Overview

Dataset statistics

Number of variables11
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory97.4 B

Variable types

Categorical4
Text3
Numeric4

Dataset

Description천안시시설관리공단 관내 공영주차장 현황에 대한 데이터로서 유료, 주차장명, 주소, 총면수, 장애인, 임산부, 경차, 전기차 충전, 사용제한, 위도, 경도를 담아 놓은 데이터 입니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15110970/fileData.do

Alerts

경차전용 is highly overall correlated with 임산부용 and 1 other fieldsHigh correlation
임산부용 is highly overall correlated with 경차전용High correlation
전기차충전 is highly overall correlated with 경차전용High correlation
임산부용 is highly imbalanced (75.7%)Imbalance
전기차충전 is highly imbalanced (54.8%)Imbalance
사용제한 is highly imbalanced (77.6%)Imbalance
주차장명 has unique valuesUnique
주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
장애인전용 has 15 (27.3%) zerosZeros
경차전용 has 42 (76.4%) zerosZeros

Reproduction

Analysis started2024-04-17 11:01:04.172460
Analysis finished2024-04-17 11:01:05.985898
Duration1.81 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size572.0 B
무료
44 
유료
11 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무료 44
80.0%
유료 11
 
20.0%

Length

2024-04-17T20:01:06.035023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:01:06.123264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무료 44
80.0%
유료 11
 
20.0%

주차장명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-04-17T20:01:06.280062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length10.163636
Min length5

Characters and Unicode

Total characters559
Distinct characters66
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row불당 제1공영주차장
2nd row불당 제5공영주차장
3rd row불당 제2공영주차장
4th row쌍용 제1공영주차장
5th row두정 제1공영주차장
ValueCountFrequency (%)
제1공영주차장 20
18.3%
제2공영주차장 10
 
9.2%
쌍용 7
 
6.4%
제1노상주차장 7
 
6.4%
불당 5
 
4.6%
신부 4
 
3.7%
제3공영주차장 4
 
3.7%
제4공영주차장 3
 
2.8%
성정 3
 
2.8%
제5공영주차장 2
 
1.8%
Other values (35) 44
40.4%
2024-04-17T20:01:06.567200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 56
 
10.0%
차 55
 
9.8%
54
 
9.7%
주 54
 
9.7%
제 51
 
9.1%
공 42
 
7.5%
영 42
 
7.5%
1 27
 
4.8%
성 12
 
2.1%
2 12
 
2.1%
Other values (56) 154
27.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 454
81.2%
Space Separator 54
 
9.7%
Decimal Number 51
 
9.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 56
12.3%
차 55
12.1%
주 54
11.9%
제 51
11.2%
공 42
 
9.3%
영 42
 
9.3%
성 12
 
2.6%
로 10
 
2.2%
노 10
 
2.2%
용 10
 
2.2%
Other values (48) 112
24.7%
Decimal Number
ValueCountFrequency (%)
1 27
52.9%
2 12
23.5%
3 5
 
9.8%
4 3
 
5.9%
5 2
 
3.9%
7 1
 
2.0%
6 1
 
2.0%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 454
81.2%
Common 105
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 56
12.3%
차 55
12.1%
주 54
11.9%
제 51
11.2%
공 42
 
9.3%
영 42
 
9.3%
성 12
 
2.6%
로 10
 
2.2%
노 10
 
2.2%
용 10
 
2.2%
Other values (48) 112
24.7%
Common
ValueCountFrequency (%)
54
51.4%
1 27
25.7%
2 12
 
11.4%
3 5
 
4.8%
4 3
 
2.9%
5 2
 
1.9%
7 1
 
1.0%
6 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 454
81.2%
ASCII 105
 
18.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 56
12.3%
차 55
12.1%
주 54
11.9%
제 51
11.2%
공 42
 
9.3%
영 42
 
9.3%
성 12
 
2.6%
로 10
 
2.2%
노 10
 
2.2%
용 10
 
2.2%
Other values (48) 112
24.7%
ASCII
ValueCountFrequency (%)
54
51.4%
1 27
25.7%
2 12
 
