Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells207
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory898.4 KiB
Average record size in memory92.0 B

Variable types

Numeric4
Text2
DateTime1
Categorical3

Dataset

Description대전광역시 교통안전시설물관리시스템에 등록된 신호등 설치 현황
Author대전광역시
URLhttps://www.data.go.kr/data/15077621/fileData.do

Alerts

신호등 재질 has constant value ""Constant
신호등 종류 is highly overall correlated with 신호등 전구High correlation
신호등 전구 is highly overall correlated with 신호등 종류High correlation
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:26:54.851364
Analysis finished2023-12-12 06:26:58.118690
Duration3.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11120.304
Minimum2
Maximum22324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:26:58.197495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1161.9
Q15594.5
median11078.5
Q316570.25
95-th percentile21262.05
Maximum22324
Range22322
Interquartile range (IQR)10975.75

Descriptive statistics

Standard deviation6423.1181
Coefficient of variation (CV)0.57760274
Kurtosis-1.176905
Mean11120.304
Median Absolute Deviation (MAD)5489
Skewness0.01327047
Sum1.1120304 × 108
Variance41256446
MonotonicityNot monotonic
2023-12-12T15:26:58.340361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5799 1
 
< 0.1%
18102 1
 
< 0.1%
21294 1
 
< 0.1%
5966 1
 
< 0.1%
21369 1
 
< 0.1%
9049 1
 
< 0.1%
16504 1
 
< 0.1%
3048 1
 
< 0.1%
20233 1
 
< 0.1%
10329 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
7 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
18 1
< 0.1%
ValueCountFrequency (%)
22324 1
< 0.1%
22320 1
< 0.1%
22316 1
< 0.1%
22314 1
< 0.1%
22310 1
< 0.1%
22309 1
< 0.1%
22308 1
< 0.1%
22307 1
< 0.1%
22304 1
< 0.1%
22302 1
< 0.1%

주소
Text

Distinct2524
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:26:58.756358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length12.4319
Min length6

Characters and Unicode

Total characters124319
Distinct characters134
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

Unique1134 ?
Unique (%)11.3%

Sample

1st row중구 오류동 196-1도
2nd row동구 삼성동 458도
3rd row중구 목동 22-5도
4th row유성구 봉명동 469-44도
5th row중구 문창동 397-1도
ValueCountFrequency (%)
유성구 3195
 
10.2%
서구 2428
 
7.7%
동구 1763
 
5.6%
중구 1426
 
4.5%
도 1288
 
4.1%
대덕구 1188
 
3.8%
둔산동 418
 
1.3%
관저동 372
 
1.2%
지족동 277
 
0.9%
월평동 224
 
0.7%
Other values (2425) 18870
60.0%
2023-12-12T15:26:59.432163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21460
17.3%
동 11763
 
9.5%
구 10346
 
8.3%
도 9423
 
7.6%
1 6110
 
4.9%
5 4375
 
3.5%
2 3759
 
3.0%
성 3751
 
3.0%
- 3559
 
2.9%
6 3516
 
2.8%
Other values (124) 46257
37.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63970
51.5%
Decimal Number 35330
28.4%
Space Separator 21460
 
17.3%
Dash Punctuation 3559
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11763
18.4%
구 10346
16.2%
도 9423
14.7%
성 3751
 
5.9%
유 3264
 
5.1%
서 2507
 
3.9%
대 2181
 
3.4%
중 1605
 
2.5%
덕 1322
 
2.1%
산 951
 
1.5%
Other values (112) 16857
26.4%
Decimal Number
ValueCountFrequency (%)
1 6110
17.3%
5 4375
12.4%
2 3759
10.6%
6 3516
10.0%
4 3437
9.7%
3 3376
9.6%
7 3050
8.6%
0 2921
8.3%
9 2485
7.0%
8 2301
 
6.5%
Space Separator
ValueCountFrequency (%)
21460
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63970
51.5%
Common 60349
48.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11763
18.4%
구 10346
16.2%
도 9423
14.7%
성 3751
 
5.9%
유 3264
 
5.1%
서 2507
 
3.9%
대 2181
 
3.4%
중 1605
 
2.5%
덕 1322
 
2.1%
산 951
 
1.5%
Other values (112) 16857
26.4%
Common
ValueCountFrequency (%)
21460
35.6%
1 6110
 
