Overview

Dataset statistics

Number of variables10
Number of observations50
Missing cells10
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory91.6 B

Variable types

Text1
Numeric8
Categorical1

Dataset

Description인천광역시 차량 미운행 시 주차 장소(자가주차장, 도로변, 골목길, 거주자 우선 주차구역 등 )에 대한 항목을 제공하는 자료입니다.
Author인천광역시
URLhttps://www.data.go.kr/data/15066307/fileData.do

Alerts

차량 보유 (퍼센트) is highly overall correlated with 차량 미보유 (퍼센트)High correlation
차량 미보유 (퍼센트) is highly overall correlated with 차량 보유 (퍼센트)High correlation
자가 주차장(주택 아파트 내 주차장) (퍼센트) is highly overall correlated with 도로변 골목길(집 앞 포함) (퍼센트) and 3 other fieldsHigh correlation
도로변 골목길(집 앞 포함) (퍼센트) is highly overall correlated with 자가 주차장(주택 아파트 내 주차장) (퍼센트) and 2 other fieldsHigh correlation
공영 주차장 (퍼센트) is highly overall correlated with 자가 주차장(주택 아파트 내 주차장) (퍼센트)High correlation
거주자 우선 주차구역 (퍼센트) is highly overall correlated with 자가 주차장(주택 아파트 내 주차장) (퍼센트) and 1 other fieldsHigh correlation
유휴지(공터) (퍼센트) is highly overall correlated with 자가 주차장(주택 아파트 내 주차장) (퍼센트) and 1 other fieldsHigh correlation
사설 유료 주차장 (퍼센트) is highly overall correlated with 기타 (퍼센트)High correlation
기타 (퍼센트) is highly overall correlated with 사설 유료 주차장 (퍼센트)High correlation
공영 주차장 (퍼센트) has 1 (2.0%) missing valuesMissing
거주자 우선 주차구역 (퍼센트) has 2 (4.0%) missing valuesMissing
유휴지(공터) (퍼센트) has 2 (4.0%) missing valuesMissing
사설 유료 주차장 (퍼센트) has 5 (10.0%) missing valuesMissing
특성별 has unique valuesUnique
유휴지(공터) (퍼센트) has 1 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:48:04.964365
Analysis finished2023-12-12 06:48:12.480200
Duration7.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

특성별
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-12T15:48:12.632192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length7.5
Mean length4.72
Min length2

Characters and Unicode

Total characters236
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row중구
2nd row동구
3rd row미추홀구
4th row연수구
5th row남동구
ValueCountFrequency (%)
미만 7
 
11.3%
이상 3
 
4.8%
기타 2
 
3.2%
중구 1
 
1.6%
학생 1
 
1.6%
주부 1
 
1.6%
무직/기타 1
 
1.6%
100만원 1
 
1.6%
100~200만원 1
 
1.6%
200~300만원 1
 
1.6%
Other values (43) 43
69.4%
2023-12-12T15:48:13.064213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
 
14.0%
만 15
 
6.4%
12
 
5.1%
~ 11
 
4.7%
세 9
 
3.8%
원 8
 
3.4%
구 8
 
3.4%
미 8
 
3.4%
3 6
 
2.5%
2 5
 
2.1%
Other values (66) 121
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
60.2%
Decimal Number 69
29.2%
Space Separator 12
 
5.1%
Math Symbol 11
 
4.7%
Other Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
기 4
 
2.8%
졸 4
 
2.8%
상 4
 
2.8%
Other values (54) 72
50.7%
Decimal Number
ValueCountFrequency (%)
0 33
47.8%
3 6
 
8.7%
2 5
 
7.2%
9 5
 
7.2%
5 5
 
7.2%
4 5
 
7.2%
1 5
 
7.2%
6 3
 
4.3%
7 2
 
2.9%
Space Separator
ValueCountFrequency (%)
12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
60.2%
Common 94
39.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
기 4
 
