Overview

Dataset statistics

Number of variables15
Number of observations596
Missing cells2569
Missing cells (%)28.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.4 KiB
Average record size in memory131.2 B

Variable types

Numeric8
Boolean2
Categorical5

Dataset

Description생활안전융합데이터를 구축하기 위해 정비한 데이터입니다. 300셀 격자단위로 의왕시 생활안전 관련 대상물 통계정보를 표현한 데이터입니다. 주차장, 도시공원, 어린이보호구역, 공중화장실, 전통시장, 어린이집, 자전거보관소, 초중등학교위치, 여성안심택배함, 정류장 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15108856/fileData.do

Alerts

데이터 생성일자 has constant value ""Constant
여성안심택배함 수 is highly overall correlated with 격자 ID and 10 other fieldsHigh correlation
어린이보호구역 여부 is highly overall correlated with 여성안심택배함 수High correlation
도시공원 여부 is highly overall correlated with 여성안심택배함 수High correlation
행정동명 is highly overall correlated with 격자 Y축좌표 and 2 other fieldsHigh correlation
격자 ID is highly overall correlated with 격자 X축좌표 and 2 other fieldsHigh correlation
격자 X축좌표 is highly overall correlated with 격자 ID and 2 other fieldsHigh correlation
격자 Y축좌표 is highly overall correlated with 격자 ID and 3 other fieldsHigh correlation
주차수 is highly overall correlated with 여성안심택배함 수High correlation
공중화장실 수 is highly overall correlated with 초·중등학교 수 and 1 other fieldsHigh correlation
어린이집 수 is highly overall correlated with 여성안심택배함 수High correlation
자전거보관소 수 is highly overall correlated with 초·중등학교 수 and 1 other fieldsHigh correlation
정류장 수 is highly overall correlated with 여성안심택배함 수High correlation
초·중등학교 수 is highly overall correlated with 공중화장실 수 and 2 other fieldsHigh correlation
어린이보호구역 여부 is highly imbalanced (52.9%)Imbalance
전통시장 수 is highly imbalanced (98.2%)Imbalance
초·중등학교 수 is highly imbalanced (83.5%)Imbalance
여성안심택배함 수 is highly imbalanced (93.0%)Imbalance
주차수 has 539 (90.4%) missing valuesMissing
공중화장실 수 has 513 (86.1%) missing valuesMissing
어린이집 수 has 517 (86.7%) missing valuesMissing
자전거보관소 수 has 524 (87.9%) missing valuesMissing
정류장 수 has 476 (79.9%) missing valuesMissing
격자 ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:32:41.191592
Analysis finished2023-12-12 09:32:50.192601
Duration9 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
2023-12-12T18:32:50.292031image/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
2023-12-12T18:32:50.452535image/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
2023-12-12T18:32:50.585183image/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
2023-12-12T18:32:50.718038image/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
2023-12-12T18:32:50.852275image/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
2023-12-12T18:32:51.009035image/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%

주차수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)12.3%
Missing539
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean2.0350877
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-12T18:32:51.156147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6686705
Coefficient of variation (CV)0.81995015
Kurtosis9.322999
Mean2.0350877
Median Absolute Deviation (MAD)0
Skewness2.6908635
Sum116
Variance2.7844612
MonotonicityNot monotonic
2023-12-12T18:32:51.279042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 30
 
5.0%
2 13
 
2.2%
3 7
 
1.2%
4 3
 
0.5%
5 2
 
0.3%
10 1
 
0.2%
7 1
 
0.2%
(Missing) 539
90.4%
ValueCountFrequency (%)
1 30
5.0%
2 13
2.2%
3 7
 
1.2%
4 3
 
0.5%
5 2
 
0.3%
7 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
7 1
 
0.2%
5 2
 
0.3%
4 3
 
0.5%
3 7
 
1.2%
2 13
2.2%
1 30
5.0%

도시공원 여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size728.0 B
False
514 
True
82 
ValueCountFrequency (%)
False 514
86.2%
True 82
 
13.8%
2023-12-12T18:32:51.382577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

