Overview

Dataset statistics

Number of variables9
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory82.1 B

Variable types

Text2
Numeric7

Dataset

Description부산광역시중구_가로등현황_20230825
Author부산광역시 중구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15020721

Alerts

가로등_합계 is highly overall correlated with 가로등_고효율_소계 and 1 other fieldsHigh correlation
가로등_고효율_소계 is highly overall correlated with 가로등_합계 and 1 other fieldsHigh correlation
총등주(본) is highly overall correlated with 가로등_합계 and 1 other fieldsHigh correlation
위치 has unique valuesUnique
가로등_일반 has 26 (60.5%) zerosZeros
가로등_고효율_소계 has 3 (7.0%) zerosZeros
가로등_고효율_LED has 19 (44.2%) zerosZeros
가로등_고효율_기타 has 20 (46.5%) zerosZeros
총등주(본) has 11 (25.6%) zerosZeros
등주(FRP)(본) has 38 (88.4%) zerosZeros

Reproduction

Analysis started2023-12-10 16:38:46.908752
Analysis finished2023-12-10 16:38:52.392738
Duration5.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로명
Text

Distinct38
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-11T01:38:52.570155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length14.465116
Min length12

Characters and Unicode

Total characters622
Distinct characters71
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

Unique34 ?
Unique (%)79.1%

Sample

1st row부산광역시 중구 충장대로
2nd row부산광역시 중구 대영로
3rd row부산광역시 중구 구덕로
4th row부산광역시 중구 중앙대로
5th row부산광역시 중구 대청로
ValueCountFrequency (%)
부산광역시 43
30.7%
중구 43
30.7%
이면도로 4
 
2.9%
대교로 3
 
2.1%
광복로 2
 
1.4%
중앙대로 2
 
1.4%
태종로 2
 
1.4%
중구로 2
 
1.4%
망양로 2
 
1.4%
복병산길 1
 
0.7%
Other values (36) 36
25.7%
2023-12-11T01:38:52.998396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97
15.6%
중 50
 
8.0%
산 48
 
7.7%
광 48
 
7.7%
구 47
 
7.6%
부 46
 
7.4%
시 43
 
6.9%
역 43
 
6.9%
로 36
 
5.8%
길 12
 
1.9%
Other values (61) 152
24.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 500
80.4%
Space Separator 97
 
15.6%
Decimal Number 20
 
3.2%
Other Punctuation 5
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 50
10.0%
산 48
9.6%
광 48
9.6%
구 47
9.4%
부 46
 
9.2%
시 43
 
8.6%
역 43
 
8.6%
로 36
 
7.2%
길 12
 
2.4%
대 11
 
2.2%
Other values (50) 116
23.2%
Decimal Number
ValueCountFrequency (%)
5 4
20.0%
3 4
20.0%
1 3
15.0%
8 2
10.0%
4 2
10.0%
2 2
10.0%
0 1
 
5.0%
7 1
 
5.0%
9 1
 
5.0%
Space Separator
ValueCountFrequency (%)
97
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 500
80.4%
Common 122
 
19.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 50
10.0%
산 48
9.6%
광 48
9.6%
구 47
9.4%
부 46
 
9.2%
시 43
 
8.6%
역 43
 
8.6%
로 36
 
7.2%
길 12
 
2.4%
대 11
 
2.2%
Other values (50) 116
23.2%
Common
ValueCountFrequency (%)
97
79.5%
, 5
 
4.1%
5 4
 
3.3%
3 4
 
3.3%
1 3
 
2.5%
8 2
 
1.6%
4 2
 
1.6%
2 2
 
1.6%
0 1
 
0.8%
7 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 500
80.4%
ASCII 122
 
19.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
97
79.5%
, 5
 
4.1%
5 4
 
3.3%
3 4
 
3.3%
1 3
 
2.5%
8 2
 
1.6%
4 2
 
1.6%
2 2
 
1.6%
0 1
 
0.8%
7 1
 
0.8%
Hangul
ValueCountFrequency (%)
중 50
10.0%
산 48
9.6%
광 48
9.6%
구 47
9.4%
부 46
 