2.8%
졸 4
 
2.8%
상 4
 
2.8%
Other values (54) 72
50.7%
Common
ValueCountFrequency (%)
0 33
35.1%
12
 
12.8%
~ 11
 
11.7%
3 6
 
6.4%
2 5
 
5.3%
9 5
 
5.3%
5 5
 
5.3%
4 5
 
5.3%
1 5
 
5.3%
6 3
 
3.2%
Other values (2) 4
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142
60.2%
ASCII 94
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
35.1%
12
 
12.8%
~ 11
 
11.7%
3 6
 
6.4%
2 5
 
5.3%
9 5
 
5.3%
5 5
 
5.3%
4 5
 
5.3%
1 5
 
5.3%
6 3
 
3.2%
Other values (2) 4
 
4.3%
Hangul
ValueCountFrequency (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
기 4
 
2.8%
졸 4
 
2.8%
상 4
 
2.8%
Other values (54) 72
50.7%

차량 보유 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.57
Minimum5
Maximum78.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:48:13.219345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13.775
Q139.025
median50
Q360.575
95-th percentile68.775
Maximum78.5
Range73.5
Interquartile range (IQR)21.55

Descriptive statistics

Standard deviation16.90986
Coefficient of variation (CV)0.3554732
Kurtosis0.24023068
Mean47.57
Median Absolute Deviation (MAD)10.95
Skewness-0.77447848
Sum2378.5
Variance285.94337
MonotonicityNot monotonic
2023-12-12T15:48:13.379571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
40.6 2
 
4.0%
41.8 2
 
4.0%
40.0 1
 
2.0%
29.0 1
 
2.0%
21.8 1
 
2.0%
13.1 1
 
2.0%
33.7 1
 
2.0%
50.1 1
 
2.0%
53.4 1
 
2.0%
57.6 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
5.0 1
2.0%
5.8 1
2.0%
13.1 1
2.0%
14.6 1
2.0%
21.8 1
2.0%
25.3 1
2.0%
29.0 1
2.0%
30.9 1
2.0%
32.8 1
2.0%
33.5 1
2.0%
ValueCountFrequency (%)
78.5 1
2.0%
70.2 1
2.0%
69.0 1
2.0%
68.5 1
2.0%
65.2 1
2.0%
64.8 1
2.0%
64.2 1
2.0%
63.9 1
2.0%
63.0 1
2.0%
62.5 1
2.0%

차량 미보유 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.43
Minimum21.5
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:48:13.518544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.5
5-th percentile31.225
Q139.425
median50
Q360.975
95-th percentile86.225
Maximum95
Range73.5
Interquartile range (IQR)21.55

Descriptive statistics

Standard deviation16.90986
Coefficient of variation (CV)0.3225226
Kurtosis0.24023068
Mean52.43
Median Absolute Deviation (MAD)10.95
Skewness0.77447848
Sum2621.5
Variance285.94337
MonotonicityNot monotonic
2023-12-12T15:48:13.667177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
59.4 2
 
4.0%
58.2 2
 
4.0%
60.0 1
 
2.0%
71.0 1
 
2.0%
78.2 1
 
2.0%
86.9 1
 
2.0%
66.3 1
 
2.0%
49.9 1
 
2.0%
46.6 1
 
2.0%
42.4 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
21.5 1
2.0%
29.8 1
2.0%
31.0 1
2.0%
31.5 1
2.0%
34.8 1
2.0%
35.2 1
2.0%
35.8 1
2.0%
36.1 1
2.0%
37.0 1
2.0%
37.5 1
2.0%
ValueCountFrequency (%)
95.0 1
2.0%
94.2 1
2.0%
86.9 1
2.0%
85.4 1
2.0%
78.2 1
2.0%
74.7 1
2.0%
71.0 1
2.0%
69.1 1
2.0%
67.2 1
2.0%
66.5 1
2.0%

