Overview

Dataset statistics

Number of variables17
Number of observations95
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory150.4 B

Variable types

Categorical11
Text1
Numeric5

Dataset

Description경상남도 김해시 전기차 등록현황에 대한 데이터로 승용차, 승합차, 화물차, 특수차의 등록대수를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15114490/fileData.do

Alerts

기준 has constant value ""Constant
시군구 has constant value ""Constant
연료 has constant value ""Constant
(관용)특수 has constant value ""Constant
(자가용)특수 has constant value ""Constant
(영업용)특수 has constant value ""Constant
계 is highly overall correlated with (자가용)승용 and 4 other fieldsHigh correlation
(자가용)승용 is highly overall correlated with 계 and 3 other fieldsHigh correlation
(자가용)화물 is highly overall correlated with 계 and 3 other fieldsHigh correlation
(영업용)승용 is highly overall correlated with 계 and 4 other fieldsHigh correlation
(영업용)화물 is highly overall correlated with 계 and 3 other fieldsHigh correlation
(관용)승용 is highly overall correlated with (관용)화물High correlation
(관용)화물 is highly overall correlated with (관용)승용High correlation
(영업용)승합 is highly overall correlated with 계 and 1 other fieldsHigh correlation
(관용)승용 is highly imbalanced (87.4%)Imbalance
(관용)승합 is highly imbalanced (91.6%)Imbalance
(관용)화물 is highly imbalanced (89.4%)Imbalance
(자가용)승합 is highly imbalanced (91.6%)Imbalance
(영업용)승합 is highly imbalanced (89.4%)Imbalance
읍면동 has unique valuesUnique
(자가용)승용 has 6 (6.3%) zerosZeros
(자가용)화물 has 16 (16.8%) zerosZeros
(영업용)승용 has 57 (60.0%) zerosZeros
(영업용)화물 has 58 (61.1%) zerosZeros

Reproduction

Analysis started2023-12-12 13:43:01.018107
Analysis finished2023-12-12 13:43:04.334295
Duration3.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
2023년5월
95 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023년5월
2nd row2023년5월
3rd row2023년5월
4th row2023년5월
5th row2023년5월

Common Values

ValueCountFrequency (%)
2023년5월 95
100.0%

Length

2023-12-12T22:43:04.399093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:04.484028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023년5월 95
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
경상남도 김해시
95 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도 김해시
2nd row경상남도 김해시
3rd row경상남도 김해시
4th row경상남도 김해시
5th row경상남도 김해시

Common Values

ValueCountFrequency (%)
경상남도 김해시 95
100.0%

Length

2023-12-12T22:43:04.583990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:04.680726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 95
50.0%
김해시 95
50.0%

읍면동
Text

UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size892.0 B
2023-12-12T22:43:04.953016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.6736842
Min length3

Characters and Unicode

Total characters539
Distinct characters96
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)100.0%

Sample

1st row동상동
2nd row서상동
3rd row부원동
4th row봉황동
5th row대성동
ValueCountFrequency (%)
한림면 11
 
7.3%
진영읍 10
 
6.7%
생림면 9
 
6.0%
대동면 9
 
6.0%
진례면 9
 
6.0%
주촌면 8
 
5.3%
상동면 6
 
4.0%
대감리 2
 
1.3%
초전리 1
 
0.7%
신안리 1
 
0.7%
Other values (84) 84
56.0%
2023-12-12T22:43:05.408906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95
17.6%
리 55
 
10.2%
동 54
 
10.0%
면 52
 
9.6%
림 22
 
4.1%
진 20
 
3.7%
대 13
 
2.4%
생 11
 
2.0%
영 11
 
2.0%
한 11
 
2.0%
Other values (86) 195
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 444
82.4%
Space Separator 95
 
17.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 55
 
12.4%
동 54
 
12.2%
면 52
 
11.7%
림 22
 
5.0%
진 20
 
4.5%
대 13
 
2.9%
생 11
 
2.5%
영 11
 
2.5%
한 11
 
2.5%
읍 10
 
2.3%
Other values (85) 185
41.7%
Space Separator
ValueCountFrequency (%)
95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 444
82.4%
Common 95
 
17.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 55
 
12.4%
동 54
 
12.2%
면 52
 
11.7%
림 22
 
5.0%
진 20
 
4.5%
대 13
 
2.9%
생 11
 
2.5%
영 11
 
2.5%
한 11
 
2.5%
읍 10
 
2.3%
Other values (85) 185
41.7%
Common
ValueCountFrequency (%)
95
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 444
82.4%
ASCII 95
 
