Overview

Dataset statistics

Number of variables15
Number of observations453
Missing cells1001
Missing cells (%)14.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.4 KiB
Average record size in memory134.3 B

Variable types

Text1
Numeric14

Dataset

Description서울시 행정동별 연료별 자동차 등록현황입니다. 출처는 국토교통부 산하 한국교통안전공단에서 운영하는 자동차관리정보시스템입니다. 자동차관리정보시스템측 문의 결과 법정동 기준과는 다르게 행정동 기준은 코드 입력이 정확하게 되어있지 않아 등록현황차이(데이터 중 경기도 포함, 차량대수)가 있을 수 있다고 전달 받았으니 참고해주시기 바랍니다.
Author서울특별시
URLhttps://www.data.go.kr/data/15094905/fileData.do

Alerts

CNG is highly overall correlated with 하이브리드(CNG-전기)High correlation
경유 is highly overall correlated with 기타연료 and 8 other fieldsHigh correlation
기타연료 is highly overall correlated with 경유 and 6 other fieldsHigh correlation
수소 is highly overall correlated with 경유 and 5 other fieldsHigh correlation
엘피지 is highly overall correlated with 경유 and 2 other fieldsHigh correlation
전기 is highly overall correlated with 경유 and 7 other fieldsHigh correlation
하이브리드(CNG-전기) is highly overall correlated with CNG and 1 other fieldsHigh correlation
하이브리드(경유-전기) is highly overall correlated with 경유 and 6 other fieldsHigh correlation
하이브리드(휘발유-전기) is highly overall correlated with 경유 and 7 other fieldsHigh correlation
휘발유 is highly overall correlated with 경유 and 7 other fieldsHigh correlation
휘발유(무연) is highly overall correlated with 경유 and 9 other fieldsHigh correlation
휘발유(유연) is highly overall correlated with 하이브리드(CNG-전기) and 1 other fieldsHigh correlation
총합계 is highly overall correlated with 경유 and 8 other fieldsHigh correlation
CNG has 133 (29.4%) missing valuesMissing
기타연료 has 32 (7.1%) missing valuesMissing
수소 has 59 (13.0%) missing valuesMissing
엘피지 has 15 (3.3%) missing valuesMissing
전기 has 26 (5.7%) missing valuesMissing
하이브리드(CNG-전기) has 446 (98.5%) missing valuesMissing
하이브리드(LPG-전기) has 62 (13.7%) missing valuesMissing
하이브리드(경유-전기) has 79 (17.4%) missing valuesMissing
하이브리드(휘발유-전기) has 26 (5.7%) missing valuesMissing
휘발유 has 13 (2.9%) missing valuesMissing
휘발유(무연) has 13 (2.9%) missing valuesMissing
휘발유(유연) has 93 (20.5%) missing valuesMissing
행정동-연료별 분류 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:14:33.766973
Analysis finished2023-12-12 18:14:58.060772
Duration24.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct453
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-13T03:14:58.293538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length13.874172
Min length11

Characters and Unicode

Total characters6285
Distinct characters197
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique453 ?
Unique (%)100.0%

Sample

1st row경기도 군포시 금정동
2nd row경기도 시흥시 정왕본동
3rd row경기도 안양시 만안구 석수1동
4th row경기도 평택시 서정동
5th row서울특별시 강남구 개포1동
ValueCountFrequency (%)
서울특별시 449
33.0%
송파구 27
 
2.0%
중구 23
 
1.7%
강남구 23
 
1.7%
강동구 22
 
1.6%
노원구 21
 
1.5%
관악구 21
 
1.5%
성북구 20
 
1.5%
강서구 20
 
1.5%
마포구 19
 
1.4%
Other values (474) 715
52.6%
2023-12-13T03:14:58.714020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
907
14.4%
523
 
8.3%
513
 
8.2%
480
 
7.6%
459
 
7.3%
449
 
7.1%
449
 
7.1%
449
 
7.1%
1 106
 
1.7%
2 100
 
1.6%
Other values (187) 1850
29.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5052
80.4%
Space Separator 907
 
14.4%
Decimal Number 314
 
5.0%
Other Punctuation 12
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
523
 