9.2%
시 43
 
8.6%
역 43
 
8.6%
로 36
 
7.2%
길 12
 
2.4%
대 11
 
2.2%
Other values (50) 116
23.2%

위치
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-11T01:38:53.228772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length15
Mean length11.953488
Min length5

Characters and Unicode

Total characters514
Distinct characters148
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row무역회관앞~동구경계
2nd rowDSEC빌딩~부산터널
3rd row옛시청교차로~자갈치공영주차장
4th row옛시청교차로~영주동조흥은행
5th row부산우체국~부민사거리
ValueCountFrequency (%)
흑교로 2
 
3.7%
무역회관앞~동구경계 1
 
1.9%
한국선원센터~세관삼거리 1
 
1.9%
망양로319번길 1
 
1.9%
383번안길 1
 
1.9%
복병산체육공원 1
 
1.9%
배수지 1
 
1.9%
체육공원 1
 
1.9%
내 1
 
1.9%
중구관리 1
 
1.9%
Other values (43) 43
79.6%
2023-12-11T01:38:53.695241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 25
 
4.9%
부 18
 
3.5%
교 17
 
3.3%
리 17
 
3.3%
거 15
 
2.9%
산 14
 
2.7%
로 13
 
2.5%
, 12
 
2.3%
11
 
2.1%
사 10
 
1.9%
Other values (138) 362
70.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 410
79.8%
Decimal Number 45
 
8.8%
Math Symbol 25
 
4.9%
Other Punctuation 12
 
2.3%
Space Separator 11
 
2.1%
Uppercase Letter 6
 
1.2%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 18
 
4.4%
교 17
 
4.1%
리 17
 
4.1%
거 15
 
3.7%
산 14
 
3.4%
로 13
 
3.2%
사 10
 
2.4%
영 10
 
2.4%
시 9
 
2.2%
주 8
 
2.0%
Other values (116) 279
68.0%
Decimal Number
ValueCountFrequency (%)
1 9
20.0%
7 7
15.6%
5 6
13.3%
2 5
11.1%
3 5
11.1%
8 3
 
6.7%
9 3
 
6.7%
4 3
 
6.7%
0 2
 
4.4%
6 2
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
T 1
16.7%
K 1
16.7%
D 1
16.7%
S 1
16.7%
E 1
16.7%
C 1
16.7%
Math Symbol
ValueCountFrequency (%)
~ 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 410
79.8%
Common 98
 
19.1%
Latin 6
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 18
 
4.4%
교 17
 
4.1%
리 17
 
4.1%
거 15
 
3.7%
산 14
 
3.4%
로 13
 
3.2%
사 10
 
2.4%
영 10
 
2.4%
시 9
 
2.2%
주 8
 
2.0%
Other values (116) 279
68.0%
Common
ValueCountFrequency (%)
~ 25
25.5%
, 12
12.2%
11
11.2%
1 9
 
9.2%
7 7
 
7.1%
5 6
 
6.1%
2 5
 
5.1%
3 5
 
5.1%
8 3
 
3.1%
9 3
 
3.1%
Other values (6) 12
12.2%
Latin
ValueCountFrequency (%)
T 1
16.7%
K 1
16.7%
D 1
16.7%
S 1
16.7%
E 1
16.7%
C 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 410
79.8%
ASCII 104
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 25
24.0%
, 12
11.5%
11
10.6%
1 9
 
8.7%
7 7
 
6.7%
5 6
 
5.8%
2 5
 
4.8%
3 5
 
4.8%
8 3
 
2.9%
9 3
 
2.9%
Other values (12) 18
17.3%
Hangul
ValueCountFrequency (%)
부 18
 
4.4%
교 17
 
4.1%
리 17
 
4.1%
거 15
 
3.7%
산 14
 
3.4%
로 13
 
3.2%
사 10
 
2.4%
영 10
 
2.4%
시 9
 
2.2%
주 8
 
2.0%
Other values (116) 279
68.0%

가로등_합계
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.325581
Minimum2
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T01:38:53.876736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.1
Q118
median34
Q358
95-th percentile109.9
Maximum126
Range124
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.243489
Coefficient of variation (CV)0.80442883
Kurtosis0.2041582
Mean41.325581
Median Absolute Deviation (MAD)20
Skewness0.93792545
Sum1777
Variance1105.1296
MonotonicityNot monotonic
2023-12-11T01:38:54.047248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4 3
 