10.1%
5 4375
 
7.2%
2 3759
 
6.2%
- 3559
 
5.9%
6 3516
 
5.8%
4 3437
 
5.7%
3 3376
 
5.6%
7 3050
 
5.1%
0 2921
 
4.8%
Other values (2) 4786
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63970
51.5%
ASCII 60349
48.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21460
35.6%
1 6110
 
10.1%
5 4375
 
7.2%
2 3759
 
6.2%
- 3559
 
5.9%
6 3516
 
5.8%
4 3437
 
5.7%
3 3376
 
5.6%
7 3050
 
5.1%
0 2921
 
4.8%
Other values (2) 4786
 
7.9%
Hangul
ValueCountFrequency (%)
동 11763
18.4%
구 10346
16.2%
도 9423
14.7%
성 3751
 
5.9%
유 3264
 
5.1%
서 2507
 
3.9%
대 2181
 
3.4%
중 1605
 
2.5%
덕 1322
 
2.1%
산 951
 
1.5%
Other values (112) 16857
26.4%

교차로명
Text

Distinct1850
Distinct (%)18.6%
Missing64
Missing (%)0.6%
Memory size156.2 KiB
2023-12-12T15:26:59.709040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length7.2702295
Min length5

Characters and Unicode

Total characters72237
Distinct characters449
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

Unique210 ?
Unique (%)2.1%

Sample

1st row오류굴다리네거리
2nd row삼성오거리
3rd row목동초교삼거리
4th row봉명네거리
5th row문창교(단)
ValueCountFrequency (%)
한일신협본점네거리 20
 
0.2%
버드내네거리 20
 
0.2%
예술가의집네거리 19
 
0.2%
중리네거리 18
 
0.2%
서대전우체국네거리 18
 
0.2%
교촌네거리 17
 
0.2%
원촌교네거리 17
 
0.2%
서대전네거리 17
 
0.2%
도시철도공사네거리 17
 
0.2%
선화공원네거리 16
 
0.2%
Other values (1865) 9825
98.2%
2023-12-12T15:27:00.136954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 8045
 
11.1%
거 7599
 
10.5%
네 4564
 
6.3%
삼 3115
 
4.3%
( 2428
 
3.4%
) 2425
 
3.4%
단 2372
 
3.3%
교 1759
 
2.4%
대 1236
 
1.7%
지 988
 
1.4%
Other values (439) 37706
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64655
89.5%
Open Punctuation 2428
 
3.4%
Close Punctuation 2425
 
3.4%
Uppercase Letter 1528
 
2.1%
Decimal Number 1079
 
1.5%
Space Separator 73
 
0.1%
Other Punctuation 24
 
< 0.1%
Lowercase Letter 19
 
< 0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 8045
 
12.4%
거 7599
 
11.8%
네 4564
 
7.1%
삼 3115
 
4.8%
단 2372
 
3.7%
교 1759
 
2.7%
대 1236
 
1.9%
지 988
 
1.5%
동 963
 
1.5%
초 949
 
1.5%
Other values (409) 33065
51.1%
Uppercase Letter
ValueCountFrequency (%)
P 465
30.4%
T 413
27.0%
A 404
26.4%
C 86
 
5.6%
B 67
 
4.4%
I 28
 
1.8%
G 18
 
1.2%
J 12
 
0.8%
L 12
 
0.8%
K 9
 
0.6%
Other values (4) 14
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 351
32.5%
2 206
19.1%
3 101
 
9.4%
5 92
 
8.5%
4 87
 
8.1%
0 74
 
6.9%
7 52
 
4.8%
9 47
 
4.4%
8 45
 
4.2%
6 24
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 2428
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2425
100.0%
Space Separator
ValueCountFrequency (%)
73
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 24
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64655
89.5%
Common 6035
 
8.4%
Latin 1547
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 8045
 
12.4%
거 7599
 
11.8%
네 4564
 
7.1%
삼 3115
 
4.8%
단 2372
 
3.7%
교 1759
 
2.7%
대 1236
 
1.9%
지 988
 
1.5%
동 963
 
1.5%
초 949
 
1.5%
Other values (409) 33065
51.1%
Common
ValueCountFrequency (%)
( 2428
40.2%
) 2425
40.2%
1 351
 