어린이보호구역 여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size728.0 B
False
536 
True
60 
ValueCountFrequency (%)
False 536
89.9%
True 60
 
10.1%
2023-12-12T18:32:51.505219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

공중화장실 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)8.4%
Missing513
Missing (%)86.1%
Infinite0
Infinite (%)0.0%
Mean1.6385542
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-12T18:32:51.625951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2550222
Coefficient of variation (CV)0.76593269
Kurtosis15.366455
Mean1.6385542
Median Absolute Deviation (MAD)0
Skewness3.4134497
Sum136
Variance1.5750808
MonotonicityNot monotonic
2023-12-12T18:32:51.776811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 54
 
9.1%
2 17
 
2.9%
3 8
 
1.3%
5 1
 
0.2%
4 1
 
0.2%
6 1
 
0.2%
9 1
 
0.2%
(Missing) 513
86.1%
ValueCountFrequency (%)
1 54
9.1%
2 17
 
2.9%
3 8
 
1.3%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
6 1
 
0.2%
5 1
 
0.2%
4 1
 
0.2%
3 8
 
1.3%
2 17
 
2.9%
1 54
9.1%

전통시장 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
<NA>
595 
1
 
1

Length

Max length4
Median length4
Mean length3.9949664
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 595
99.8%
1 1
 
0.2%

Length

2023-12-12T18:32:51.960011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:52.099477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 595
99.8%
1 1
 
0.2%

어린이집 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)13.9%
Missing517
Missing (%)86.7%
Infinite0
Infinite (%)0.0%
Mean2.8227848
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-12T18:32:52.223274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33.5
95-th percentile9
Maximum11
Range10
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.5204776
Coefficient of variation (CV)0.89290463
Kurtosis2.1157237
Mean2.8227848
Median Absolute Deviation (MAD)1
Skewness1.6937786
Sum223
Variance6.3528075
MonotonicityNot monotonic
2023-12-12T18:32:52.371816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 32
 
5.4%
2 20
 
3.4%
3 7
 
1.2%
4 6
 
1.0%
6 4
 
0.7%
5 2
 
0.3%
8 2
 
0.3%
10 2
 
0.3%
9 2
 
0.3%
7 1
 
0.2%
(Missing) 517
86.7%
ValueCountFrequency (%)
1 32
5.4%
2 20
3.4%
3 7
 
1.2%
4 6
 
1.0%
5 2
 
0.3%
6 4
 
0.7%
7 1
 
0.2%
8 2
 
0.3%
9 2
 
0.3%
10 2
 
0.3%
ValueCountFrequency (%)
11 1
 
0.2%
10 2
 
0.3%
9 2
 
0.3%
8 2
 
0.3%
7 1
 
0.2%
6 4
 
0.7%
5 2
 
0.3%
4 6
 
1.0%
3 7
 
1.2%
2 20
3.4%

자전거보관소 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)11.1%
Missing524
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean2.2916667
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-12T18:32:52.528808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum25
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.110353
Coefficient of variation (CV)1.3572449
Kurtosis40.679608
Mean2.2916667
Median Absolute Deviation (MAD)0
Skewness5.7716631
Sum165
Variance9.6742958
MonotonicityNot monotonic
2023-12-12T18:32:52.673241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 42
 
7.0%
2 10
 
1.7%
3 10
 
1.7%
4 3
 
0.5%
7 2
 
0.3%
6 2
 
0.3%
5 2
 
0.3%
25 1
 
0.2%
(Missing) 524
87.9%
ValueCountFrequency (%)
1 42
7.0%
2 10
 
1.7%
3 10
 
1.7%
4 3
 
0.5%
5 2
 
0.3%
6 2
 
0.3%
7 2
 
0.3%
25 1
 
0.2%
ValueCountFrequency (%)
25 1
 
0.2%
7 2
 
0.3%
6 2
 
0.3%
5 2
 
0.3%
4 3
 
0.5%
3 10
 
1.7%
2 10
 
1.7%
1 42
7.0%

초·중등학교 수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
<NA>
571 
1
 
24
2
 
1

Length

Max length4
Median length4
Mean length3.8741611
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 571
95.8%
1 24
 
