3.6%
읍 307
 
2.7%
장 278
 
2.4%
대 278
 
2.4%
송 253
 
2.2%
명 227
 
2.0%
가 215
 
1.9%
천 210
 
1.8%
전 205
 
1.8%
Other values (99) 5411
47.6%
ASCII
ValueCountFrequency (%)
2 114
40.7%
1 79
28.2%
3 58
20.7%
4 18
 
6.4%
5 7
 
2.5%
6 4
 
1.4%

행정구역(리)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct42
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
-
3488 
용수리
 
38
달산리
 
38
매학리
 
24
예림리
 
23
Other values (37)
 
204

Length

Max length3
Median length1
Mean length1.1677588
Min length1

Unique

Unique11 ?
Unique (%)0.3%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 3488
91.4%
용수리 38
 
1.0%
달산리 38
 
1.0%
매학리 24
 
0.6%
예림리 23
 
0.6%
반룡리 21
 
0.6%
모전리 20
 
0.5%
대라리 16
 
0.4%
청강리 16
 
0.4%
동부리 13
 
0.3%
Other values (32) 118
 
3.1%

Length

2023-12-13T07:36:21.483102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3488
91.4%
용수리 38
 
1.0%
달산리 38
 
1.0%
매학리 24
 
0.6%
예림리 23
 
0.6%
반룡리 21
 
0.6%
모전리 20
 
0.5%
대라리 16
 
0.4%
청강리 16
 
0.4%
동부리 13
 
0.3%
Other values (32) 118
 
3.1%

지번
Text

Distinct2278
Distinct (%)59.8%
Missing8
Missing (%)0.2%
Memory size29.9 KiB
2023-12-13T07:36:21.901263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.0488574
Min length1

Characters and Unicode

Total characters19221
Distinct characters37
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

Unique1579 ?
Unique (%)41.5%

Sample

1st row1372-16
2nd row1372-16
3rd row452-5
4th row1372-16
5th row산1-1
ValueCountFrequency (%)
185 26
 
0.7%
02월 23
 
0.6%
01월 21
 
0.5%
1318 19
 
0.5%
960 19
 
0.5%
462 19
 
0.5%
174 18
 
0.5%
1445 18
 
0.5%
1402 16
 
0.4%
575 15
 
0.4%
Other values (2246) 3712
95.0%
2023-12-13T07:36:22.510077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3311
17.2%
- 2545
13.2%
2 1878
9.8%
3 1712
8.9%
4 1465
7.6%
5 1399
7.3%
6 1317
 
6.9%
7 1306
 
6.8%
8 1174
 
6.1%
0 1140
 
5.9%
Other values (27) 1974
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15716
81.8%
Dash Punctuation 2545
 
13.2%
Lowercase Letter 402
 
2.1%
Other Letter 258
 
1.3%
Uppercase Letter 201
 
1.0%
Space Separator 99
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 87
21.6%
n 76
18.9%
e 48
11.9%
u 40
10.0%
b 38
9.5%
r 26
 
6.5%
y 21
 
5.2%
p 16
 
4.0%
c 12
 
3.0%
g 10
 
2.5%
Other values (4) 28
 
7.0%
Decimal Number
ValueCountFrequency (%)
1 3311
21.1%
2 1878
11.9%
3 1712
10.9%
4 1465
9.3%
5 1399
8.9%
6 1317
 
8.4%
7 1306
 
8.3%
8 1174
 
7.5%
0 1140
 
7.3%
9 1014
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
J 80
39.8%
F 38
18.9%
M 37
18.4%
A 20
 
10.0%
O 8
 
4.0%
N 8
 
4.0%
S 6
 
3.0%
D 4
 
2.0%
Other Letter
ValueCountFrequency (%)
일 95
36.8%
월 95
36.8%
산 68
26.4%
Dash Punctuation
ValueCountFrequency (%)
- 2545
100.0%
Space Separator
ValueCountFrequency (%)
99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18360
95.5%
Latin 603
 
3.1%
Hangul 258
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 87
14.4%
J 80
13.3%
n 76
12.6%
e 48
8.0%
u 40
 
6.6%
b 38
 
6.3%
F 38
 
6.3%
M 37
 
6.1%
r 26
 
4.3%
y 21
 
3.5%
Other values (12) 112
18.6%
Common
ValueCountFrequency (%)
1 3311
18.0%
- 2545
13.9%
2 1878
10.2%
3 1712
9.3%
4 1465
8.0%
5 1399
7.6%
6 1317
 