5.8%
2 206
 
3.4%
3 101
 
1.7%
5 92
 
1.5%
4 87
 
1.4%
0 74
 
1.2%
73
 
1.2%
7 52
 
0.9%
Other values (5) 146
 
2.4%
Latin
ValueCountFrequency (%)
P 465
30.1%
T 413
26.7%
A 404
26.1%
C 86
 
5.6%
B 67
 
4.3%
I 28
 
1.8%
e 19
 
1.2%
G 18
 
1.2%
J 12
 
0.8%
L 12
 
0.8%
Other values (5) 23
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64655
89.5%
ASCII 7582
 
10.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 8045
 
12.4%
거 7599
 
11.8%
네 4564
 
7.1%
삼 3115
 
4.8%
단 2372
 
3.7%
교 1759
 
2.7%
대 1236
 
1.9%
지 988
 
1.5%
동 963
 
1.5%
초 949
 
1.5%
Other values (409) 33065
51.1%
ASCII
ValueCountFrequency (%)
( 2428
32.0%
) 2425
32.0%
P 465
 
6.1%
T 413
 
5.4%
A 404
 
5.3%
1 351
 
4.6%
2 206
 
2.7%
3 101
 
1.3%
5 92
 
1.2%
4 87
 
1.1%
Other values (20) 610
 
8.0%

교차로번호
Real number (ℝ)

Distinct1850
Distinct (%)18.6%
Missing64
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean767.94857
Minimum1
Maximum1979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:27:00.319068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile53
Q1316
median732
Q31169
95-th percentile1685
Maximum1979
Range1978
Interquartile range (IQR)853

Descriptive statistics

Standard deviation511.03354
Coefficient of variation (CV)0.66545281
Kurtosis-0.87859071
Mean767.94857
Median Absolute Deviation (MAD)427
Skewness0.33246783
Sum7630337
Variance261155.28
MonotonicityNot monotonic
2023-12-12T15:27:00.769666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 20
 
0.2%
1313 20
 
0.2%
18 19
 
0.2%
1257 18
 
0.2%
65 18
 
0.2%
1301 17
 
0.2%
437 17
 
0.2%
928 17
 
0.2%
143 17
 
0.2%
434 16
 
0.2%
Other values (1840) 9757
97.6%
(Missing) 64
 
0.6%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 9
0.1%
3 10
0.1%
4 5
 
0.1%
5 7
0.1%
6 6
 
0.1%
7 9
0.1%
8 14
0.1%
9 7
0.1%
10 16
0.2%
ValueCountFrequency (%)
1979 2
< 0.1%
1978 1
 
< 0.1%
1977 1
 
< 0.1%
1976 3
< 0.1%
1974 1
 
< 0.1%
1973 3
< 0.1%
1972 3
< 0.1%
1971 1
 
< 0.1%
1970 1
 
< 0.1%
1968 2
< 0.1%

설치일자
Date

Distinct668
Distinct (%)6.7%
Missing79
Missing (%)0.8%
Memory size156.2 KiB
Minimum1970-04-15 00:00:00
Maximum2021-02-16 00:00:00
2023-12-12T15:27:00.937441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:01.098012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

신호등 종류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
차량등
5596 
보행등
3428 
경보등
609 
보조등
 
305
버스
 
62

Length

Max length3
Median length3
Mean length2.9938
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보행등
2nd row차량등
3rd row차량등
4th row보행등
5th row보조등

Common Values

ValueCountFrequency (%)
차량등 5596
56.0%
보행등 3428
34.3%
경보등 609
 
6.1%
보조등 305
 
3.0%
버스 62
 
0.6%

Length

2023-12-12T15:27:01.262959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:27:01.360767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
차량등 5596
56.0%
보행등 3428
34.3%
경보등 609
 
6.1%
보조등 305
 
3.0%
버스 62
 
0.6%

신호등 재질
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
LED
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
LED 10000
100.0%

Length

2023-12-12T15:27:01.471242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:27:01.566328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
led 10000
100.0%

신호등 전구
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2색등
3451 
3색등
3336 
4색등
2842 
1색등
 
137
황색3색등
 
89
Other values (4)
 
145

Length

Max length7
Median length3
Mean length3.0328
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2색등
2nd row5색등
3rd row4색등
4th row2색등
5th row3색등

