Overview

Dataset statistics

Number of variables11
Number of observations596
Missing cells1349
Missing cells (%)20.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.0 KiB
Average record size in memory96.2 B

Variable types

Numeric7
Categorical2
Boolean1
DateTime1

Dataset

Description생활안전융합데이터를 구축하기 위해 정비한 데이터입니다. 300셀 격자단위로 의왕시 생활안전 관련 예방시설물 통계정보를 표현한 데이터입니다. CCTV,보안등,안전비상벨,가로등,미끄럼방지시설,장애인편의시설 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15108858/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
CCTV 수 is highly overall correlated with 가로등수High correlation
가로등수 is highly overall correlated with CCTV 수High correlation
행정동명 is highly overall correlated with 격자 Y축좌표High correlation
안전비상벨 수 is highly imbalanced (78.1%)Imbalance
CCTV 수 has 388 (65.1%) missing valuesMissing
보안등 수 has 291 (48.8%) missing valuesMissing
가로등수 has 286 (48.0%) missing valuesMissing
장애인편의시설 수 has 384 (64.4%) missing valuesMissing
격자 ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:13:23.877313
Analysis finished2023-12-12 22:13:28.965048
Duration5.09 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
2023-12-13T07:13:29.034893image/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
2023-12-13T07:13:29.166056image/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
2023-12-13T07:13:29.276809image/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
2023-12-13T07:13:29.397912image/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
2023-12-13T07:13:29.538352image/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
2023-12-13T07:13:29.672425image/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%

CCTV 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)6.2%
Missing388
Missing (%)65.1%
Infinite0
Infinite (%)0.0%
Mean2.9182692
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T07:13:29.781075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8.65
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5037272
Coefficient of variation (CV)0.85794937
Kurtosis3.1016626
Mean2.9182692
Median Absolute Deviation (MAD)1
Skewness1.8129147
Sum607
Variance6.2686501
MonotonicityNot monotonic
2023-12-13T07:13:29.883633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 77
 
12.9%
2 46
 
7.7%
3 28
 
4.7%
4 22
 
3.7%
5 10
 
1.7%
8 7
 
1.2%
9 4
 
0.7%
7 4
 
0.7%
10 3
 
0.5%
6 3
 
0.5%
Other values (3) 4
 
0.7%
(Missing) 388
65.1%
ValueCountFrequency (%)
1 77
12.9%
2 46
7.7%
3 28
 
4.7%
4 22
 
3.7%
5 10
 
1.7%
6 3
 
0.5%
7 4
 
0.7%
8 7
 
1.2%
9 4
 
0.7%
10 3
 
0.5%
ValueCountFrequency (%)
13 1
 
0.2%
12 2
 
0.3%
11 1
 
0.2%
10 3
 
0.5%
9 4
 
0.7%
8 7
 
1.2%
7 4
 
0.7%
6 3
 
0.5%
5 10
1.7%
4 22
3.7%

보안등 수
Real number (ℝ)

MISSING 

Distinct60
Distinct (%)19.7%
Missing291
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean19.891803
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T07:13:29.999839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q328
95-th percentile47.8
Maximum89
Range88
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.974427
Coefficient of variation (CV)0.80306583
Kurtosis2.9782049
Mean19.891803
Median Absolute Deviation (MAD)9
Skewness1.4891853
Sum6067
Variance255.18233
MonotonicityNot monotonic
2023-12-13T07:13:30.130894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 14
 
2.3%
20 13
 
2.2%
16 13
 
2.2%
7 13
 
2.2%
13 12
 
2.0%
8 12
 
2.0%
1 11
 
1.8%
12 11
 
1.8%
17 11
 
1.8%
5 10
 
1.7%
Other values (50) 185
31.0%
(Missing) 291
48.8%
ValueCountFrequency (%)
1 11
1.8%
2 14
2.3%
3 8
1.3%
4 9
1.5%
5 10
1.7%
6 7
1.2%
7 13
2.2%
8 12
2.0%
9 7
1.2%
10 10
1.7%
ValueCountFrequency (%)
89 1
0.2%
86 1
0.2%
79 1
0.2%
77 1
0.2%
75 1
0.2%
74 1
0.2%
73 1
0.2%
60 1
0.2%
59 2
0.3%
57 1
0.2%

가로등수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct73
Distinct (%)23.5%
Missing286
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean19.832258
Minimum1
Maximum107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T07:13:30.252056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q325
95-th percentile70
Maximum107
Range106
Interquartile range (IQR)20