11.4%
3 5
 
4.8%
4 3
 
2.9%
5 2
 
1.9%
7 1
 
1.0%
6 1
 
1.0%

주소
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-04-17T20:01:06.774446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length17.727273
Min length14

Characters and Unicode

Total characters975
Distinct characters71
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row충청남도 천안시 서북구 검은들 1길 15
2nd row충청남도 천안시 서북구 불당동 1543
3rd row충청남도 천안시 서북구 불당동 735
4th row충청남도 천안시 서북구 미라3길 16
5th row천안시 서북구 두정동 1366
ValueCountFrequency (%)
천안시 55
23.2%
동남구 29
 
12.2%
서북구 25
 
10.5%
충청남도 11
 
4.6%
쌍용동 7
 
3.0%
성정동 5
 
2.1%
불당동 4
 
1.7%
원성동 3
 
1.3%
대흥로 2
 
0.8%
다가동 2
 
0.8%
Other values (84) 94
39.7%
2024-04-17T20:01:07.083799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
182
18.7%
동 65
 
6.7%
천 60
 
6.2%
안 57
 
5.8%
구 56
 
5.7%
시 55
 
5.6%
1 43
 
4.4%
남 40
 
4.1%
서 27
 
2.8%
북 27
 
2.8%
Other values (61) 363
37.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 561
57.5%
Decimal Number 202
 
20.7%
Space Separator 182
 
18.7%
Dash Punctuation 24
 
2.5%
Other Punctuation 6
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 65
11.6%
천 60
10.7%
안 57
 
10.2%
구 56
 
10.0%
시 55
 
9.8%
남 40
 
7.1%
서 27
 
4.8%
북 27
 
4.8%
성 15
 
2.7%
청 12
 
2.1%
Other values (47) 147
26.2%
Decimal Number
ValueCountFrequency (%)
1 43
21.3%
5 25
12.4%
3 22
10.9%
2 22
10.9%
4 20
9.9%
0 20
9.9%
6 17
 
8.4%
7 15
 
7.4%
9 13
 
6.4%
8 5
 
2.5%
Other Punctuation
ValueCountFrequency (%)
· 4
66.7%
, 2
33.3%
Space Separator
ValueCountFrequency (%)
182
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 561
57.5%
Common 414
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 65
11.6%
천 60
10.7%
안 57
 
10.2%
구 56
 
10.0%
시 55
 
9.8%
남 40
 
7.1%
서 27
 
4.8%
북 27
 
4.8%
성 15
 
2.7%
청 12
 
2.1%
Other values (47) 147
26.2%
Common
ValueCountFrequency (%)
182
44.0%
1 43
 
10.4%
5 25
 
6.0%
- 24
 
5.8%
3 22
 
5.3%
2 22
 
5.3%
4 20
 
4.8%
0 20
 
4.8%
6 17
 
4.1%
7 15
 
3.6%
Other values (4) 24
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 561
57.5%
ASCII 410
42.1%
None 4
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
182
44.4%
1 43
 
10.5%
5 25
 
6.1%
- 24
 
5.9%
3 22
 
5.4%
2 22
 
5.4%
4 20
 
4.9%
0 20
 
4.9%
6 17
 
4.1%
7 15
 
3.7%
Other values (3) 20
 
4.9%
Hangul
ValueCountFrequency (%)
동 65
11.6%
천 60
10.7%
안 57
 
10.2%
구 56
 
10.0%
시 55
 
9.8%
남 40
 
7.1%
서 27
 
4.8%
북 27
 
4.8%
성 15
 
2.7%
청 12
 
2.1%
Other values (47) 147
26.2%
None
ValueCountFrequency (%)
· 4
100.0%

총면수
Text

Distinct42
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-04-17T20:01:07.246825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.2363636
Min length2

Characters and Unicode

Total characters123
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)60.0%

Sample

1st row144
2nd row130
3rd row49
4th row111
5th row18
ValueCountFrequency (%)
13 4
 
7.3%
18 3
 
5.5%
25 3
 
5.5%
27 2
 
3.6%
49 2
 
3.6%
12 2
 
3.6%
19 2
 
3.6%
28 2
 
3.6%
57 2
 
3.6%
45 1
 
1.8%
Other values (32) 32
58.2%
2024-04-17T20:01:07.507708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 27
22.0%
2 18
14.6%
4 18
14.6%
5 13
10.6%
3 11
8.9%
8 9
 