Common Values

ValueCountFrequency (%)
2색등 3451
34.5%
3색등 3336
33.4%
4색등 2842
28.4%
1색등 137
 
1.4%
황색3색등 89
 
0.9%
5색등 81
 
0.8%
적색3색등 53
 
0.5%
화살표 3색등 10
 
0.1%
화살표 6색등 1
 
< 0.1%

Length

2023-12-12T15:27:01.673741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:27:01.803746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2색등 3451
34.5%
3색등 3346
33.4%
4색등 2842
28.4%
1색등 137
 
1.4%
황색3색등 89
 
0.9%
5색등 81
 
0.8%
적색3색등 53
 
0.5%
화살표 11
 
0.1%
6색등 1
 
< 0.1%

경도
Real number (ℝ)

Distinct9650
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.38836
Minimum127.27329
Maximum127.53724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:27:01.944911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.27329
5-th percentile127.31195
Q1127.34783
median127.39056
Q3127.42462
95-th percentile127.45778
Maximum127.53724
Range0.263958
Interquartile range (IQR)0.07678465

Descriptive statistics

Standard deviation0.045553528
Coefficient of variation (CV)0.00035759568
Kurtosis-0.86365153
Mean127.38836
Median Absolute Deviation (MAD)0.03670505
Skewness-0.12238785
Sum1273883.6
Variance0.0020751239
MonotonicityNot monotonic
2023-12-12T15:27:02.100296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.4278503 3
 
< 0.1%
127.3904459 3
 
< 0.1%
127.3768314 3
 
< 0.1%
127.3942843 3
 
< 0.1%
127.3406283 3
 
< 0.1%
127.4255314 3
 
< 0.1%
127.3664993 3
 
< 0.1%
127.3371552 3
 
< 0.1%
127.4357671 3
 
< 0.1%
127.3766938 3
 
< 0.1%
Other values (9640) 9970
99.7%
ValueCountFrequency (%)
127.2732865 1
< 0.1%
127.273451 1
< 0.1%
127.273492 1
< 0.1%
127.2735172 1
< 0.1%
127.2812565 1
< 0.1%
127.2812642 1
< 0.1%
127.2812745 1
< 0.1%
127.2814426 1
< 0.1%
127.2814572 1
< 0.1%
127.2815042 1
< 0.1%
ValueCountFrequency (%)
127.5372445 1
< 0.1%
127.5319136 1
< 0.1%
127.5149878 1
< 0.1%
127.5149784 1
< 0.1%
127.5146809 1
< 0.1%
127.5072581 1
< 0.1%
127.5006258 1
< 0.1%
127.4993992 2
< 0.1%
127.4980372 2
< 0.1%
127.4974327 1
< 0.1%

위도
Real number (ℝ)

Distinct9691
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.347918
Minimum36.200557
Maximum36.471474
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:27:02.271863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.200557
5-th percentile36.293832
Q136.319784
median36.343328
Q336.369398
95-th percentile36.43116
Maximum36.471474
Range0.27091716
Interquartile range (IQR)0.049614018

Descriptive statistics

Standard deviation0.042430606
Coefficient of variation (CV)0.0011673463
Kurtosis0.4808185
Mean36.347918
Median Absolute Deviation (MAD)0.024279525
Skewness0.2760843
Sum363479.18
Variance0.0018003563
MonotonicityNot monotonic
2023-12-12T15:27:02.462558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.35210826 3
 
< 0.1%
36.34192931 3
 
< 0.1%
36.35632939 2
 
< 0.1%
36.30796497 2
 
< 0.1%
36.29776329 2
 
< 0.1%
36.30110318 2
 
< 0.1%
36.33648127 2
 
< 0.1%
36.34670061 2
 
< 0.1%
36.34910565 2
 
< 0.1%
36.34646811 2
 
< 0.1%
Other values (9681) 9978
99.8%
ValueCountFrequency (%)
36.20055683 1
< 0.1%
36.20055834 1
< 0.1%
36.20070871 1
< 0.1%
36.20073144 1
< 0.1%
36.21221394 1
< 0.1%
36.21222145 1
< 0.1%
36.21280826 1
< 0.1%
36.21644727 1
< 0.1%
36.21649244 1
< 0.1%
36.21652747 1
< 0.1%
ValueCountFrequency (%)
36.47147399 1
< 0.1%
36.471438 1
< 0.1%
36.47140036 1
< 0.1%
36.47134657 1
< 0.1%
36.46192987 1
< 0.1%
36.4618919 1
< 0.1%
36.45906853 1
< 0.1%
36.4555998 1
< 0.1%
36.4555853 1
< 0.1%
36.45555613 1
< 0.1%