4.0%
2 1
 
0.2%

Length

2023-12-12T18:32:52.852296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:52.981520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 571
95.8%
1 24
 
4.0%
2 1
 
0.2%

여성안심택배함 수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
<NA>
591 
1
 
5

Length

Max length4
Median length4
Mean length3.9748322
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 591
99.2%
1 5
 
0.8%

Length

2023-12-12T18:32:53.158846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:53.300682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 591
99.2%
1 5
 
0.8%

정류장 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)5.0%
Missing476
Missing (%)79.9%
Infinite0
Infinite (%)0.0%
Mean2.275
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-12T18:32:53.443901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.321843
Coefficient of variation (CV)0.58102989
Kurtosis0.70389146
Mean2.275
Median Absolute Deviation (MAD)1
Skewness1.1432039
Sum273
Variance1.7472689
MonotonicityNot monotonic
2023-12-12T18:32:53.595873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 44
 
7.4%
1 39
 
6.5%
3 16
 
2.7%
4 11
 
1.8%
5 6
 
1.0%
6 4
 
0.7%
(Missing) 476
79.9%
ValueCountFrequency (%)
1 39
6.5%
2 44
7.4%
3 16
 
2.7%
4 11
 
1.8%
5 6
 
1.0%
6 4
 
0.7%
ValueCountFrequency (%)
6 4
 
0.7%
5 6
 
1.0%
4 11
 
1.8%
3 16
 
2.7%
2 44
7.4%
1 39
6.5%

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

2023-12-12T18:32:53.774770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:53.918266image/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

2023-12-12T18:32:54.077010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:32:54.189775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-10-19 596
100.0%

