Overview

Dataset statistics

Number of variables11
Number of observations596
Missing cells1580
Missing cells (%)24.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.4 KiB
Average record size in memory95.2 B

Variable types

Numeric7
Boolean2
Categorical2

Dataset

Description생활안전융합데이터를 구축하기 위해 정비한 데이터입니다. 300셀 격자단위로 의왕시 교통생활안전관련 예방시설물 통계정보를 표현한 데이터입니다. 도로횡단, 과속방지턱, 신호등, 방호울타리, 중앙분리대, 표지판 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15108861/fileData.do

Alerts

데이터 생성일자 has constant value ""Constant
격자 ID is highly overall correlated with 격자 X축좌표 and 1 other fieldsHigh correlation
격자 X축좌표 is highly overall correlated with 격자 ID and 1 other fieldsHigh correlation
격자 Y축좌표 is highly overall correlated with 격자 ID and 2 other fieldsHigh correlation
도로횡단 수 is highly overall correlated with 신호등 수 and 1 other fieldsHigh correlation
신호등 수 is highly overall correlated with 도로횡단 수 and 1 other fieldsHigh correlation
표지판 수 is highly overall correlated with 도로횡단 수 and 1 other fieldsHigh correlation
방호울타리 여부 is highly overall correlated with 중앙분리대 여부High correlation
중앙분리대 여부 is highly overall correlated with 방호울타리 여부High correlation
행정동명 is highly overall correlated with 격자 Y축좌표High correlation
도로횡단 수 has 404 (67.8%) missing valuesMissing
과속방지턱 수 has 435 (73.0%) missing valuesMissing
신호등 수 has 437 (73.3%) missing valuesMissing
표지판 수 has 304 (51.0%) missing valuesMissing
격자 ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:09:09.783726
Analysis finished2023-12-12 21:09:15.668059
Duration5.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

격자 ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct596
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31073113
Minimum30545233
Maximum31565350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T06:09:15.744278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30545233
5-th percentile30665241
Q130905274
median31085292
Q331265282
95-th percentile31445330
Maximum31565350
Range1020117
Interquartile range (IQR)360007.5

Descriptive statistics

Standard deviation234529.51
Coefficient of variation (CV)0.007547667
Kurtosis-0.73120697
Mean31073113
Median Absolute Deviation (MAD)180004.5
Skewness-0.15071067
Sum1.8519575 × 1010
Variance5.5004091 × 1010
MonotonicityNot monotonic
2023-12-13T06:09:15.905958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30635227 1
 
0.2%
30995314 1
 
0.2%
31055314 1
 
0.2%
31085314 1
 
0.2%
31115314 1
 
0.2%
31145314 1
 
0.2%
31175314 1
 
0.2%
31205314 1
 
0.2%
31235314 1
 
0.2%
31265314 1
 
0.2%
Other values (586) 586
98.3%
ValueCountFrequency (%)
30545233 1
0.2%
30545239 1
0.2%
30545242 1
0.2%
30545245 1
0.2%
30575230 1
0.2%
30575233 1
0.2%
30575236 1
0.2%
30575239 1
0.2%
30575242 1
0.2%
30575245 1
0.2%
ValueCountFrequency (%)
31565350 1
0.2%
31565347 1
0.2%
31535350 1
0.2%
31535347 1
0.2%
31535344 1
0.2%
31535341 1
0.2%
31505350 1
0.2%
31505347 1
0.2%
31505344 1
0.2%
31505341 1
0.2%

격자 X축좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310721.19
Minimum305443
Maximum315643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T06:09:16.041209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum305443
5-th percentile306643
Q1309043
median310843
Q3312643
95-th percentile314443
Maximum315643
Range10200
Interquartile range (IQR)3600

Descriptive statistics

Standard deviation2345.0346
Coefficient of variation (CV)0.0075470702
Kurtosis-0.73124664
Mean310721.19
Median Absolute Deviation (MAD)1800
Skewness-0.15065577
Sum1.8518983 × 108
Variance5499187.3
MonotonicityNot monotonic
2023-12-13T06:09:16.193210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
310543 29
 
4.9%
310843 28
 
4.7%
310243 28
 
4.7%
312343 27
 
4.5%
311143 26
 
4.4%
312643 25
 
4.2%
312043 25
 
4.2%
311743 25
 
4.2%
311443 25
 
4.2%
309943 25
 
4.2%
Other values (25) 333
55.9%
ValueCountFrequency (%)
305443 4
 
0.7%
305743 6
 
1.0%
306043 7
 
1.2%
306343 8
1.3%
306643 7
 
1.2%
306943 10
1.7%
307243 15
2.5%
307543 14
2.3%
307843 15
2.5%
308143 18
3.0%
ValueCountFrequency (%)
315643 2
 