7.2%
7 1306
 
7.1%
8 1174
 
6.4%
0 1140
 
6.2%
Other values (2) 1113
 
6.1%
Hangul
ValueCountFrequency (%)
일 95
36.8%
월 95
36.8%
산 68
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18963
98.7%
Hangul 258
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3311
17.5%
- 2545
13.4%
2 1878
9.9%
3 1712
9.0%
4 1465
7.7%
5 1399
7.4%
6 1317
 
6.9%
7 1306
 
6.9%
8 1174
 
6.2%
0 1140
 
6.0%
Other values (24) 1716
9.0%
Hangul
ValueCountFrequency (%)
일 95
36.8%
월 95
36.8%
산 68
26.4%

교차로
Text

MISSING 

Distinct1726
Distinct (%)47.3%
Missing166
Missing (%)4.4%
Memory size29.9 KiB
2023-12-13T07:36:22.767447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length7.620444
Min length3

Characters and Unicode

Total characters27807
Distinct characters484
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

Unique757 ?
Unique (%)20.7%

Sample

1st row동성장여관
2nd row동성장여관
3rd row신익강변타운
4th row동성장여관
5th row엘시티앞
ValueCountFrequency (%)
화전지구산업단지 55
 
1.3%
앞 48
 
1.1%
명지주거단지 46
 
1.1%
서부산유통단지 26
 
0.6%
사거리 20
 
0.5%
화명동 16
 
0.4%
마린시티 16
 
0.4%
금정산lh뉴웰시티아파트 15
 
0.3%
장안일반산업단지 15
 
0.3%
주변 14
 
0.3%
Other values (1835) 4019
93.7%
2023-12-13T07:36:23.119904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 925
 
3.3%
( 773
 
2.8%
) 773
 
2.8%
604
 
2.2%
동 603
 
2.2%
리 599
 
2.2%
교 597
 
2.1%
구 582
 
2.1%
산 580
 
2.1%
대 473
 
1.7%
Other values (474) 21298
76.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23108
83.1%
Decimal Number 1806
 
6.5%
Open Punctuation 773
 
2.8%
Close Punctuation 773
 
2.8%
Space Separator 604
 
2.2%
Uppercase Letter 400
 
1.4%
Other Punctuation 176
 
0.6%
Dash Punctuation 105
 
0.4%
Control 39
 
0.1%
Other Symbol 12
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 925
 
4.0%
동 603
 
2.6%
리 599
 
2.6%
교 597
 
2.6%
구 582
 
2.5%
산 580
 
2.5%
대 473
 
2.0%
점 410
 
1.8%
트 403
 
1.7%
사 390
 
1.7%
Other values (422) 17546
75.9%
Uppercase Letter
ValueCountFrequency (%)
P 72
18.0%
B 52
13.0%
L 45
11.2%
G 35
8.8%
E 29
7.2%
C 28
 
7.0%
S 21
 
5.2%
I 21
 
5.2%
A 21
 
5.2%
H 19
 
4.8%
Other values (13) 57
14.2%
Decimal Number
ValueCountFrequency (%)
1 456
25.2%
2 414
22.9%
3 213
11.8%
4 191
10.6%
0 102
 
5.6%
6 101
 
5.6%
5 97
 
5.4%
8 89
 
4.9%
7 79
 
4.4%
9 64
 
3.5%
Other Punctuation
ValueCountFrequency (%)
# 88
50.0%
, 56
31.8%
: 12
 
6.8%
. 10
 
5.7%
" 6
 
3.4%
/ 3
 
1.7%
@ 1
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
r 1
25.0%
a 1
25.0%
e 1
25.0%
k 1
25.0%
Math Symbol
ValueCountFrequency (%)
~ 5
71.4%
→ 2
 
28.6%
Open Punctuation
ValueCountFrequency (%)
( 773
100.0%
Close Punctuation
ValueCountFrequency (%)
) 773
100.0%
Space Separator
ValueCountFrequency (%)
604
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 105
100.0%
Control
ValueCountFrequency (%)
39
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23120
83.1%
Common 4283
 
15.4%
Latin 404
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 925
 
4.0%
동 603
 
2.6%
리 599
 
2.6%
교 597
 
2.6%
구 582
 
2.5%
산 580
 
2.5%
대 473
 
2.0%
점 410
 
1.8%
트 403
 
1.7%
사 390
 
1.7%
Other values (423) 17558
75.9%
Latin
ValueCountFrequency (%)
P 72
17.8%
B 52
12.9%
L 45
11.1%
G 35
8.7%
E 29
7.2%
C 28
 