10.4%
513
 
10.2%
480
 
9.5%
459
 
9.1%
449
 
8.9%
449
 
8.9%
449
 
8.9%
82
 
1.6%
53
 
1.0%
52
 
1.0%
Other values (175) 1543
30.5%
Decimal Number
ValueCountFrequency (%)
1 106
33.8%
2 100
31.8%
3 47
15.0%
4 29
 
9.2%
5 13
 
4.1%
6 8
 
2.5%
7 6
 
1.9%
8 3
 
1.0%
9 1
 
0.3%
0 1
 
0.3%
Space Separator
ValueCountFrequency (%)
907
100.0%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5052
80.4%
Common 1233
 
19.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
523
 
10.4%
513
 
10.2%
480
 
9.5%
459
 
9.1%
449
 
8.9%
449
 
8.9%
449
 
8.9%
82
 
1.6%
53
 
1.0%
52
 
1.0%
Other values (175) 1543
30.5%
Common
ValueCountFrequency (%)
907
73.6%
1 106
 
8.6%
2 100
 
8.1%
3 47
 
3.8%
4 29
 
2.4%
5 13
 
1.1%
. 12
 
1.0%
6 8
 
0.6%
7 6
 
0.5%
8 3
 
0.2%
Other values (2) 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5052
80.4%
ASCII 1233
 
19.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
907
73.6%
1 106
 
8.6%
2 100
 
8.1%
3 47
 
3.8%
4 29
 
2.4%
5 13
 
1.1%
. 12
 
1.0%
6 8
 
0.6%
7 6
 
0.5%
8 3
 
0.2%
Other values (2) 2
 
0.2%
Hangul
ValueCountFrequency (%)
523
 
10.4%
513
 
10.2%
480
 
9.5%
459
 
9.1%
449
 
8.9%
449
 
8.9%
449
 
8.9%
82
 
1.6%
53
 
1.0%
52
 
1.0%
Other values (175) 1543
30.5%

CNG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct66
Distinct (%)20.6%
Missing133
Missing (%)29.4%
Infinite0
Infinite (%)0.0%
Mean28.15
Minimum1
Maximum572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:14:58.872131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q314.25
95-th percentile156.1
Maximum572
Range571
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation66.618099
Coefficient of variation (CV)2.3665399
Kurtosis22.878469
Mean28.15
Median Absolute Deviation (MAD)3
Skewness4.2297494
Sum9008
Variance4437.9712
MonotonicityNot monotonic
2023-12-13T03:14:59.004746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 83
18.3%
2 46
 
10.2%
3 26
 
5.7%
4 14
 
3.1%
6 13
 
2.9%
5 11
 
2.4%
7 10
 
2.2%
10 10
 
2.2%
11 6
 
1.3%
14 6
 
1.3%
Other values (56) 95
21.0%
(Missing) 133
29.4%
ValueCountFrequency (%)
1 83
18.3%
2 46
10.2%
3 26
 
5.7%
4 14
 
3.1%
5 11
 
2.4%
6 13
 
2.9%
7 10
 
2.2%
8 4
 
0.9%
9 4
 
0.9%
10 10
 
2.2%
ValueCountFrequency (%)
572 1
0.2%
461 1
0.2%
345 1
0.2%
312 1
0.2%
295 1
0.2%
289 1
0.2%
254 1
0.2%
235 1
0.2%
214 1
0.2%
204 1
0.2%

경유
Real number (ℝ)

HIGH CORRELATION 

Distinct414
Distinct (%)92.2%
Missing4
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean2425.441
Minimum1
Maximum8982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:14:59.152883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile145.6
Q11572
median2283
Q33056
95-th percentile4960.6
Maximum8982
Range8981
Interquartile range (IQR)1484

Descriptive statistics

Standard deviation1355.4995
Coefficient of variation (CV)0.55886723
Kurtosis2.5182655
Mean2425.441
Median Absolute Deviation (MAD)736
Skewness0.99789217
Sum1089023
Variance1837378.9
MonotonicityNot monotonic
2023-12-13T03:14:59.292724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
2.0%
3467 3
 