0.3%
315343 4
 
0.7%
315043 7
 
1.2%
314743 11
1.8%
314443 12
2.0%
314143 12
2.0%
313843 17
2.9%
313543 18
3.0%
313243 20
3.4%
312943 24
4.0%

격자 Y축좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529443.35
Minimum522716
Maximum535016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T06:09:16.325000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum522716
5-th percentile523616
Q1526916
median529616
Q3532316
95-th percentile534416
Maximum535016
Range12300
Interquartile range (IQR)5400

Descriptive statistics

Standard deviation3291.8908
Coefficient of variation (CV)0.006217645
Kurtosis-0.95285719
Mean529443.35
Median Absolute Deviation (MAD)2700
Skewness-0.23965475
Sum3.1554824 × 108
Variance10836545
MonotonicityIncreasing
2023-12-13T06:09:16.472814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
532016 19
 
3.2%
531716 18
 
3.0%
529916 18
 
3.0%
530216 18
 
3.0%
530516 18
 
3.0%
526916 18
 
3.0%
532616 18
 
3.0%
532316 18
 
3.0%
530816 18
 
3.0%
531416 18
 
3.0%
Other values (32) 415
69.6%
ValueCountFrequency (%)
522716 4
 
0.7%
523016 9
1.5%
523316 11
1.8%
523616 10
1.7%
523916 11
1.8%
524216 11
1.8%
524516 11
1.8%
524816 9
1.5%
525116 6
1.0%
525416 7
1.2%
ValueCountFrequency (%)
535016 6
 
1.0%
534716 12
2.0%
534416 13
2.2%
534116 16
2.7%
533816 17
2.9%
533516 16
2.7%
533216 17
2.9%
532916 17
2.9%
532616 18
3.0%
532316 18
3.0%

도로횡단 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)9.9%
Missing404
Missing (%)67.8%
Infinite0
Infinite (%)0.0%
Mean4.8854167
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T06:09:16.589817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile14
Maximum22
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.3048002
Coefficient of variation (CV)0.88115313
Kurtosis2.18968
Mean4.8854167
Median Absolute Deviation (MAD)2
Skewness1.5018526
Sum938
Variance18.531305
MonotonicityNot monotonic
2023-12-13T06:09:16.698021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 43
 
7.2%
2 32
 
5.4%
3 29
 
4.9%
5 14
 
2.3%
7 13
 
2.2%
6 11
 
1.8%
4 9
 
1.5%
8 8
 
1.3%
10 8
 
1.3%
14 4
 
0.7%
Other values (9) 21
 
3.5%
(Missing) 404
67.8%
ValueCountFrequency (%)
1 43
7.2%
2 32
5.4%
3 29
4.9%
4 9
 
1.5%
5 14
 
2.3%
6 11
 
1.8%
7 13
 
2.2%
8 8
 
1.3%
9 4
 
0.7%
10 8
 
1.3%
ValueCountFrequency (%)
22 1
 
0.2%
20 2
 
0.3%
18 1
 
0.2%
16 2
 
0.3%
15 1
 
0.2%
14 4
0.7%
13 2
 
0.3%
12 4
0.7%
11 4
0.7%
10 8
1.3%

과속방지턱 수
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)10.6%
Missing435
Missing (%)73.0%
Infinite0
Infinite (%)0.0%
Mean3.6521739
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T06:09:16.823853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile12
Maximum19
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7521009
Coefficient of variation (CV)1.0273609
Kurtosis3.8132721
Mean3.6521739
Median Absolute Deviation (MAD)1
Skewness2.0002198
Sum588
Variance14.078261
MonotonicityNot monotonic
2023-12-13T06:09:16.935570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 56
 
9.4%
2 35
 
5.9%
3 20
 
3.4%
5 12
 
2.0%
4 8
 
1.3%
6 5
 
0.8%
9 5
 
0.8%
8 4
 
0.7%
12 3
 
0.5%
7 2
 
0.3%
Other values (7) 11
 
1.8%
(Missing) 435
73.0%
ValueCountFrequency (%)
1 56
9.4%
2 35
5.9%
3 20
 
3.4%
4 8
 
1.3%
5 12
 
2.0%
6 5
 
0.8%
7 2
 
0.3%
8 4
 
0.7%
9 5
 
0.8%
10 2
 
0.3%
ValueCountFrequency (%)
19 1
 
0.2%
18 1
 
0.2%
16 1
 
0.2%
15 2
 
0.3%
14 2
 
0.3%
12 3
0.5%
11 2
 
0.3%
10 2
 
0.3%
9 5
0.8%
8 4
0.7%

신호등 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)13.8%
Missing437
Missing (%)73.3%
Infinite0
Infinite (%)0.0%
Mean7.672956
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T06:09:17.083564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q311
95-th percentile18
Maximum27
Range26
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.8151884
Coefficient of variation (CV)0.75788111
Kurtosis0.71280169
Mean7.672956
Median Absolute Deviation (MAD)4
Skewness0.99230936
Sum1220
Variance33.816416
MonotonicityNot monotonic
2023-12-13T06:09:17.213174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 20
 