6.9%
S 21
 
5.2%
I 21
 
5.2%
A 21
 
5.2%
H 19
 
4.7%
Other values (17) 61
15.1%
Common
ValueCountFrequency (%)
( 773
18.0%
) 773
18.0%
604
14.1%
1 456
10.6%
2 414
9.7%
3 213
 
5.0%
4 191
 
4.5%
- 105
 
2.5%
0 102
 
2.4%
6 101
 
2.4%
Other values (14) 551
12.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23108
83.1%
ASCII 4685
 
16.8%
None 12
 
< 0.1%
Arrows 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 925
 
4.0%
동 603
 
2.6%
리 599
 
2.6%
교 597
 
2.6%
구 582
 
2.5%
산 580
 
2.5%
대 473
 
2.0%
점 410
 
1.8%
트 403
 
1.7%
사 390
 
1.7%
Other values (422) 17546
75.9%
ASCII
ValueCountFrequency (%)
( 773
16.5%
) 773
16.5%
604
12.9%
1 456
9.7%
2 414
8.8%
3 213
 
4.5%
4 191
 
4.1%
- 105
 
2.2%
0 102
 
2.2%
6 101
 
2.2%
Other values (40) 953
20.3%
None
ValueCountFrequency (%)
㈜ 12
100.0%
Arrows
ValueCountFrequency (%)
→ 2
100.0%

신호등존재유무
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
유
3815 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유
2nd row유
3rd row유
4th row유
5th row유

Common Values

ValueCountFrequency (%)
유 3815
100.0%

Length

2023-12-13T07:36:23.240908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:36:23.325962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유 3815
100.0%

가로길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)0.9%
Missing51
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean7.3583953
Minimum0
Maximum42
Zeros13
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-13T07:36:23.422227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median7
Q38
95-th percentile12
Maximum42
Range42
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8468539
Coefficient of variation (CV)0.38688516
Kurtosis18.642051
Mean7.3583953
Median Absolute Deviation (MAD)1
Skewness2.9598231
Sum27697
Variance8.1045774
MonotonicityNot monotonic
2023-12-13T07:36:23.524386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6 1177
30.9%
8 711
18.6%
7 590
15.5%
9 372
 
9.8%
4 311
 
8.2%
5 144
 
3.8%
10 108
 
2.8%
11 74
 
1.9%
12 54
 
1.4%
13 40
 
1.0%
Other values (22) 183
 
4.8%
(Missing) 51
 
1.3%
ValueCountFrequency (%)
0 13
 
0.3%
2 6
 
0.2%
3 33
 
0.9%
4 311
 
8.2%
5 144
 
3.8%
6 1177
30.9%
7 590
15.5%
8 711
18.6%
9 372
 
9.8%
10 108
 
2.8%
ValueCountFrequency (%)
42 1
< 0.1%
39 1
< 0.1%
30 1
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
27 1
< 0.1%
26 1
< 0.1%
25 2
0.1%
24 1
< 0.1%
23 1
< 0.1%

세로길이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)1.3%
Missing51
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean17.113709
Minimum0
Maximum59
Zeros13
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-13T07:36:23.647148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median16
Q322
95-th percentile31
Maximum59
Range59
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.6942872
Coefficient of variation (CV)0.44959788
Kurtosis1.1877696
Mean17.113709
Median Absolute Deviation (MAD)5
Skewness0.93682312
Sum64416
Variance59.202055
MonotonicityNot monotonic
2023-12-13T07:36:23.779247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 314
 
8.2%
14 242
 
6.3%
12 224
 
5.9%
15 215
 
5.6%
19 192
 
5.0%
17 187
 
4.9%
16 184
 
4.8%
8 174
 
4.6%
9 164
 
4.3%
10 155
 
4.1%
Other values (40) 1713
44.9%
ValueCountFrequency (%)
0 13
 
0.3%
3 2
 
0.1%
4 20
 
0.5%
5 20
 
0.5%
6 64
 
1.7%
7 118
3.1%
8 174
4.6%
9 164
4.3%
10 155
4.1%
11 134
3.5%
ValueCountFrequency (%)
59 1
 
< 0.1%
52 1
 
< 0.1%
51 2
 
0.1%
50 1
 
< 0.1%
48 1
 
< 0.1%
46 4
0.1%
45 5
0.1%
44 3
0.1%
43 4
0.1%
42 4
0.1%

면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct344
Distinct (%)9.1%
Missing51
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean119.04224
Minimum0
Maximum531
Zeros13
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-13T07:36:23.905322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q173
median103
Q3154
95-th percentile244.85
Maximum531
Range531
Interquartile range (IQR)81