17.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95
100.0%
Hangul
ValueCountFrequency (%)
리 55
 
12.4%
동 54
 
12.2%
면 52
 
11.7%
림 22
 
5.0%
진 20
 
4.5%
대 13
 
2.9%
생 11
 
2.5%
영 11
 
2.5%
한 11
 
2.5%
읍 10
 
2.3%
Other values (85) 185
41.7%

연료
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
전기
95 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전기 95
100.0%

Length

2023-12-12T22:43:05.567556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:05.680043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전기 95
100.0%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.136842
Minimum1
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T22:43:05.802006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q13
median7
Q344
95-th percentile115
Maximum219
Range218
Interquartile range (IQR)41

Descriptive statistics

Standard deviation41.293864
Coefficient of variation (CV)1.4676083
Kurtosis4.9618341
Mean28.136842
Median Absolute Deviation (MAD)5
Skewness2.1330054
Sum2673
Variance1705.1832
MonotonicityNot monotonic
2023-12-12T22:43:05.951545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3 15
15.8%
2 10
 
10.5%
5 8
 
8.4%
1 5
 
5.3%
6 5
 
5.3%
4 4
 
4.2%
10 3
 
3.2%
8 3
 
3.2%
44 3
 
3.2%
17 2
 
2.1%
Other values (34) 37
38.9%
ValueCountFrequency (%)
1 5
 
5.3%
2 10
10.5%
3 15
15.8%
4 4
 
4.2%
5 8
8.4%
6 5
 
5.3%
7 2
 
2.1%
8 3
 
3.2%
9 1
 
1.1%
10 3
 
3.2%
ValueCountFrequency (%)
219 1
1.1%
143 1
1.1%
140 1
1.1%
131 1
1.1%
122 1
1.1%
112 1
1.1%
110 1
1.1%
101 2
2.1%
97 1
1.1%
85 1
1.1%

(관용)승용
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size892.0 B
0
92 
33
 
1
3
 
1
2
 
1

Length

Max length2
Median length1
Mean length1.0105263
Min length1

Unique

Unique3 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row33
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 92
96.8%
33 1
 
1.1%
3 1
 
1.1%
2 1
 
1.1%

Length

2023-12-12T22:43:06.076724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:06.187732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 92
96.8%
33 1
 
1.1%
3 1
 
1.1%
2 1
 
1.1%

(관용)승합
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
0
94 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 94
98.9%
1 1
 
1.1%

Length

2023-12-12T22:43:06.279560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:06.367521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 94
98.9%
1 1
 
1.1%

(관용)화물
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size892.0 B
0
93 
8
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 93
97.9%
8 1
 
1.1%
1 1
 
1.1%

Length

2023-12-12T22:43:06.460703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:06.547888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 93
97.9%
8 1
 
1.1%
1 1
 
1.1%

(관용)특수
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
0
95 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 95
100.0%

Length

2023-12-12T22:43:06.637314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:06.737746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 95
100.0%

(자가용)승용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.357895
Minimum0
Maximum159
Zeros6
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T22:43:06.827146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q323.5
95-th percentile86
Maximum159
Range159
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation30.079652
Coefficient of variation (CV)1.6385131
Kurtosis6.387048
Mean18.357895
Median Absolute Deviation (MAD)3
Skewness2.4309776
Sum1744
Variance904.78544
MonotonicityNot monotonic
2023-12-12T22:43:06.949262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 17
17.9%
2 13
13.7%
4 7
 
7.4%
3 6
 
6.3%
0 6
 
6.3%
5 5
 
5.3%
7 4
 
4.2%
6 3
 
3.2%
47 2
 
2.1%
13 2
 
2.1%
Other values (27) 30
31.6%
ValueCountFrequency (%)
0 6
 
6.3%
1 17
17.9%
2 13
13.7%
3 6
 
6.3%
4 7
7.4%
5 5
 
5.3%
6 3
 
3.2%
7 4
 
4.2%
8 2
 
2.1%
9 1
 
1.1%
ValueCountFrequency (%)
159 1
1.1%
126 1
1.1%
100 1
1.1%
94 1
1.1%
93 1
1.1%
83 1
1.1%
72 1
1.1%
70 1
1.1%
67 1
1.1%
65 1
1.1%

(자가용)승합
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
0
94 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 94
98.9%
1 1
 
1.1%

Length

2023-12-12T22:43:07.069314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:07.449939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 94
98.9%
1 1
 