0.7%
5 3
 
0.7%
2557 2
 
0.4%
3151 2
 
0.4%
1842 2
 
0.4%
1351 2
 
0.4%
2492 2
 
0.4%
1992 2
 
0.4%
1900 2
 
0.4%
Other values (404) 420
92.7%
(Missing) 4
 
0.9%
ValueCountFrequency (%)
1 9
2.0%
2 2
 
0.4%
3 1
 
0.2%
5 3
 
0.7%
9 1
 
0.2%
11 1
 
0.2%
13 1
 
0.2%
18 1
 
0.2%
22 1
 
0.2%
25 1
 
0.2%
ValueCountFrequency (%)
8982 1
0.2%
8139 1
0.2%
8109 1
0.2%
7099 1
0.2%
6468 1
0.2%
6391 1
0.2%
6203 1
0.2%
6166 1
0.2%
6059 1
0.2%
5955 1
0.2%

기타연료
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)14.5%
Missing32
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean16.501188
Minimum1
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:14:59.427680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median12
Q320
95-th percentile48
Maximum128
Range127
Interquartile range (IQR)13

Descriptive statistics

Standard deviation15.797321
Coefficient of variation (CV)0.9573445
Kurtosis11.405428
Mean16.501188
Median Absolute Deviation (MAD)6
Skewness2.8357492
Sum6947
Variance249.55536
MonotonicityNot monotonic
2023-12-13T03:14:59.589689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 28
 
6.2%
9 23
 
5.1%
4 22
 
4.9%
5 22
 
4.9%
7 22
 
4.9%
6 22
 
4.9%
12 21
 
4.6%
8 21
 
4.6%
13 18
 
4.0%
3 16
 
3.5%
Other values (51) 206
45.5%
(Missing) 32
 
7.1%
ValueCountFrequency (%)
1 7
 
1.5%
2 6
 
1.3%
3 16
3.5%
4 22
4.9%
5 22
4.9%
6 22
4.9%
7 22
4.9%
8 21
4.6%
9 23
5.1%
10 28
6.2%
ValueCountFrequency (%)
128 1
0.2%
106 1
0.2%
91 1
0.2%
88 1
0.2%
84 1
0.2%
72 1
0.2%
70 1
0.2%
69 1
0.2%
67 1
0.2%
64 2
0.4%

수소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)6.9%
Missing59
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean5.7994924
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:14:59.731609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile13.35
Maximum106
Range105
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.4244558
Coefficient of variation (CV)1.2801906
Kurtosis90.180055
Mean5.7994924
Median Absolute Deviation (MAD)2
Skewness7.7589133
Sum2285
Variance55.122544
MonotonicityNot monotonic
2023-12-13T03:14:59.872957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 62
13.7%
1 57
12.6%
2 52
11.5%
5 39
8.6%
4 35
7.7%
6 32
7.1%
7 25
5.5%
8 22
 
4.9%
9 16
 
3.5%
12 12
 
2.6%
Other values (17) 42
9.3%
(Missing) 59
13.0%
ValueCountFrequency (%)
1 57
12.6%
2 52
11.5%
3 62
13.7%
4 35
7.7%
5 39
8.6%
6 32
7.1%
7 25
5.5%
8 22
 
4.9%
9 16
 
3.5%
10 8
 
1.8%
ValueCountFrequency (%)
106 1
0.2%
48 1
0.2%
47 1
0.2%
43 1
0.2%
29 1
0.2%
26 1
0.2%
24 1
0.2%
21 1
0.2%
19 1
0.2%
18 1
0.2%

엘피지
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct359
Distinct (%)82.0%
Missing15
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean563.42694
Minimum1
Maximum2555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:00.000409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.4
Q1308.75
median490.5
Q3716.75
95-th percentile1288.65
Maximum2555
Range2554
Interquartile range (IQR)408

Descriptive statistics

Standard deviation389.47784
Coefficient of variation (CV)0.69126591
Kurtosis4.8968572
Mean563.42694
Median Absolute Deviation (MAD)197.5
Skewness1.739001
Sum246781
Variance151692.99
MonotonicityNot monotonic
2023-12-13T03:15:00.150597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
2.0%
463 3
 