자가 주차장(주택 아파트 내 주차장) (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.258
Minimum45.4
Maximum96.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:48:13.818050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45.4
5-th percentile64.78
Q176
median81.3
Q386.15
95-th percentile91.43
Maximum96.6
Range51.2
Interquartile range (IQR)10.15

Descriptive statistics

Standard deviation9.1337475
Coefficient of variation (CV)0.11380482
Kurtosis3.3702821
Mean80.258
Median Absolute Deviation (MAD)5.3
Skewness-1.3385194
Sum4012.9
Variance83.425343
MonotonicityNot monotonic
2023-12-12T15:48:14.007259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
76.0 2
 
4.0%
86.0 2
 
4.0%
81.3 2
 
4.0%
85.0 1
 
2.0%
45.4 1
 
2.0%
70.0 1
 
2.0%
76.2 1
 
2.0%
76.4 1
 
2.0%
81.2 1
 
2.0%
84.2 1
 
2.0%
Other values (37) 37
74.0%
ValueCountFrequency (%)
45.4 1
2.0%
60.1 1
2.0%
61.9 1
2.0%
68.3 1
2.0%
70.0 1
2.0%
71.4 1
2.0%
71.6 1
2.0%
72.6 1
2.0%
73.3 1
2.0%
74.0 1
2.0%
ValueCountFrequency (%)
96.6 1
2.0%
92.8 1
2.0%
92.6 1
2.0%
90.0 1
2.0%
89.7 1
2.0%
89.5 1
2.0%
89.1 1
2.0%
88.9 1
2.0%
87.5 1
2.0%
87.4 1
2.0%

도로변 골목길(집 앞 포함) (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.062
Minimum0.8
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:48:14.146195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile4.625
Q19.35
median13.2
Q317.8
95-th percentile24.42
Maximum44
Range43.2
Interquartile range (IQR)8.45

Descriptive statistics

Standard deviation7.3958038
Coefficient of variation (CV)0.52594253
Kurtosis4.4962578
Mean14.062
Median Absolute Deviation (MAD)4.25
Skewness1.4751938
Sum703.1
Variance54.697914
MonotonicityNot monotonic
2023-12-12T15:48:14.320825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
10.0 2
 
4.0%
10.5 2
 
4.0%
10.7 2
 
4.0%
7.3 2
 
4.0%
0.8 1
 
2.0%
17.9 1
 
2.0%
17.3 1
 
2.0%
14.4 1
 
2.0%
11.6 1
 
2.0%
6.8 1
 
2.0%
Other values (36) 36
72.0%
ValueCountFrequency (%)
0.8 1
2.0%
3.7 1
2.0%
4.4 1
2.0%
4.9 1
2.0%
6.5 1
2.0%
6.8 1
2.0%
7.1 1
2.0%
7.3 2
4.0%
8.4 1
2.0%
8.8 1
2.0%
ValueCountFrequency (%)
44.0 1
2.0%
31.0 1
2.0%
25.5 1
2.0%
23.1 1
2.0%
22.1 1
2.0%
20.8 1
2.0%
20.5 1
2.0%
20.3 1
2.0%
19.4 1
2.0%
19.3 1
2.0%

공영 주차장 (퍼센트)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)63.3%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean2.8387755
Minimum0.9
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:48:14.526936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.18
Q11.9
median2.3
Q33.2
95-th percentile6.2
Maximum11
Range10.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.8591907
Coefficient of variation (CV)0.654927
Kurtosis8.5650447
Mean2.8387755
Median Absolute Deviation (MAD)0.5
Skewness2.6745541
Sum139.1
Variance3.4565901
MonotonicityNot monotonic
2023-12-12T15:48:14.679516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1.9 5
 