1.1%

(자가용)화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6210526
Minimum0
Maximum38
Zeros16
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T22:43:07.540902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q38
95-th percentile21.9
Maximum38
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.2349314
Coefficient of variation (CV)1.2871133
Kurtosis4.6697652
Mean5.6210526
Median Absolute Deviation (MAD)2
Skewness2.0397377
Sum534
Variance52.344233
MonotonicityNot monotonic
2023-12-12T22:43:07.714207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 20
21.1%
0 16
16.8%
2 13
13.7%
3 8
 
8.4%
7 7
 
7.4%
12 5
 
5.3%
9 4
 
4.2%
8 3
 
3.2%
4 2
 
2.1%
11 2
 
2.1%
Other values (11) 15
15.8%
ValueCountFrequency (%)
0 16
16.8%
1 20
21.1%
2 13
13.7%
3 8
 
8.4%
4 2
 
2.1%
5 2
 
2.1%
6 1
 
1.1%
7 7
 
7.4%
8 3
 
3.2%
9 4
 
4.2%
ValueCountFrequency (%)
38 1
 
1.1%
26 2
 
2.1%
25 1
 
1.1%
24 1
 
1.1%
21 1
 
1.1%
20 1
 
1.1%
17 1
 
1.1%
16 2
 
2.1%
12 5
5.3%
11 2
 
2.1%

(자가용)특수
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
0
95 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 95
100.0%

Length

2023-12-12T22:43:07.887279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:07.970726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 95
100.0%

(영업용)승용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9157895
Minimum0
Maximum21
Zeros57
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T22:43:08.060113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8.9
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8748045
Coefficient of variation (CV)2.0225628
Kurtosis9.1745598
Mean1.9157895
Median Absolute Deviation (MAD)0
Skewness2.891687
Sum182
Variance15.01411
MonotonicityNot monotonic
2023-12-12T22:43:08.162247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 57
60.0%
1 13
 
13.7%
6 7
 
7.4%
2 5
 
5.3%
5 4
 
4.2%
16 2
 
2.1%
4 2
 
2.1%
11 1
 
1.1%
8 1
 
1.1%
21 1
 
1.1%
Other values (2) 2
 
2.1%
ValueCountFrequency (%)
0 57
60.0%
1 13
 
13.7%
2 5
 
5.3%
3 1
 
1.1%
4 2
 
2.1%
5 4
 
4.2%
6 7
 
7.4%
8 1
 
1.1%
11 1
 
1.1%
14 1
 
1.1%
ValueCountFrequency (%)
21 1
 
1.1%
16 2
 
2.1%
14 1
 
1.1%
11 1
 
1.1%
8 1
 
1.1%
6 7
7.4%
5 4
4.2%
4 2
 
2.1%
3 1
 
1.1%
2 5
5.3%

(영업용)승합
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size892.0 B
0
93 
4
 
1
48
 
1

Length

Max length2
Median length1
Mean length1.0105263
Min length1

Unique

Unique2 ?
Unique (%)2.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 93
97.9%
4 1
 
1.1%
48 1
 
1.1%

Length

2023-12-12T22:43:08.292407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:08.423032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 93
97.9%
4 1
 
1.1%
48 1
 
1.1%

(영업용)화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1789474
Minimum0
Maximum11
Zeros58
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T22:43:08.537596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5.6
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2596316
Coefficient of variation (CV)1.9166518
Kurtosis6.2085755
Mean1.1789474
Median Absolute Deviation (MAD)0
Skewness2.5024059
Sum112
Variance5.1059351
MonotonicityNot monotonic
2023-12-12T22:43:08.691799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 58
61.1%
1 18
 
18.9%
3 5
 
5.3%
5 5
 
5.3%
2 3
 
3.2%
9 2
 
2.1%
11 1
 
1.1%
8 1
 
1.1%
7 1
 
1.1%
4 1
 
1.1%
ValueCountFrequency (%)
0 58
61.1%
1 18
 
18.9%
2 3
 
3.2%
3 5
 
5.3%
4 1
 
1.1%
5 5
 
5.3%
7 1
 
1.1%
8 1
 
1.1%
9 2
 
2.1%
11 1
 
1.1%
ValueCountFrequency (%)
11 1
 
1.1%
9 2
 
2.1%
8 1
 
1.1%
7 1
 
1.1%
5 5
 
5.3%
4 1
 
1.1%
3 5
 
5.3%
2 3
 
3.2%
1 18
 
18.9%
0 58
61.1%

(영업용)특수
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
0
95 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 95
100.0%

Length

2023-12-12T22:43:08.857105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:43:09.095095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 95
100.0%