Descriptive statistics

Standard deviation22.306868
Coefficient of variation (CV)1.124777
Kurtosis3.0602095
Mean19.832258
Median Absolute Deviation (MAD)8
Skewness1.8556222
Sum6148
Variance497.59637
MonotonicityNot monotonic
2023-12-13T07:13:30.410111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 22
 
3.7%
2 20
 
3.4%
5 19
 
3.2%
9 16
 
2.7%
4 15
 
2.5%
6 15
 
2.5%
11 12
 
2.0%
10 12
 
2.0%
3 11
 
1.8%
16 9
 
1.5%
Other values (63) 159
26.7%
(Missing) 286
48.0%
ValueCountFrequency (%)
1 22
3.7%
2 20
3.4%
3 11
1.8%
4 15
2.5%
5 19
3.2%
6 15
2.5%
7 9
1.5%
8 9
1.5%
9 16
2.7%
10 12
2.0%
ValueCountFrequency (%)
107 1
0.2%
103 1
0.2%
100 1
0.2%
99 1
0.2%
94 1
0.2%
90 2
0.3%
89 1
0.2%
86 1
0.2%
84 1
0.2%
82 1
0.2%

안전비상벨 수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
<NA>
561 
1
 
32
2
 
3

Length

Max length4
Median length4
Mean length3.8238255
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row1
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 561
94.1%
1 32
 
5.4%
2 3
 
0.5%

Length

2023-12-13T07:13:30.536704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:30.639609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 561
94.1%
1 32
 
5.4%
2 3
 
0.5%

미끄럼방지시설 여부
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size728.0 B
False
410 
True
186 
ValueCountFrequency (%)
False 410
68.8%
True 186
31.2%
2023-12-13T07:13:30.724580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

장애인편의시설 수
Real number (ℝ)

MISSING 

Distinct48
Distinct (%)22.6%
Missing384
Missing (%)64.4%
Infinite0
Infinite (%)0.0%
Mean15.523585
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-13T07:13:30.841579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10.5
Q319.5
95-th percentile44.45
Maximum95
Range94
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation15.35533
Coefficient of variation (CV)0.98916136
Kurtosis5.6492391
Mean15.523585
Median Absolute Deviation (MAD)6.5
Skewness2.077871
Sum3291
Variance235.78617
MonotonicityNot monotonic
2023-12-13T07:13:31.001183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
8 16
 
2.7%
4 13
 
2.2%
14 12
 
2.0%
1 12
 
2.0%
6 12
 
2.0%
3 12
 
2.0%
2 12
 
2.0%
9 10
 
1.7%
17 8
 
1.3%
5 7
 
1.2%
Other values (38) 98
 
16.4%
(Missing) 384
64.4%
ValueCountFrequency (%)
1 12
2.0%
2 12
2.0%
3 12
2.0%
4 13
2.2%
5 7
1.2%
6 12
2.0%
7 5
 