7.3%
7 9
 
7.3%
9 6
 
4.9%
0 6
 
4.9%
6 5
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122
99.2%
Other Punctuation 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27
22.1%
2 18
14.8%
4 18
14.8%
5 13
10.7%
3 11
9.0%
8 9
 
7.4%
7 9
 
7.4%
9 6
 
4.9%
0 6
 
4.9%
6 5
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 27
22.0%
2 18
14.6%
4 18
14.6%
5 13
10.6%
3 11
8.9%
8 9
 
7.3%
7 9
 
7.3%
9 6
 
4.9%
0 6
 
4.9%
6 5
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 27
22.0%
2 18
14.6%
4 18
14.6%
5 13
10.6%
3 11
8.9%
8 9
 
7.3%
7 9
 
7.3%
9 6
 
4.9%
0 6
 
4.9%
6 5
 
4.1%

장애인전용
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2363636
Minimum0
Maximum63
Zeros15
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-04-17T20:01:07.598607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile10.3
Maximum63
Range63
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.7558734
Coefficient of variation (CV)2.7054665
Kurtosis41.867763
Mean3.2363636
Median Absolute Deviation (MAD)1
Skewness6.1759324
Sum178
Variance76.66532
MonotonicityNot monotonic
2024-04-17T20:01:07.680106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 18
32.7%
0 15
27.3%
2 9
16.4%
4 3
 
5.5%
3 2
 
3.6%
5 2
 
3.6%
8 1
 
1.8%
11 1
 
1.8%
10 1
 
1.8%
6 1
 
1.8%
Other values (2) 2
 
3.6%
ValueCountFrequency (%)
0 15
27.3%
1 18
32.7%
2 9
16.4%
3 2
 
3.6%
4 3
 
5.5%
5 2
 
3.6%
6 1
 
1.8%
8 1
 
1.8%
10 1
 
1.8%
11 1
 
1.8%
ValueCountFrequency (%)
63 1
 
1.8%
16 1
 
1.8%
11 1
 
1.8%
10 1
 
1.8%
8 1
 
1.8%
6 1
 
1.8%
5 2
 
3.6%
4 3
 
5.5%
3 2
 
3.6%
2 9
16.4%

임산부용
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size572.0 B
0
51 
2
 
2
1
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)3.6%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 51
92.7%
2 2
 
3.6%
1 1
 
1.8%
3 1
 
1.8%

Length

2024-04-17T20:01:07.769290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:01:07.848874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51
92.7%
2 2
 
3.6%
1 1
 
1.8%
3 1
 
1.8%

경차전용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5636364
Minimum0
Maximum25
Zeros42
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-04-17T20:01:07.931177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7.9
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.508279
Coefficient of variation (CV)2.8832017
Kurtosis16.909726
Mean1.5636364
Median Absolute Deviation (MAD)0
Skewness3.9679532
Sum86
Variance20.324579
MonotonicityNot monotonic
2024-04-17T20:01:08.022520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 42
76.4%
1 3
 
5.5%
2 2
 
3.6%
4 1
 
1.8%
10 1
 
1.8%
7 1
 
1.8%
3 1
 
1.8%
25 1
 
1.8%
19 1
 
1.8%
6 1
 
1.8%
ValueCountFrequency (%)
0 42
76.4%
1 3
 
5.5%
2 2
 
3.6%
3 1
 
1.8%
4 1
 
1.8%
5 1
 
1.8%
6 1
 
1.8%
7 1
 
1.8%
10 1
 
1.8%
19 1
 
1.8%
ValueCountFrequency (%)
25 1
 
1.8%
19 1
 
1.8%
10 1
 
1.8%
7 1
 
1.8%
6 1
 
1.8%
5 1
 
1.8%
4 1
 
1.8%
3 1
 
1.8%
2 2
3.6%
1 3
5.5%

전기차충전
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
0
47 
2
6 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 47
85.5%
2 6
 