Interactions

2023-12-12T22:43:03.520785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:01.666964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.147890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.632560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.093698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.611386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:01.764356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.229925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.715852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.171089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.706360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:01.863923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.351820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.840571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.266994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.798390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:01.968507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.452470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.929501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.356258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.873757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.067651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:02.548116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.005355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:43:03.429064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:43:09.179360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동계(관용)승용(관용)승합(관용)화물(자가용)승용(자가용)승합(자가용)화물(영업용)승용(영업용)승합(영업용)화물
읍면동1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
계1.0001.0000.6660.0000.6070.8950.0000.9380.9260.6820.824
(관용)승용1.0000.6661.0000.0000.6670.2950.0000.4460.4230.0000.550
(관용)승합1.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
(관용)화물1.0000.6070.6670.0001.0000.5950.0000.6110.5680.0000.731
(자가용)승용1.0000.8950.2950.0000.5951.0000.0000.8340.7710.5620.966
(자가용)승합1.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
(자가용)화물1.0000.9380.4460.0000.6110.8340.0001.0000.9060.4350.801
(영업용)승용1.0000.9260.4230.0000.5680.7710.0000.9061.0000.7900.789
(영업용)승합1.0000.6820.0000.0000.0000.5620.0000.4350.7901.0000.717
(영업용)화물1.0000.8240.5500.0000.7310.9660.0000.8010.7890.7171.000
2023-12-12T22:43:09.326315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(자가용)승합(관용)승용(관용)승합(관용)화물(영업용)승합
(자가용)승합1.0000.0000.0000.0000.000
(관용)승용0.0001.0000.0000.6920.000
(관용)승합0.0000.0001.0000.0000.000
(관용)화물0.0000.6920.0001.0000.000
(영업용)승합0.0000.0000.0000.0001.000
2023-12-12T22:43:09.466757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계(자가용)승용(자가용)화물(영업용)승용(영업용)화물(관용)승용(관용)승합(관용)화물(자가용)승합(영업용)승합
계1.0000.9350.8680.6620.6350.2950.0000.4630.0000.548
(자가용)승용0.9351.0000.7120.5740.6220.1850.0000.3090.0000.286
(자가용)화물0.8680.7121.0000.5630.5400.2050.0000.4670.0000.298
(영업용)승용0.6620.5740.5631.0000.5660.1930.0000.4220.0000.690
(영업용)화물0.6350.6220.5400.5661.0000.3740.0000.4210.0000.409
(관용)승용0.2950.1850.2050.1930.3741.0000.0000.6920.0000.000
(관용)승합0.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
(관용)화물0.4630.3090.4670.4220.4210.6920.0001.0000.0000.000
(자가용)승합0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
(영업용)승합0.5480.2860.2980.6900.4090.0000.0000.0000.0001.000

Missing values

2023-12-12T22:43:04.002379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:43:04.243547image/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

기준시군구읍면동연료계(관용)승용(관용)승합(관용)화물(관용)특수(자가용)승용(자가용)승합(자가용)화물(자가용)특수(영업용)승용(영업용)승합(영업용)화물(영업용)특수
02023년5월경상남도 김해시동상동전기330000200802030
12023년5월경상남도 김해시서상동전기3000010101000
22023년5월경상남도 김해시부원동전기101330804701001020
32023년5월경상남도 김해시봉황동전기270000180306000
42023년5월경상남도 김해시대성동전기11000070202000
52023년5월경상남도 김해시구산동전기112001070020016050
62023년5월경상남도 김해시삼계동전기21900001590380110110
72023년5월경상남도 김해시내동전기10100007201608050
82023년5월경상남도 김해시외동전기143000083026021490
92023년5월경상남도 김해시흥동전기20000030905030
기준시군구읍면동연료계(관용)승용(관용)승합(관용)화물(관용)특수(자가용)승용(자가용)승합(자가용)화물(자가용)특수(영업용)승용(영업용)승합(영업용)화물(영업용)특수
852023년5월경상남도 김해시상동면 우계리전기2000010100000
862023년5월경상남도 김해시대동면전기8000010700000
872023년5월경상남도 김해시대동면 수안리전기3000030000000
882023년5월경상남도 김해시대동면 주중리전기2000000200000
892023년5월경상남도 김해시대동면 예안리전기10000070300000
902023년5월경상남도 김해시대동면 초정리전기9000050301000
912023년5월경상남도 김해시대동면 괴정리전기6000060000000
922023년5월경상남도 김해시대동면 대감리전기6000050100000
932023년5월경상남도 김해시대동면 덕산리전기5000050000000
942023년5월경상남도 김해시대동면 조눌리전기5000030100010