10.0%
2.3 4
 
8.0%
2.6 2
 
4.0%
1.6 2
 
4.0%
2.2 2
 
4.0%
1.7 2
 
4.0%
2.1 2
 
4.0%
3.2 2
 
4.0%
2.8 2
 
4.0%
2.0 2
 
4.0%
Other values (21) 24
48.0%
ValueCountFrequency (%)
0.9 1
 
2.0%
1.0 1
 
2.0%
1.1 1
 
2.0%
1.3 1
 
2.0%
1.4 1
 
2.0%
1.5 1
 
2.0%
1.6 2
 
4.0%
1.7 2
 
4.0%
1.8 1
 
2.0%
1.9 5
10.0%
ValueCountFrequency (%)
11.0 1
2.0%
8.6 1
2.0%
7.0 1
2.0%
5.0 1
2.0%
4.7 2
4.0%
4.3 1
2.0%
3.7 1
2.0%
3.6 1
2.0%
3.5 1
2.0%
3.3 1
2.0%

거주자 우선 주차구역 (퍼센트)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)45.8%
Missing2
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1.6791667
Minimum0.4
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:48:14.803775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.8
Q11.2
median1.55
Q32.15
95-th percentile2.9
Maximum3
Range2.6
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.69587182
Coefficient of variation (CV)0.41441498
Kurtosis-0.77832007
Mean1.6791667
Median Absolute Deviation (MAD)0.5
Skewness0.34293253
Sum80.6
Variance0.48423759
MonotonicityNot monotonic
2023-12-12T15:48:14.956709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1.3 4
 
8.0%
1.2 4
 
8.0%
1.6 4
 
8.0%
0.9 3
 
6.0%
0.8 3
 
6.0%
2.1 3
 
6.0%
2.5 3
 
6.0%
1.5 3
 
6.0%
1.4 3
 
6.0%
3.0 2
 
4.0%
Other values (12) 16
32.0%
ValueCountFrequency (%)
0.4 1
 
2.0%
0.5 1
 
2.0%
0.8 3
6.0%
0.9 3
6.0%
1.0 1
 
2.0%
1.1 1
 
2.0%
1.2 4
8.0%
1.3 4
8.0%
1.4 3
6.0%
1.5 3
6.0%
ValueCountFrequency (%)
3.0 2
4.0%
2.9 2
4.0%
2.8 1
 
2.0%
2.7 1
 
2.0%
2.5 3
6.0%
2.4 2
4.0%
2.3 1
 
2.0%
2.1 3
6.0%
2.0 1
 
2.0%
1.9 2
4.0%

유휴지(공터) (퍼센트)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)29.2%
Missing2
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean0.62083333
Minimum0
Maximum2.8
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:48:15.103334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.4
median0.5
Q30.7
95-th percentile1.555
Maximum2.8
Range2.8
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.50400875
Coefficient of variation (CV)0.81182618
Kurtosis9.126
Mean0.62083333
Median Absolute Deviation (MAD)0.2
Skewness2.7824092
Sum29.8
Variance0.25402482
MonotonicityNot monotonic
2023-12-12T15:48:15.220769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.4 13
26.0%
0.5 7
14.0%
0.7 6
12.0%
0.9 4
 
8.0%
0.3 4
 
8.0%
0.6 3
 
6.0%
0.2 3
 
6.0%
0.8 2
 
4.0%
0.1 1
 
2.0%
1.1 1
 
2.0%
Other values (4) 4
 
8.0%
(Missing) 2
 
4.0%
ValueCountFrequency (%)
0.0 1
 
2.0%
0.1 1
 
2.0%
0.2 3
 
6.0%
0.3 4
 
8.0%
0.4 13
26.0%
0.5 7
14.0%
0.6 3
 
6.0%
0.7 6
12.0%
0.8 2
 
4.0%
0.9 4
 
8.0%
ValueCountFrequency (%)
2.8 1
 
2.0%
2.3 1
 
2.0%
1.8 1
 
2.0%
1.1 1
 
2.0%
0.9 4
 
8.0%
0.8 2
 
4.0%
0.7 6
12.0%
0.6 3
 
6.0%
0.5 7
14.0%
0.4 13
26.0%

사설 유료 주차장 (퍼센트)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)24.4%
Missing5
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean0.72666667
Minimum0.2
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T15:48:15.351165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.4
median0.5
Q30.6
95-th percentile0.98
Maximum8.5
Range8.3
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation1.2368214
Coefficient of variation (CV)1.7020479
Kurtosis37.544637
Mean0.72666667
Median Absolute Deviation (MAD)0.1
Skewness5.9583912
Sum32.7
Variance1.5297273
MonotonicityNot monotonic
2023-12-12T15:48:15.462531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.4 11
22.0%
0.5 9
18.0%
0.3 7
14.0%
0.6 6
12.0%
0.9 3
 