Interactions

2023-12-12T15:26:57.233745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:55.955929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.410241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.843842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:57.352330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.049534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.528187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.938789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:57.450635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.173090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.634267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:57.033552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:57.545864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.294292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:56.733611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:26:57.134283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:27:02.605357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호교차로번호신호등 종류신호등 전구경도위도
관리번호1.0000.7950.1850.1910.8290.827
교차로번호0.7951.0000.6420.3940.6390.679
신호등 종류0.1850.6421.0000.7900.1870.151
신호등 전구0.1910.3940.7901.0000.1290.126
경도0.8290.6390.1870.1291.0000.647
위도0.8270.6790.1510.1260.6471.000
2023-12-12T15:27:02.743296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신호등 전구신호등 종류
신호등 전구1.0000.612
신호등 종류0.6121.000
2023-12-12T15:27:02.846092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호교차로번호경도위도신호등 종류신호등 전구
관리번호1.0000.163-0.269-0.2420.0780.087
교차로번호0.1631.0000.1680.0290.3200.200
경도-0.2690.1681.000-0.1120.0790.059
위도-0.2420.029-0.1121.0000.0630.057
신호등 종류0.0780.3200.0790.0631.0000.612
신호등 전구0.0870.2000.0590.0570.6121.000

Missing values

2023-12-12T15:26:57.709327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:26:57.897905image/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.
2023-12-12T15:26:58.040110image/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

관리번호주소교차로명교차로번호설치일자신호등 종류신호등 재질신호등 전구경도위도
158715799중구 오류동 196-1도오류굴다리네거리2741991-12-30보행등LED2색등127.40329936.326505
174854096동구 삼성동 458도삼성오거리451980-10-01차량등LED5색등127.42333836.342294
134468249중구 목동 22-5도목동초교삼거리3322004-06-08차량등LED4색등127.4080536.333498
586515992유성구 봉명동 469-44도봉명네거리611989-12-11보행등LED2색등127.33685336.355276
147146973중구 문창동 397-1도문창교(단)9812013-01-16보조등LED3색등127.43865536.31467
950112302서구 관저동 1634 도구봉네거리4222013-11-22차량등LED4색등127.33076536.292481
191362415유성구 화암동 63-12대KT대덕센터네거리7641996-12-24보행등LED2색등127.37390936.397489
145967093중구 석교동 102-1도아래돌다리(경)19672010-04-28경보등LED3색등127.44310236.310846
875113069서구 관저동 1506도관저3지구네거리4142002-09-06보행등LED2색등127.34071836.300098
192272324유성구 전민동 395-13도세종APT(단)13091996-09-21차량등LED3색등127.40254136.399613
관리번호주소교차로명교차로번호설치일자신호등 종류신호등 재질신호등 전구경도위도
175943983동구 삼성동 457 도삼성네거리411979-07-01차량등LED4색등127.42965336.338168
158485822중구 오류동 196-1도서대전역(단)9892010-11-05차량등LED3색등127.40470236.322643
1164110102서구 월평동 301대누리(단)12501997-08-31보행등LED2색등127.3713936.359729
490617013유성구 하기동 158-5전아래터울마을(단)5132010-05-20차량등LED3색등127.33968436.38504
179343641대덕구 비래동 520도대전나들목네거리13171993-06-25보행등LED2색등127.44559936.357693
160345614동구 인동 352 도인동네거리781985-05-01차량등LED4색등127.43755636.321664
127208989서구 월평동 114-5도도시철도공사네거리9282013-10-15차량등LED4색등127.35340536.349927
21189342대덕구 석봉동 781 도엑슬루타워(경)17722012-02-01경보등LED적색3색등127.41889336.449827
562116247유성구 봉명동 570-1도유성(단)8132010-10-07보행등LED2색등127.33652136.356331
125619152서구 갈마동 704-1구갈마초교삼거리621993-12-01차량등LED3색등127.36936936.355188