0.8%
8 16
2.7%
9 10
1.7%
10 7
1.2%
ValueCountFrequency (%)
95 1
 
0.2%
86 1
 
0.2%
69 1
 
0.2%
65 1
 
0.2%
59 1
 
0.2%
53 3
0.5%
50 1
 
0.2%
46 1
 
0.2%
45 1
 
0.2%
44 3
0.5%

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

2023-12-13T07:13:31.147303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:31.252852image/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
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
Minimum2022-10-19 00:00:00
Maximum2022-10-19 00:00:00
2023-12-13T07:13:31.366527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:31.471600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T07:13:27.885477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:24.387141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.040285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.699868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.332401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.925121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.373523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:28.202207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:24.487169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.137024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.795287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.429315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.995167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.448791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:28.267857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:24.576680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.235587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.897488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.505385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.055798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.516803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:28.344702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:24.694294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.343488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.009929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.603891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.119848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.591451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:28.412154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:24.792742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.451633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.082916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.712936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.180982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.658714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:28.483245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:24.866349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.547682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.169580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.789621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.243181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.730698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:28.552348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:24.953670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:25.624650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.250500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:26.857847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.310427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:27.812076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:13:31.583250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자 ID격자 X축좌표격자 Y축좌표CCTV 수보안등 수가로등수안전비상벨 수미끄럼방지시설 여부장애인편의시설 수행정동명
격자 ID1.0001.0000.7780.0000.0240.3190.5370.0300.1820.725
격자 X축좌표1.0001.0000.7720.0000.0530.3100.4310.0530.1700.727
격자 Y축좌표0.7780.7721.0000.2020.3010.3620.3750.4610.1660.811
CCTV 수0.0000.0000.2021.0000.2740.7500.0000.2200.2760.236
보안등 수0.0240.0530.3010.2741.0000.1100.2050.2640.6580.125
가로등수0.3190.3100.3620.7500.1101.0000.0000.0000.2340.277
안전비상벨 수0.5370.4310.3750.0000.2050.0001.0000.0000.0000.325
미끄럼방지시설 여부0.0300.0530.4610.2200.2640.0000.0001.0000.0000.086
장애인편의시설 수0.1820.1700.1660.2760.6580.2340.0000.0001.0000.091
행정동명0.7250.7270.8110.2360.1250.2770.3250.0860.0911.000
2023-12-13T07:13:31.741734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
안전비상벨 수미끄럼방지시설 여부행정동명
안전비상벨 수1.0000.0000.210
미끄럼방지시설 여부0.0001.0000.092
행정동명0.2100.0921.000
2023-12-13T07:13:31.836014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자 ID격자 X축좌표격자 Y축좌표CCTV 수보안등 수가로등수장애인편의시설 수안전비상벨 수미끄럼방지시설 여부행정동명
격자 ID1.0000.9990.783-0.2280.019-0.237-0.0990.4740.0000.478
격자 X축좌표0.9991.0000.762-0.2360.014-0.243-0.1070.3750.0000.481
격자 Y축좌표0.7830.7621.000-0.0180.095-0.059-0.0240.2590.3540.591
CCTV 수-0.228-0.236-0.0181.0000.2520.5260.3680.0000.1990.141
보안등 수0.0190.0140.0950.2521.0000.1890.4430.1810.2580.073
가로등수-0.237-0.243-0.0590.5260.1891.0000.1380.0000.0000.142
장애인편의시설 수-0.099-0.107-0.0240.3680.4430.1381.0000.0000.0000.046
안전비상벨 수0.4740.3750.2590.0000.1810.0000.0001.0000.0000.210
미끄럼방지시설 여부0.0000.0000.3540.1990.2580.0000.0000.0001.0000.092
행정동명0.4780.4810.5910.1410.0730.1420.0460.2100.0921.000

Missing values

2023-12-13T07:13:28.656975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:13:28.800787image/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.
2023-12-13T07:13:28.905905image/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 Y축좌표CCTV 수보안등 수가로등수안전비상벨 수미끄럼방지시설 여부장애인편의시설 수행정동명데이터 생성일자
030635227306343522716<NA>34<NA>N6부곡동2022-10-19
130665227306643522716211111N9부곡동2022-10-19
230695227306943522716<NA><NA><NA><NA>N<NA>부곡동2022-10-19
3307252273072435227161<NA><NA><NA>N<NA>부곡동2022-10-19
430575230305743523016<NA><NA><NA><NA>N<NA>부곡동2022-10-19
530605230306043523016<NA><NA><NA><NA>N<NA>부곡동2022-10-19
63063523030634352301631616<NA>Y23부곡동2022-10-19
730665230306643523016266<NA>N2부곡동2022-10-19
830695230306943523016<NA><NA><NA><NA>N<NA>부곡동2022-10-19
9307252303072435230162713<NA>N3부곡동2022-10-19
격자 ID격자 X축좌표격자 Y축좌표CCTV 수보안등 수가로등수안전비상벨 수미끄럼방지시설 여부장애인편의시설 수행정동명데이터 생성일자
586314753473147435347161<NA>91N<NA>청계동2022-10-19
58731505347315043534716<NA><NA><NA><NA>N<NA>청계동2022-10-19
58831535347315343534716<NA><NA><NA><NA>N<NA>청계동2022-10-19
58931565347315643534716<NA><NA><NA><NA>N<NA>청계동2022-10-19
59031295350312943535016<NA><NA><NA><NA>N<NA>청계동2022-10-19
59131325350313243535016<NA><NA><NA><NA>N<NA>청계동2022-10-19
59231475350314743535016<NA><NA><NA><NA>N<NA>청계동2022-10-19
59331505350315043535016<NA><NA><NA><NA>N<NA>청계동2022-10-19
59431535350315343535016<NA><NA><NA><NA>N<NA>청계동2022-10-19
59531565350315643535016<NA><NA><NA><NA>N<NA>청계동2022-10-19