7.0%
58 2
 
4.7%
44 2
 
4.7%
3 2
 
4.7%
51 2
 
4.7%
25 2
 
4.7%
69 2
 
4.7%
22 2
 
4.7%
18 2
 
4.7%
16 1
 
2.3%
Other values (23) 23
53.5%
ValueCountFrequency (%)
2 1
 
2.3%
3 2
4.7%
4 3
7.0%
5 1
 
2.3%
9 1
 
2.3%
14 1
 
2.3%
16 1
 
2.3%
18 2
4.7%
19 1
 
2.3%
20 1
 
2.3%
ValueCountFrequency (%)
126 1
2.3%
118 1
2.3%
111 1
2.3%
100 1
2.3%
94 1
2.3%
82 1
2.3%
74 1
2.3%
69 2
4.7%
61 1
2.3%
58 2
4.7%

가로등_일반
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.372093
Minimum0
Maximum125
Zeros26
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T01:38:54.188511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.5
95-th percentile46.8
Maximum125
Range125
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation26.283277
Coefficient of variation (CV)2.5340379
Kurtosis13.011857
Mean10.372093
Median Absolute Deviation (MAD)0
Skewness3.5748504
Sum446
Variance690.81063
MonotonicityNot monotonic
2023-12-11T01:38:54.307539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 26
60.5%
5 3
 
7.0%
2 2
 
4.7%
9 2
 
4.7%
19 1
 
2.3%
14 1
 
2.3%
112 1
 
2.3%
125 1
 
2.3%
36 1
 
2.3%
6 1
 
2.3%
Other values (4) 4
 
9.3%
ValueCountFrequency (%)
0 26
60.5%
2 2
 
4.7%
4 1
 
2.3%
5 3
 
7.0%
6 1
 
2.3%
9 2
 
4.7%
14 1
 
2.3%
19 1
 
2.3%
21 1
 
2.3%
24 1
 
2.3%
ValueCountFrequency (%)
125 1
2.3%
112 1
2.3%
48 1
2.3%
36 1
2.3%
24 1
2.3%
21 1
2.3%
19 1
2.3%
14 1
2.3%
9 2
4.7%
6 1
2.3%

가로등_고효율_소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.953488
Minimum0
Maximum111
Zeros3
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T01:38:54.456043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q18.5
median25
Q345
95-th percentile91.5
Maximum111
Range111
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation27.839637
Coefficient of variation (CV)0.89940226
Kurtosis1.0355504
Mean30.953488
Median Absolute Deviation (MAD)19
Skewness1.1295561
Sum1331
Variance775.0454
MonotonicityNot monotonic
2023-12-11T01:38:54.596015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 3
 
7.0%
20 2
 
4.7%
25 2
 
4.7%
51 2
 
4.7%
1 2
 
4.7%
44 2
 
4.7%
4 2
 
4.7%
39 1
 
2.3%
48 1
 
2.3%
34 1
 
2.3%
Other values (25) 25
58.1%
ValueCountFrequency (%)
0 3
7.0%
1 2
4.7%
2 1
 
2.3%
3 1
 
2.3%
4 2
4.7%
6 1
 
2.3%
8 1
 
2.3%
9 1
 
2.3%
10 1
 
2.3%
14 1
 
2.3%
ValueCountFrequency (%)
111 1
2.3%
100 1
2.3%
94 1
2.3%
69 1
2.3%
67 1
2.3%
60 1
2.3%
58 1
2.3%
51 2
4.7%
48 1
2.3%
46 1
2.3%

가로등_고효율_LED
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.395349
Minimum0
Maximum100
Zeros19
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T01:38:54.734924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q325.5
95-th percentile67.4
Maximum100
Range100
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation24.163274
Coefficient of variation (CV)1.4737883
Kurtosis2.5343901
Mean16.395349
Median Absolute Deviation (MAD)2
Skewness1.6767392
Sum705
Variance583.86379
MonotonicityNot monotonic
2023-12-11T01:38:54.918765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 19
44.2%
4 2
 