10.9%
1 2
 
3.6%

Length

2024-04-17T20:01:08.111017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:01:08.183241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 47
85.5%
2 6
 
10.9%
1 2
 
3.6%

사용제한
Categorical

IMBALANCE 

Distinct3
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
0
52 
1
 
2
8
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row8
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 52
94.5%
1 2
 
3.6%
8 1
 
1.8%

Length

2024-04-17T20:01:08.261204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:01:08.333285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 52
94.5%
1 2
 
3.6%
8 1
 
1.8%

위도
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.804476
Minimum36.672505
Maximum36.916227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-04-17T20:01:08.421716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.672505
5-th percentile36.761279
Q136.797242
median36.805893
Q336.816139
95-th percentile36.848792
Maximum36.916227
Range0.243722
Interquartile range (IQR)0.0188963

Descriptive statistics

Standard deviation0.036182034
Coefficient of variation (CV)0.00098308788
Kurtosis6.5321431
Mean36.804476
Median Absolute Deviation (MAD)0.0093667
Skewness-1.0387142
Sum2024.2462
Variance0.0013091396
MonotonicityNot monotonic
2024-04-17T20:01:08.531019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.8111909 1
 
1.8%
36.8083197 1
 
1.8%
36.916227 1
 
1.8%
36.773007 1
 
1.8%
36.814005 1
 
1.8%
36.820381 1
 
1.8%
36.797959 1
 
1.8%
36.800345 1
 
1.8%
36.787632 1
 
1.8%
36.807032 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
36.672505 1
1.8%
36.674485 1
1.8%
36.759512 1
1.8%
36.762036 1
1.8%
36.773007 1
1.8%
36.777899 1
1.8%
36.778608 1
1.8%
36.7794 1
1.8%
36.785089 1
1.8%
36.787632 1
1.8%
ValueCountFrequency (%)
36.916227 1
1.8%
36.873908 1
1.8%
36.872552 1
1.8%
36.838609 1
1.8%
36.837569 1
1.8%
36.8319609 1
1.8%
36.830905 1
1.8%
36.825202 1
1.8%
36.824614 1
1.8%
36.8222344 1
1.8%

경도
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.14846
Minimum127.04596
Maximum127.29849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-04-17T20:01:08.637283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.04596
5-th percentile127.10755
Q1127.12257
median127.14242
Q3127.15769
95-th percentile127.24756
Maximum127.29849
Range0.252524
Interquartile range (IQR)0.03512165

Descriptive statistics

Standard deviation0.045388164
Coefficient of variation (CV)0.00035696983
Kurtosis3.082107
Mean127.14846
Median Absolute Deviation (MAD)0.017733
Skewness1.2393423
Sum6993.1654
Variance0.0020600855
MonotonicityNot monotonic
2024-04-17T20:01:08.752005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1095158 1
 
1.8%
127.106278 1
 
1.8%
127.131052 1
 
1.8%
127.132113 1
 
1.8%
127.160149 1
 
1.8%
127.153829 1
 
1.8%
127.122546 1
 
1.8%
127.125688 1
 
1.8%
127.114287 1
 
1.8%
127.117955 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
127.045962 1
1.8%
127.053144 1
1.8%
127.106278 1
1.8%
127.108095 1
1.8%
127.108532 1
1.8%
127.1095158 1
1.8%
127.1107093 1
1.8%
127.114287 1
1.8%
127.115092 1
1.8%
127.116717 1
1.8%
ValueCountFrequency (%)
127.298486 1
1.8%
127.271976 1
1.8%
127.268718 1
1.8%
127.238488 1
1.8%
127.227601 1
1.8%
127.201433 1
1.8%
127.201385 1
1.8%
127.173361 1
1.8%
127.1647 1
1.8%
127.162437 1
1.8%