Interactions

2023-12-12T18:32:48.500946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:42.310952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.200377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.106404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.997990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.773798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:46.772191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.659310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:48.621632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:42.429604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.336437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.235879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.107168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.908106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:46.899107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.755403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:48.725487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:42.526175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.457362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.340377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.205789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:46.039231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.017566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.876858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:48.825167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:42.624855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.578478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.459152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.302553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:46.151460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.132870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.964271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:48.933332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:42.721636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.679122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.548485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.395237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:46.286995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.261683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:48.046918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:49.041255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:42.846872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.787384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.676957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.492967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:46.424500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.365451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:48.152182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:49.132236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:42.956802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.900304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.760066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.590265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:46.533579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.454381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:48.283637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:49.258293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:43.060078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.006943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:44.887155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:45.676833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:46.655169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:47.569867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:32:48.400827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:32:54.275179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자 ID격자 X축좌표격자 Y축좌표주차수도시공원 여부어린이보호구역 여부공중화장실 수어린이집 수자전거보관소 수초·중등학교 수정류장 수행정동명
격자 ID1.0001.0000.7780.0000.2910.3410.0000.3540.3450.0000.0000.725
격자 X축좌표1.0001.0000.7720.0000.3070.3410.0000.2990.3560.0000.0450.727
격자 Y축좌표0.7780.7721.0000.0000.1680.2180.0000.5240.0000.2740.0000.811
주차수0.0000.0000.0001.0000.1610.1730.0000.3990.0000.0000.5100.174
도시공원 여부0.2910.3070.1680.1611.0000.6560.0000.1000.1230.0000.2810.271
어린이보호구역 여부0.3410.3410.2180.1730.6561.0000.0000.3610.0000.0000.3020.200
공중화장실 수0.0000.0000.0000.0000.0000.0001.0000.6410.1861.0000.3180.000
어린이집 수0.3540.2990.5240.3990.1000.3610.6411.0000.1620.0000.0000.461
자전거보관소 수0.3450.3560.0000.0000.1230.0000.1860.1621.0000.3700.4150.000
초·중등학교 수0.0000.0000.2740.0000.0000.0001.0000.0000.3701.0000.6480.786
정류장 수0.0000.0450.0000.5100.2810.3020.3180.0000.4150.6481.0000.000
행정동명0.7250.7270.8110.1740.2710.2000.0000.4610.0000.7860.0001.000
2023-12-12T18:32:54.430574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전통시장 수초·중등학교 수여성안심택배함 수어린이보호구역 여부도시공원 여부행정동명
전통시장 수1.000NaNNaNNaNNaNNaN
초·중등학교 수NaN1.000NaN0.0000.0000.532
여성안심택배함 수NaNNaN1.0001.0001.0001.000
어린이보호구역 여부NaN0.0001.0001.0000.4560.213
도시공원 여부NaN0.0001.0000.4561.0000.289
행정동명NaN0.5321.0000.2130.2891.000
2023-12-12T18:32:54.574084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자 ID격자 X축좌표격자 Y축좌표주차수공중화장실 수어린이집 수자전거보관소 수정류장 수도시공원 여부어린이보호구역 여부전통시장 수초·중등학교 수여성안심택배함 수행정동명
격자 ID1.0000.9990.783-0.092-0.015-0.278-0.276-0.0920.2220.260NaN0.0001.0000.478
격자 X축좌표0.9991.0000.762-0.092-0.006-0.287-0.280-0.0970.2360.261NaN0.0001.0000.481
격자 Y축좌표0.7830.7621.000-0.0580.006-0.017-0.204-0.0470.1290.168NaN0.2411.0000.591
주차수-0.092-0.092-0.0581.000-0.236-0.221-0.1220.0930.1590.171NaN0.0001.0000.093
공중화장실 수-0.015-0.0060.006-0.2361.0000.2520.1000.0750.0000.000NaN0.8941.0000.000
어린이집 수-0.278-0.287-0.017-0.2210.2521.000-0.2560.1250.0000.000NaN0.0001.0000.215
자전거보관소 수-0.276-0.280-0.204-0.1220.100-0.2561.0000.3340.0760.0000.0000.5481.0000.000
정류장 수-0.092-0.097-0.0470.0930.0750.1250.3341.0000.1980.213NaN0.3781.0000.000
도시공원 여부0.2220.2360.1290.1590.0000.0000.0760.1981.0000.456NaN0.0001.0000.289
어린이보호구역 여부0.2600.2610.1680.1710.0000.0000.0000.2130.4561.000NaN0.0001.0000.213
전통시장 수NaNNaNNaNNaNNaNNaN0.000NaNNaNNaN1.0000.0000.000NaN
초·중등학교 수0.0000.0000.2410.0000.8940.0000.5480.3780.0000.0000.0001.000NaN0.532
여성안심택배함 수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000NaN1.0001.000
행정동명0.4780.4810.5910.0930.0000.2150.0000.0000.2890.213NaN0.5321.0001.000

Missing values

2023-12-12T18:32:49.726346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:32:49.945078image/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.
2023-12-12T18:32:50.092592image/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 Y축좌표주차수도시공원 여부어린이보호구역 여부공중화장실 수전통시장 수어린이집 수자전거보관소 수초·중등학교 수여성안심택배함 수정류장 수행정동명데이터 생성일자
030635227306343522716<NA>NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
130665227306643522716<NA>NN1<NA><NA><NA><NA><NA><NA>부곡동2022-10-19
230695227306943522716<NA>NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
330725227307243522716<NA>NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
430575230305743523016<NA>NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
530605230306043523016<NA>NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
6306352303063435230162NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
7306652303066435230162NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
830695230306943523016<NA>NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
930725230307243523016<NA>NN<NA><NA><NA><NA><NA><NA><NA>부곡동2022-10-19
격자 ID격자 X축좌표격자 Y축좌표주차수도시공원 여부어린이보호구역 여부공중화장실 수전통시장 수어린이집 수자전거보관소 수초·중등학교 수여성안심택배함 수정류장 수행정동명데이터 생성일자
58631475347314743534716<NA>YN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
58731505347315043534716<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
58831535347315343534716<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
58931565347315643534716<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
59031295350312943535016<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
59131325350313243535016<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
59231475350314743535016<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
59331505350315043535016<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
59431535350315343535016<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19
59531565350315643535016<NA>NN<NA><NA><NA><NA><NA><NA><NA>청계동2022-10-19