4.7%
1 2
 
4.7%
40 1
 
2.3%
25 1
 
2.3%
51 1
 
2.3%
3 1
 
2.3%
2 1
 
2.3%
17 1
 
2.3%
10 1
 
2.3%
Other values (13) 13
30.2%
ValueCountFrequency (%)
0 19
44.2%
1 2
 
4.7%
2 1
 
2.3%
3 1
 
2.3%
4 2
 
4.7%
10 1
 
2.3%
14 1
 
2.3%
16 1
 
2.3%
17 1
 
2.3%
18 1
 
2.3%
ValueCountFrequency (%)
100 1
2.3%
70 1
2.3%
69 1
2.3%
53 1
2.3%
51 1
2.3%
46 1
2.3%
44 1
2.3%
40 1
2.3%
37 1
2.3%
30 1
2.3%

가로등_고효율_기타
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.55814
Minimum0
Maximum111
Zeros20
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T01:38:55.098807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q321.5
95-th percentile49.9
Maximum111
Range111
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation21.767497
Coefficient of variation (CV)1.4952114
Kurtosis8.2687112
Mean14.55814
Median Absolute Deviation (MAD)1
Skewness2.4498526
Sum626
Variance473.82392
MonotonicityNot monotonic
2023-12-11T01:38:55.292154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 20
46.5%
20 4
 
9.3%
1 2
 
4.7%
21 2
 
4.7%
39 1
 
2.3%
26 1
 
2.3%
58 1
 
2.3%
51 1
 
2.3%
25 1
 
2.3%
33 1
 
2.3%
Other values (9) 9
20.9%
ValueCountFrequency (%)
0 20
46.5%
1 2
 
4.7%
6 1
 
2.3%
8 1
 
2.3%
9 1
 
2.3%
14 1
 
2.3%
20 4
 
9.3%
21 2
 
4.7%
22 1
 
2.3%
24 1
 
2.3%
ValueCountFrequency (%)
111 1
2.3%
58 1
2.3%
51 1
2.3%
40 1
2.3%
39 1
2.3%
36 1
2.3%
33 1
2.3%
26 1
2.3%
25 1
2.3%
24 1
2.3%

총등주(본)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.069767
Minimum0
Maximum118
Zeros11
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T01:38:55.483407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median19
Q353
95-th percentile92.8
Maximum118
Range118
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation33.329806
Coefficient of variation (CV)1.0727408
Kurtosis0.37377866
Mean31.069767
Median Absolute Deviation (MAD)19
Skewness1.0735697
Sum1336
Variance1110.876
MonotonicityNot monotonic
2023-12-11T01:38:55.689344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 11
25.6%
16 3
 
7.0%
118 2
 
4.7%
44 2
 
4.7%
25 2
 
4.7%
69 2
 
4.7%
58 1
 
2.3%
12 1
 
2.3%
1 1
 
2.3%
82 1
 
2.3%
Other values (17) 17
39.5%
ValueCountFrequency (%)
0 11
25.6%
1 1
 
2.3%
2 1
 
2.3%
6 1
 
2.3%
9 1
 
2.3%
12 1
 
2.3%
15 1
 
2.3%
16 3
 
7.0%
18 1
 
2.3%
19 1
 
2.3%
ValueCountFrequency (%)
118 2
4.7%
94 1
2.3%
82 1
2.3%
74 1
2.3%
72 1
2.3%
69 2
4.7%
61 1
2.3%
58 1
2.3%
56 1
2.3%
50 1
2.3%

등주(FRP)(본)
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86046512
Minimum0
Maximum20
Zeros38
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T01:38:55.875677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.8
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.3351047
Coefficient of variation (CV)3.8759325
Kurtosis27.217593
Mean0.86046512
Median Absolute Deviation (MAD)0
Skewness5.0025736
Sum37
Variance11.122924
MonotonicityNot monotonic
2023-12-11T01:38:56.060139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 38
88.4%
20 1
 