3.4%
2 15
 
2.5%
3 13
 
2.2%
7 12
 
2.0%
4 12
 
2.0%
5 10
 
1.7%
8 9
 
1.5%
6 9
 
1.5%
10 9
 
1.5%
13 8
 
1.3%
Other values (12) 42
 
7.0%
(Missing) 437
73.3%
ValueCountFrequency (%)
1 20
3.4%
2 15
2.5%
3 13
2.2%
4 12
2.0%
5 10
1.7%
6 9
1.5%
7 12
2.0%
8 9
1.5%
9 5
 
0.8%
10 9
1.5%
ValueCountFrequency (%)
27 2
 
0.3%
24 2
 
0.3%
21 1
 
0.2%
19 2
 
0.3%
18 3
 
0.5%
17 4
0.7%
16 3
 
0.5%
15 4
0.7%
14 4
0.7%
13 8
1.3%

방호울타리 여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size728.0 B
True
307 
False
289 
ValueCountFrequency (%)
True 307
51.5%
False 289
48.5%
2023-12-13T06:09:17.323720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

중앙분리대 여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size728.0 B
False
417 
True
179 
ValueCountFrequency (%)
False 417
70.0%
True 179
30.0%
2023-12-13T06:09:17.415472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

표지판 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)24.3%
Missing304
Missing (%)51.0%
Infinite0
Infinite (%)0.0%
Mean20.791096
Minimum1
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T06:09:17.549707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median14
Q329
95-th percentile65.9
Maximum124
Range123
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.014698
Coefficient of variation (CV)1.0588522
Kurtosis3.6720994
Mean20.791096
Median Absolute Deviation (MAD)10
Skewness1.8299022
Sum6071
Variance484.64693
MonotonicityNot monotonic
2023-12-13T06:09:17.702670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 19
 
3.2%
2 19
 
3.2%
4 17
 
2.9%
3 16
 
2.7%
14 13
 
2.2%
12 11
 
1.8%
9 10
 
1.7%
6 10
 
1.7%
5 9
 
1.5%
8 9
 
1.5%
Other values (61) 159
26.7%
(Missing) 304
51.0%
ValueCountFrequency (%)
1 19
3.2%
2 19
3.2%
3 16
2.7%
4 17
2.9%
5 9
1.5%
6 10
1.7%
7 7
 
1.2%
8 9
1.5%
9 10
1.7%
10 8
1.3%
ValueCountFrequency (%)
124 1
0.2%
107 1
0.2%
101 1
0.2%
100 1
0.2%
96 1
0.2%
93 1
0.2%
88 1
0.2%
87 1
0.2%
82 1
0.2%
77 1
0.2%

행정동명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
청계동
265 
부곡동
122 
고천동
96 
오전동
74 
내손1동
 
22
Other values (2)
 
17

Length

Max length4
Median length3
Mean length3.0654362
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
청계동 265
44.5%
부곡동 122
20.5%
고천동 96
 
16.1%
오전동 74
 
12.4%
내손1동 22
 
3.7%
내손2동 16
 
2.7%
군포1동 1
 
0.2%

Length

2023-12-13T06:09:17.851496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:09:17.967344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청계동 265
44.5%
부곡동 122
20.5%
고천동 96
 
16.1%
오전동 74
 
12.4%
내손1동 22
 
3.7%
내손2동 16
 
2.7%
군포1동 1
 
0.2%

데이터 생성일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2022-10-19
596 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-10-19
2nd row2022-10-19
3rd row2022-10-19
4th row2022-10-19
5th row2022-10-19

Common Values

ValueCountFrequency (%)
2022-10-19 596
100.0%

Length

2023-12-13T06:09:18.096477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:09:18.200844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-10-19 596
100.0%