0.7%
588 3
 
0.7%
429 3
 
0.7%
487 3
 
0.7%
654 3
 
0.7%
553 3
 
0.7%
336 3
 
0.7%
634 3
 
0.7%
579 3
 
0.7%
Other values (349) 402
88.7%
(Missing) 15
 
3.3%
ValueCountFrequency (%)
1 9
2.0%
4 1
 
0.2%
9 1
 
0.2%
23 1
 
0.2%
38 1
 
0.2%
49 1
 
0.2%
55 1
 
0.2%
63 1
 
0.2%
64 1
 
0.2%
82 1
 
0.2%
ValueCountFrequency (%)
2555 1
0.2%
2523 1
0.2%
2392 1
0.2%
2158 1
0.2%
1951 1
0.2%
1935 1
0.2%
1888 1
0.2%
1766 1
0.2%
1696 1
0.2%
1638 1
0.2%

전기
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct135
Distinct (%)31.6%
Missing26
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean82.124122
Minimum5
Maximum3835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:00.308143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14
Q125.5
median41
Q365
95-th percentile147.7
Maximum3835
Range3830
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation244.12033
Coefficient of variation (CV)2.9725776
Kurtosis141.64295
Mean82.124122
Median Absolute Deviation (MAD)18
Skewness10.670137
Sum35067
Variance59594.733
MonotonicityNot monotonic
2023-12-13T03:15:00.748764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 11
 
2.4%
21 11
 
2.4%
24 11
 
2.4%
36 10
 
2.2%
30 10
 
2.2%
31 10
 
2.2%
32 9
 
2.0%
41 9
 
2.0%
44 9
 
2.0%
27 9
 
2.0%
Other values (125) 328
72.4%
(Missing) 26
 
5.7%
ValueCountFrequency (%)
5 2
 
0.4%
6 1
 
0.2%
7 2
 
0.4%
8 5
1.1%
9 2
 
0.4%
10 1
 
0.2%
11 3
0.7%
13 3
0.7%
14 6
1.3%
15 4
0.9%
ValueCountFrequency (%)
3835 1
0.2%
1685 1
0.2%
1487 1
0.2%
1461 1
0.2%
1173 1
0.2%
1103 1
0.2%
940 1
0.2%
774 1
0.2%
505 1
0.2%
473 1
0.2%

하이브리드(CNG-전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)85.7%
Missing446
Missing (%)98.5%
Infinite0
Infinite (%)0.0%
Mean7.2857143
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:00.872797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q12
median3
Q36
95-th percentile24.1
Maximum31
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation10.703804
Coefficient of variation (CV)1.4691496
Kurtosis5.9704579
Mean7.2857143
Median Absolute Deviation (MAD)1
Skewness2.412315
Sum51
Variance114.57143
MonotonicityNot monotonic
2023-12-13T03:15:00.976693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 2
 
0.4%
31 1
 
0.2%
8 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
1 1
 
0.2%
(Missing) 446
98.5%
ValueCountFrequency (%)
1 1
0.2%
2 2
0.4%
3 1
0.2%
4 1
0.2%
8 1
0.2%
31 1
0.2%
ValueCountFrequency (%)
31 1
0.2%
8 1
0.2%
4 1
0.2%
3 1
0.2%
2 2
0.4%
1 1
0.2%

하이브리드(LPG-전기)
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)4.3%
Missing62
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean4.3120205
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:01.083796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile9.5
Maximum35
Range34
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7436899
Coefficient of variation (CV)0.86819855
Kurtosis29.373105
Mean4.3120205
Median Absolute Deviation (MAD)1
Skewness4.2294571
Sum1686
Variance14.015214
MonotonicityNot monotonic
2023-12-13T03:15:01.200932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 77
17.0%
2 67
14.8%
4 58
12.8%
1 57
12.6%
5 33
7.3%
6 27
 