2.3%
1 1
 
2.3%
8 1
 
2.3%
5 1
 
2.3%
3 1
 
2.3%
ValueCountFrequency (%)
0 38
88.4%
1 1
 
2.3%
3 1
 
2.3%
5 1
 
2.3%
8 1
 
2.3%
20 1
 
2.3%
ValueCountFrequency (%)
20 1
 
2.3%
8 1
 
2.3%
5 1
 
2.3%
3 1
 
2.3%
1 1
 
2.3%
0 38
88.4%

Interactions

2023-12-11T01:38:50.964497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:47.267333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:47.941973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.560329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.185130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.734815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.357652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:51.063671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:47.360401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.025447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.663283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.262836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.809358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.451610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:51.196111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:47.457919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.114786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.759466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.353318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.884934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.531957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:51.296480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:47.550878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.208988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.839046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.437946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.977153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.609916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:51.402506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:47.633150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.294906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.925591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.513029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.071467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.702873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:51.507737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:47.723594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.377836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.018561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.584182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.158488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.785565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:51.625368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:47.810880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:48.463475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.098163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:49.659864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.247890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:38:50.871592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:38:56.198130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명위치가로등_합계가로등_일반가로등_고효율_소계가로등_고효율_LED가로등_고효율_기타총등주(본)등주(FRP)(본)
도로명1.0001.0000.0000.0000.8600.7380.8110.8161.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.000
가로등_합계0.0001.0001.0000.7390.8870.6640.8170.8700.528
가로등_일반0.0001.0000.7391.0000.0000.0000.1270.7280.673
가로등_고효율_소계0.8601.0000.8870.0001.0000.7100.7360.9500.000
가로등_고효율_LED0.7381.0000.6640.0000.7101.0000.0000.6580.000
가로등_고효율_기타0.8111.0000.8170.1270.7360.0001.0000.6480.290
총등주(본)0.8161.0000.8700.7280.9500.6580.6481.0000.573
등주(FRP)(본)1.0001.0000.5280.6730.0000.0000.2900.5731.000
2023-12-11T01:38:56.373608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가로등_합계가로등_일반가로등_고효율_소계가로등_고효율_LED가로등_고효율_기타총등주(본)등주(FRP)(본)
가로등_합계1.0000.2350.7190.2200.4250.7690.281
가로등_일반0.2351.000-0.317-0.4140.0890.1750.447
가로등_고효율_소계0.719-0.3171.0000.4820.4350.5680.026
가로등_고효율_LED0.220-0.4140.4821.000-0.4760.098-0.096
가로등_고효율_기타0.4250.0890.435-0.4761.0000.4730.168
총등주(본)0.7690.1750.5680.0980.4731.0000.354
등주(FRP)(본)0.2810.4470.026-0.0960.1680.3541.000

Missing values

2023-12-11T01:38:51.817894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:38:52.336214image/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

도로명위치가로등_합계가로등_일반가로등_고효율_소계가로등_고효율_LED가로등_고효율_기타총등주(본)등주(FRP)(본)
0부산광역시 중구 충장대로무역회관앞~동구경계5819390395820
1부산광역시 중구 대영로DSEC빌딩~부산터널69069690690
2부산광역시 중구 구덕로옛시청교차로~자갈치공영주차장692675314691
3부산광역시 중구 중앙대로옛시청교차로~영주동조흥은행11101110111720
4부산광역시 중구 대청로부산우체국~부민사거리7414604020740
5부산광역시 중구 대교로부산대교~부산본부세관1181126061180
6부산광역시 중구 보수로자갈치사거리~흑교사거리1261251011188
7부산광역시 중구 태종로옛시청교차로~영도대교18990990
8부산광역시 중구 해관로중부경찰서~부산데파트44044440440
9부산광역시 중구 백산길백산기념관~한국투자증권16016160160
도로명위치가로등_합계가로등_일반가로등_고효율_소계가로등_고효율_LED가로등_고효율_기타총등주(본)등주(FRP)(본)
33부산광역시 중구 남포길엔터테이너거리9900000
34부산광역시 중구 태종로영도대교입구(백화점 측면)5500000
35부산광역시 중구 대교로부산대교 상부58481010000
36부산광역시 중구 중앙대로중앙대로223210100
37부산광역시 중구 중구로188번길영주교회일원2251717000
38부산광역시 중구 대청로135번길40계단문화테마거리4400000
39부산광역시 중구 영주고가교 하부등영주고가교 교각하부등2022000
40부산광역시 중구 복병산길복병산길9-13(큰마루터진입로)3033000
41부산광역시 중구 용두산공원 밑 체육공원광복동1가74044000
42부산광역시 중구 광복로35번길아리랑거리5105151000