Interactions

2023-12-13T06:09:14.563303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.219079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.869891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.520400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.184401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.795329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.618830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:14.665452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.309966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.969832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.612225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.268660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.913464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.702710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:14.765842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.426068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.075388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.702919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.343265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.028982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.806569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:14.854376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.527433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.156392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.785367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.428691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.147859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.901300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:14.948971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.618132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.233099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.878582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.515293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.266206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:14.290694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:15.035889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.717211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.333589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.999907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.591991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.377062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:14.391021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:15.130116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:10.791019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:11.428986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.099390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:12.696207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:13.487031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:09:14.478280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:09:18.265081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자 ID격자 X축좌표격자 Y축좌표도로횡단 수과속방지턱 수신호등 수방호울타리 여부중앙분리대 여부표지판 수행정동명
격자 ID1.0001.0000.7780.0000.0000.0000.4550.3990.3650.725
격자 X축좌표1.0001.0000.7720.0000.0000.0000.4590.4140.3590.727
격자 Y축좌표0.7780.7721.0000.3380.3580.0000.3590.4480.2960.811
도로횡단 수0.0000.0000.3381.0000.5820.7560.0000.2300.7030.132
과속방지턱 수0.0000.0000.3580.5821.0000.0000.1110.3350.0000.449
신호등 수0.0000.0000.0000.7560.0001.0000.0000.0000.7810.087
방호울타리 여부0.4550.4590.3590.0000.1110.0001.0000.7460.1590.222
중앙분리대 여부0.3990.4140.4480.2300.3350.0000.7461.0000.2610.227
표지판 수0.3650.3590.2960.7030.0000.7810.1590.2611.0000.048
행정동명0.7250.7270.8110.1320.4490.0870.2220.2270.0481.000
2023-12-13T06:09:18.395767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중앙분리대 여부행정동명방호울타리 여부
중앙분리대 여부1.0000.2420.536
행정동명0.2421.0000.237
방호울타리 여부0.5360.2371.000
2023-12-13T06:09:18.490037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자 ID격자 X축좌표격자 Y축좌표도로횡단 수과속방지턱 수신호등 수표지판 수방호울타리 여부중앙분리대 여부행정동명
격자 ID1.0000.9990.7830.020-0.0490.0410.1170.3480.3020.478
격자 X축좌표0.9991.0000.7620.014-0.0520.0310.1090.3480.3030.481
격자 Y축좌표0.7830.7621.0000.0980.0010.0950.1840.2800.3400.591
도로횡단 수0.0200.0140.0981.0000.2420.6280.5330.0000.1720.064
과속방지턱 수-0.049-0.0520.0010.2421.0000.0970.2090.0810.2620.251
신호등 수0.0410.0310.0950.6280.0971.0000.6420.0000.0000.039
표지판 수0.1170.1090.1840.5330.2090.6421.0000.1200.1970.022
방호울타리 여부0.3480.3480.2800.0000.0810.0000.1201.0000.5360.237
중앙분리대 여부0.3020.3030.3400.1720.2620.0000.1970.5361.0000.242
행정동명0.4780.4810.5910.0640.2510.0390.0220.2370.2421.000

Missing values

2023-12-13T06:09:15.301423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:09:15.482403image/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.
2023-12-13T06:09:15.611053image/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

격자 ID격자 X축좌표격자 Y축좌표도로횡단 수과속방지턱 수신호등 수방호울타리 여부중앙분리대 여부표지판 수행정동명데이터 생성일자
030635227306343522716<NA><NA><NA>YN10부곡동2022-10-19
1306652273066435227162<NA><NA>YN10부곡동2022-10-19
230695227306943522716<NA><NA><NA>YN<NA>부곡동2022-10-19
330725227307243522716<NA><NA><NA>YN<NA>부곡동2022-10-19
430575230305743523016<NA><NA><NA>YY<NA>부곡동2022-10-19
530605230306043523016<NA><NA><NA>NN<NA>부곡동2022-10-19
630635230306343523016214YN23부곡동2022-10-19
730665230306643523016<NA>1<NA>YN7부곡동2022-10-19
830695230306943523016<NA><NA><NA>NN<NA>부곡동2022-10-19
93072523030724352301612<NA>YN1부곡동2022-10-19
격자 ID격자 X축좌표격자 Y축좌표도로횡단 수과속방지턱 수신호등 수방호울타리 여부중앙분리대 여부표지판 수행정동명데이터 생성일자
58631475347314743534716<NA><NA><NA>YN18청계동2022-10-19
58731505347315043534716<NA><NA><NA>NN<NA>청계동2022-10-19
58831535347315343534716<NA><NA><NA>NN<NA>청계동2022-10-19
58931565347315643534716<NA><NA><NA>NN<NA>청계동2022-10-19
59031295350312943535016<NA><NA><NA>NN<NA>청계동2022-10-19
59131325350313243535016<NA><NA><NA>NN<NA>청계동2022-10-19
59231475350314743535016<NA><NA><NA>NN<NA>청계동2022-10-19
59331505350315043535016<NA><NA><NA>NN<NA>청계동2022-10-19
59431535350315343535016<NA><NA><NA>NN<NA>청계동2022-10-19
59531565350315643535016<NA><NA><NA>NN<NA>청계동2022-10-19