Descriptive statistics

Standard deviation67.85794
Coefficient of variation (CV)0.57003244
Kurtosis2.2973529
Mean119.04224
Median Absolute Deviation (MAD)38
Skewness1.2585002
Sum448075
Variance4604.7
MonotonicityNot monotonic
2023-12-13T07:36:24.040565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83 42
 
1.1%
82 39
 
1.0%
81 39
 
1.0%
90 39
 
1.0%
77 38
 
1.0%
106 38
 
1.0%
73 38
 
1.0%
89 38
 
1.0%
101 37
 
1.0%
92 37
 
1.0%
Other values (334) 3379
88.6%
(Missing) 51
 
1.3%
ValueCountFrequency (%)
0 13
0.3%
7 1
 
< 0.1%
9 2
 
0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 3
 
0.1%
13 2
 
0.1%
15 1
 
< 0.1%
16 9
0.2%
17 1
 
< 0.1%
ValueCountFrequency (%)
531 1
< 0.1%
445 1
< 0.1%
437 1
< 0.1%
434 1
< 0.1%
430 1
< 0.1%
428 1
< 0.1%
422 1
< 0.1%
417 1
< 0.1%
415 1
< 0.1%
408 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct3810
Distinct (%)99.9%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean129.04936
Minimum128.80934
Maximum129.30292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-13T07:36:24.426281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.80934
5-th percentile128.86547
Q1128.98803
median129.06189
Q3129.11279
95-th percentile129.19172
Maximum129.30292
Range0.4935816
Interquartile range (IQR)0.12476665

Descriptive statistics

Standard deviation0.095224088
Coefficient of variation (CV)0.00073788891
Kurtosis-0.22122027
Mean129.04936
Median Absolute Deviation (MAD)0.0591586
Skewness-0.30698428
Sum491936.14
Variance0.0090676269
MonotonicityNot monotonic
2023-12-13T07:36:24.551718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0760783 2
 
0.1%
129.0843612 2
 
0.1%
129.112007 1
 
< 0.1%
129.087249 1
 
< 0.1%
129.0875657 1
 
< 0.1%
129.0885565 1
 
< 0.1%
129.0884449 1
 
< 0.1%
129.072461 1
 
< 0.1%
129.072267 1
 
< 0.1%
129.085728 1
 
< 0.1%
Other values (3800) 3800
99.6%
(Missing) 3
 
0.1%
ValueCountFrequency (%)
128.8093358 1
< 0.1%
128.8111582 1
< 0.1%
128.8117566 1
< 0.1%
128.8193325 1
< 0.1%
128.8194917 1
< 0.1%
128.8221348 1
< 0.1%
128.822189 1
< 0.1%
128.8231386 1
< 0.1%
128.8235537 1
< 0.1%
128.8235588 1
< 0.1%
ValueCountFrequency (%)
129.3029174 1
< 0.1%
129.3028907 1
< 0.1%
129.3027384 1
< 0.1%
129.3027295 1
< 0.1%
129.283008 1
< 0.1%
129.2796486 1
< 0.1%
129.2753376 1
< 0.1%
129.27517 1
< 0.1%
129.2669227 1
< 0.1%
129.2666032 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct3812
Distinct (%)100.0%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean35.167406
Minimum35.032821
Maximum35.38506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2023-12-13T07:36:24.686794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.032821
5-th percentile35.082411
Q135.113189
median35.16433
Q335.201531
95-th percentile35.318769
Maximum35.38506
Range0.35223878
Interquartile range (IQR)0.088341385

Descriptive statistics

Standard deviation0.064183019
Coefficient of variation (CV)0.0018250712
Kurtosis0.35579265
Mean35.167406
Median Absolute Deviation (MAD)0.041650125
Skewness0.71187624
Sum134058.15
Variance0.00411946
MonotonicityNot monotonic
2023-12-13T07:36:24.823826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.18580057 1
 
< 0.1%
35.18267824 1
 
< 0.1%
35.18905591 1
 
< 0.1%
35.18930465 1
 
< 0.1%
35.18974972 1
 
< 0.1%
35.18961857 1
 
< 0.1%
35.18075201 1
 
< 0.1%
35.18072506 1
 
< 0.1%
35.17633691 1
 
< 0.1%
35.17403276 1
 
< 0.1%
Other values (3802) 3802
99.7%
(Missing) 3
 
0.1%
ValueCountFrequency (%)
35.03282134 1
< 0.1%
35.04764279 1
< 0.1%
35.04779533 1
< 0.1%
35.0479076 1
< 0.1%
35.04837316 1
< 0.1%
35.04850912 1
< 0.1%
35.04940131 1
< 0.1%
35.04940311 1
< 0.1%
35.04999669 1
< 0.1%
35.05004103 1
< 0.1%
ValueCountFrequency (%)
35.38506012 1
< 0.1%
35.37055211 1
< 0.1%
35.35798761 1
< 0.1%
35.35796302 1
< 0.1%
35.35782682 1
< 0.1%
35.35774267 1
< 0.1%
35.35762309 1
< 0.1%
35.35535775 1
< 0.1%
35.35525464 1
< 0.1%
35.35507342 1
< 0.1%