6.0%
0.8 2
 
4.0%
0.7 2
 
4.0%
0.2 2
 
4.0%
8.5 1
 
2.0%
1.0 1
 
2.0%
(Missing) 5
10.0%
ValueCountFrequency (%)
0.2 2
 
4.0%
0.3 7
14.0%
0.4 11
22.0%
0.5 9
18.0%
0.6 6
12.0%
0.7 2
 
4.0%
0.8 2
 
4.0%
0.9 3
 
6.0%
1.0 1
 
2.0%
2.5 1
 
2.0%
ValueCountFrequency (%)
8.5 1
 
2.0%
2.5 1
 
2.0%
1.0 1
 
2.0%
0.9 3
 
6.0%
0.8 2
 
4.0%
0.7 2
 
4.0%
0.6 6
12.0%
0.5 9
18.0%
0.4 11
22.0%
0.3 7
14.0%

기타 (퍼센트)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
29 
0.1
13 
0.0
5 
0.2
3 

Length

Max length4
Median length4
Mean length3.58
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1
2nd row<NA>
3rd row0.2
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 29
58.0%
0.1 13
26.0%
0.0 5
 
10.0%
0.2 3
 
6.0%

Length

2023-12-12T15:48:15.588496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:48:15.695512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
58.0%
0.1 13
26.0%
0.0 5
 
10.0%
0.2 3
 
6.0%

Interactions

2023-12-12T15:48:11.258818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:05.318622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.137102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.946780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.772547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.550464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.356100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.457932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.362814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:05.431508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.246452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.093156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.875644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.642690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.463406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.569685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.490938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:05.529084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.358463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.278068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.000018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.755755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.588578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.681821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.587829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:05.648526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.445418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.354065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.087018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.871269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.028992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.782574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.709912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:05.735854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.528676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.442231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.166589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.959296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.123849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.857166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.799966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:05.831572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.612401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.523536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.247518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.039999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.228768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.933911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.874427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:05.919504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.722517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.600311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.361109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.150592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.308422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.052160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.949981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.044499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:06.827217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:07.683998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:08.477901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:09.248359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:10.383173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:48:11.134422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:48:15.774489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특성별차량 보유 (퍼센트)차량 미보유 (퍼센트)자가 주차장(주택 아파트 내 주차장) (퍼센트)도로변 골목길(집 앞 포함) (퍼센트)공영 주차장 (퍼센트)거주자 우선 주차구역 (퍼센트)유휴지(공터) (퍼센트)사설 유료 주차장 (퍼센트)기타 (퍼센트)
특성별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
차량 보유 (퍼센트)1.0001.0001.0000.2240.4070.7200.0000.6920.7760.342
차량 미보유 (퍼센트)1.0001.0001.0000.2240.4070.7200.0000.6920.7760.342
자가 주차장(주택 아파트 내 주차장) (퍼센트)1.0000.2240.2241.0000.9500.8700.3390.5830.9800.460
도로변 골목길(집 앞 포함) (퍼센트)1.0000.4070.4070.9501.0000.8960.0000.5940.7400.611
공영 주차장 (퍼센트)1.0000.7200.7200.8700.8961.0000.3620.7461.0000.339
거주자 우선 주차구역 (퍼센트)1.0000.0000.0000.3390.0000.3621.0000.2280.2450.000
유휴지(공터) (퍼센트)1.0000.6920.6920.5830.5940.7460.2281.0000.0000.214
사설 유료 주차장 (퍼센트)1.0000.7760.7760.9800.7401.0000.2450.0001.000NaN
기타 (퍼센트)1.0000.3420.3420.4600.6110.3390.0000.214NaN1.000
2023-12-12T15:48:16.281604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량 보유 (퍼센트)차량 미보유 (퍼센트)자가 주차장(주택 아파트 내 주차장) (퍼센트)도로변 골목길(집 앞 포함) (퍼센트)공영 주차장 (퍼센트)거주자 우선 주차구역 (퍼센트)유휴지(공터) (퍼센트)사설 유료 주차장 (퍼센트)기타 (퍼센트)
차량 보유 (퍼센트)1.000-1.0000.497-0.412-0.406-0.308-0.256-0.3010.179
차량 미보유 (퍼센트)-1.0001.000-0.4970.4120.4060.3080.2560.3010.179
자가 주차장(주택 아파트 내 주차장) (퍼센트)0.497-0.4971.000-0.976-0.505-0.719-0.591-0.3300.284
도로변 골목길(집 앞 포함) (퍼센트)-0.4120.412-0.9761.0000.3740.6520.5540.2680.433
공영 주차장 (퍼센트)-0.4060.406-0.5050.3741.0000.4810.1630.2150.237
거주자 우선 주차구역 (퍼센트)-0.3080.308-0.7190.6520.4811.0000.3350.2810.290
유휴지(공터) (퍼센트)-0.2560.256-0.5910.5540.1630.3351.0000.1700.118
사설 유료 주차장 (퍼센트)-0.3010.301-0.3300.2680.2150.2810.1701.0001.000
기타 (퍼센트)0.1790.1790.2840.4330.2370.2900.1181.0001.000