Interactions

2024-04-17T20:01:05.548127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:04.536481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:04.821209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:05.307436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:05.608945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:04.598731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:04.882467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:05.370383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:05.670397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:04.671666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:04.942388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:05.432531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:05.728642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:04.746152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:05.018177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:01:05.488716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T20:01:08.832047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분주차장명주소총면수장애인전용임산부용경차전용전기차충전사용제한위도경도
구분1.0001.0001.0000.0000.0000.5280.6960.0000.1530.0000.000
주차장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
총면수0.0001.0001.0001.0001.0000.5800.9630.9910.9080.9400.838
장애인전용0.0001.0001.0001.0001.0000.7790.5310.2160.0000.3320.113
임산부용0.5281.0001.0000.5800.7791.0000.9070.0000.0000.0000.000
경차전용0.6961.0001.0000.9630.5310.9071.0000.8960.8160.1460.604
전기차충전0.0001.0001.0000.9910.2160.0000.8961.0000.6240.5170.812
사용제한0.1531.0001.0000.9080.0000.0000.8160.6241.0000.0000.000
위도0.0001.0001.0000.9400.3320.0000.1460.5170.0001.0000.844
경도0.0001.0001.0000.8380.1130.0000.6040.8120.0000.8441.000
2024-04-17T20:01:08.935228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기차충전구분사용제한임산부용
전기차충전1.0000.0000.2890.000
구분0.0001.0000.2480.351
사용제한0.2890.2481.0000.000
임산부용0.0000.3510.0001.000
2024-04-17T20:01:09.010943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장애인전용경차전용위도경도구분임산부용전기차충전사용제한
장애인전용1.0000.387-0.192-0.2890.0000.4160.2010.000
경차전용0.3871.0000.132-0.1480.4280.7360.5890.437
위도-0.1920.1321.0000.0770.0000.0000.3870.000
경도-0.289-0.1480.0771.0000.0000.0000.4870.000
구분0.0000.4280.0000.0001.0000.3510.0000.248
임산부용0.4160.7360.0000.0000.3511.0000.0000.000
전기차충전0.2010.5890.3870.4870.0000.0001.0000.289
사용제한0.0000.4370.0000.0000.2480.0000.2891.000

Missing values

2024-04-17T20:01:05.822982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T20:01:05.942022image/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

구분주차장명주소총면수장애인전용임산부용경차전용전기차충전사용제한위도경도
0유료불당 제1공영주차장충청남도 천안시 서북구 검은들 1길 151444040836.811191127.109516
1유료불당 제5공영주차장충청남도 천안시 서북구 불당동 154313082100036.80832127.106278
2유료불당 제2공영주차장충청남도 천안시 서북구 불당동 735492000036.810226127.110709
3유료쌍용 제1공영주차장충청남도 천안시 서북구 미라3길 161113071136.801024127.130965
4유료두정 제1공영주차장천안시 서북구 두정동 1366181000036.830905127.139062
5유료두정역 제1공영주차장천안시 서북구 두정동 91-1422012036.831961127.150425
6유료신부 제1공영주차장충청남도 천안시 동남구 터미널8길 7381010036.822234127.157981
7유료신부 제4공영주차장충청남도 천안시 동남구 먹거리9길 16131000036.817474127.155577
8유료대흥로 제1노상주차장충청남도 천안시 동남구 대흥로 194560000036.805323127.146933
9유료대흥 제1공영주차장천안시 동남구 대흥로 205251130036.80648127.146845
구분주차장명주소총면수장애인전용임산부용경차전용전기차충전사용제한위도경도
45무료성정로 제1노상주차장천안시 서북구 성정동 751270000036.813768127.138063
46무료성황로 제2노상주차장천안시 동남구 원성동 610500000036.811504127.162437
47무료성황로 제3노상주차장천안시 동남구 원성동 224-3220000036.812431127.161399
48무료신용로 제1노상주차장충청남도 천안시 동남구 다가동 478950000036.801306127.140675
49무료쌍용대로 제1노상주차장천안시 서북구 성정동 413-14431000036.824614127.137011
50무료중앙로 제1노상주차장천안시 동남구 영성동 129180000036.802088127.153013
51무료차돌로 제1노상주차장충청남도 천안시 동남구 다가동 471642000036.802107127.138816
52무료천안축구센터 주차장천안시 서북구 축구센터로 15035816200036.8217127.1476
53무료천안실내배드민턴장 주차장천안시 동남구 천안대로 357312000036.7794127.1647
54무료종합운동장충청남도 천안시 서북구 번영로2081,43063052036.818921127.115092