Interactions

2023-12-13T07:36:18.837580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:15.339531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:15.962741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.600606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:17.454530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:18.153014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:18.965514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:15.445701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.064833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.713644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:17.588226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:18.257501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:19.066896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:15.540096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.162577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.821486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:17.696145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:18.358464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:19.173789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:15.650443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.282199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.921447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:17.830754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:18.475271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:19.290201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:15.749427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.370160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:17.006375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:17.938161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:18.596179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:19.431710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:15.861676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:16.476563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:17.338228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:18.051435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:36:18.708484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:36:24.911445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번행정구역(구)행정구역(리)가로길이세로길이면적경도위도
순번1.0000.9650.5860.1180.2700.2450.8940.845
행정구역(구)0.9651.0000.6490.1460.2520.2450.8960.862
행정구역(리)0.5860.6491.0000.4130.0860.1620.8490.861
가로길이0.1180.1460.4131.0000.3680.6410.1840.107
세로길이0.2700.2520.0860.3681.0000.8750.2720.221
면적0.2450.2450.1620.6410.8751.0000.3100.173
경도0.8940.8960.8490.1840.2720.3101.0000.766
위도0.8450.8620.8610.1070.2210.1730.7661.000
2023-12-13T07:36:25.005551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역(리)행정구역(구)
행정구역(리)1.0000.233
행정구역(구)0.2331.000
2023-12-13T07:36:25.081342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번가로길이세로길이면적경도위도행정구역(구)행정구역(리)
순번1.0000.0350.1240.129-0.539-0.3420.8400.243
가로길이0.0351.0000.3380.644-0.0530.0160.0580.148
세로길이0.1240.3381.0000.877-0.034-0.0480.1010.030
면적0.1290.6440.8771.000-0.075-0.0600.0980.056
경도-0.539-0.053-0.034-0.0751.0000.5600.6370.490
위도-0.3420.016-0.048-0.0600.5601.0000.5690.511
행정구역(구)0.8400.0580.1010.0980.6370.5691.0000.233
행정구역(리)0.2430.1480.0300.0560.4900.5110.2331.000

Missing values

2023-12-13T07:36:19.611223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:36:19.802778image/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.
2023-12-13T07:36:19.944370image/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

순번행정구역(구)행정구역(동)행정구역(리)지번교차로신호등존재유무가로길이세로길이면적경도위도
01해운대구중동-1372-16동성장여관유623139129.16536835.162321
12해운대구중동-1372-16동성장여관유1116110129.16506535.16213
23사하구신평동-452-5신익강변타운유820162128.96230635.094678
34해운대구중동-1372-16동성장여관유830202129.16534435.16214
45기장군장안읍-산1-1<NA>유41248129.30289135.340494
56기장군장안읍-산1-1<NA>유61377129.30291735.340645
67기장군장안읍-산1-1<NA>유61485129.30273835.340654
78기장군철마면-1008-52<NA>유817142129.12323235.335774
89강서구강동동-661-1<NA>유836283128.92851235.221076
910기장군철마면-402-1<NA>유61488129.1096735.309636
순번행정구역(구)행정구역(동)행정구역(리)지번교차로신호등존재유무가로길이세로길이면적경도위도
38053806중구중앙동4가-Jul-88세관로타리유820146129.03747435.105052
38063807중구중앙동4가-Jul-88세관로타리유1210114129.03777435.104999
38073808중구중앙동4가-May-87세관로타리유1034342129.03767235.105262
38083809중구중앙동5가-17-25연안부두유7963129.0374435.102957
38093810중구중앙동5가-Jan-92연안부두유924196129.03762935.102876
38103811중구대청동2가-Jan-93미문화원유816127129.03154835.10287
38113812중구동광동3가-51미문화원유61699129.03275335.102837
38123813중구중앙동3가-49-14부산우체국유61488129.03437235.102819
38133814중구중앙동3가-20부산우체국유715113129.03546435.102886
38143815중구중앙동6가-45광복동입구유735218129.03565335.098