Missing values

2023-12-12T15:48:12.058911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:48:12.268732image/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.
2023-12-12T15:48:12.402719image/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

특성별차량 보유 (퍼센트)차량 미보유 (퍼센트)자가 주차장(주택 아파트 내 주차장) (퍼센트)도로변 골목길(집 앞 포함) (퍼센트)공영 주차장 (퍼센트)거주자 우선 주차구역 (퍼센트)유휴지(공터) (퍼센트)사설 유료 주차장 (퍼센트)기타 (퍼센트)
0중구53.146.985.010.03.20.90.40.40.1
1동구40.659.474.019.34.31.40.60.4<NA>
2미추홀구40.659.476.020.80.91.00.70.50.2
3연수구59.940.192.83.71.91.20.10.4<NA>
4남동구47.552.580.315.42.40.80.60.6<NA>
5부평구41.858.283.511.92.01.20.90.30.1
6계양구48.451.683.910.52.82.10.30.4<NA>
7서구57.342.782.910.72.62.50.40.8<NA>
8강화군57.542.589.74.93.51.60.4<NA><NA>
9옹진군70.229.874.620.51.42.41.1<NA><NA>
특성별차량 보유 (퍼센트)차량 미보유 (퍼센트)자가 주차장(주택 아파트 내 주차장) (퍼센트)도로변 골목길(집 앞 포함) (퍼센트)공영 주차장 (퍼센트)거주자 우선 주차구역 (퍼센트)유휴지(공터) (퍼센트)사설 유료 주차장 (퍼센트)기타 (퍼센트)
40연립/다세대주택38.761.361.931.03.72.50.40.40.0
41기타49.950.160.125.58.62.90.42.5<NA>
42자가50.849.286.29.81.71.20.50.50.0
43전세49.850.277.117.53.11.40.40.4<NA>
44월세 및 기타41.858.273.318.04.73.00.50.40.1
451인36.064.071.419.44.73.00.90.6<NA>
462인47.252.881.314.12.31.60.50.3<NA>
473인52.747.382.812.61.91.60.40.60.1
484인54.745.387.58.41.91.10.40.60.1
495인 이상47.852.287.19.12.00.80.80.2<NA>