6.0%
7 22
 
4.9%
9 15
 
3.3%
8 15
 
3.3%
10 8
 
1.8%
Other values (7) 12
 
2.6%
(Missing) 62
13.7%
ValueCountFrequency (%)
1 57
12.6%
2 67
14.8%
3 77
17.0%
4 58
12.8%
5 33
7.3%
6 27
 
6.0%
7 22
 
4.9%
8 15
 
3.3%
9 15
 
3.3%
10 8
 
1.8%
ValueCountFrequency (%)
35 2
 
0.4%
32 1
 
0.2%
15 2
 
0.4%
14 1
 
0.2%
13 1
 
0.2%
12 2
 
0.4%
11 3
 
0.7%
10 8
1.8%
9 15
3.3%
8 15
3.3%

하이브리드(경유-전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)7.0%
Missing79
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean5.2486631
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:01.325058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile16.35
Maximum53
Range52
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.5268961
Coefficient of variation (CV)1.0530103
Kurtosis18.648853
Mean5.2486631
Median Absolute Deviation (MAD)2
Skewness3.3086327
Sum1963
Variance30.54658
MonotonicityNot monotonic
2023-12-13T03:15:01.452917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 81
17.9%
3 56
12.4%
2 51
11.3%
4 41
9.1%
5 26
 
5.7%
9 19
 
4.2%
7 17
 
3.8%
6 17
 
3.8%
8 16
 
3.5%
12 10
 
2.2%
Other values (16) 40
8.8%
(Missing) 79
17.4%
ValueCountFrequency (%)
1 81
17.9%
2 51
11.3%
3 56
12.4%
4 41
9.1%
5 26
 
5.7%
6 17
 
3.8%
7 17
 
3.8%
8 16
 
3.5%
9 19
 
4.2%
10 9
 
2.0%
ValueCountFrequency (%)
53 1
 
0.2%
37 1
 
0.2%
29 1
 
0.2%
25 1
 
0.2%
23 2
 
0.4%
22 1
 
0.2%
20 2
 
0.4%
19 1
 
0.2%
18 4
0.9%
17 5
1.1%

하이브리드(휘발유-전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct303
Distinct (%)71.0%
Missing26
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean314.3466
Minimum6
Maximum3714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:01.576354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile87
Q1162.5
median248
Q3391
95-th percentile731
Maximum3714
Range3708
Interquartile range (IQR)228.5

Descriptive statistics

Standard deviation301.69129
Coefficient of variation (CV)0.9597409
Kurtosis69.420806
Mean314.3466
Median Absolute Deviation (MAD)105
Skewness6.7481514
Sum134226
Variance91017.635
MonotonicityNot monotonic
2023-12-13T03:15:01.761326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237 5
 
1.1%
283 5
 
1.1%
308 4
 
0.9%
125 4
 
0.9%
427 3
 
0.7%
166 3
 
0.7%
230 3
 
0.7%
141 3
 
0.7%
120 3
 
0.7%
78 3
 
0.7%
Other values (293) 391
86.3%
(Missing) 26
 
5.7%
ValueCountFrequency (%)
6 1
0.2%
19 1
0.2%
27 1
0.2%
30 1
0.2%
34 1
0.2%
42 1
0.2%
45 2
0.4%
49 1
0.2%
50 1
0.2%
54 1
0.2%
ValueCountFrequency (%)
3714 1
0.2%
3588 1
0.2%
1308 1
0.2%
1254 1
0.2%
1045 1
0.2%
903 1
0.2%
889 1
0.2%
861 1
0.2%
853 1
0.2%
829 1
0.2%

휘발유
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct396
Distinct (%)90.0%
Missing13
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1523.2432
Minimum1
Maximum6182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:01.920321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile322.85
Q1904
median1272.5
Q31927.5
95-th percentile3676.35
Maximum6182
Range6181
Interquartile range (IQR)1023.5

Descriptive statistics

Standard deviation976.21001
Coefficient of variation (CV)0.64087601
Kurtosis3.0914697
Mean1523.2432
Median Absolute Deviation (MAD)466
Skewness1.4949547
Sum670227
Variance952985.97
MonotonicityNot monotonic
2023-12-13T03:15:02.094509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
0.9%
877 3
 
0.7%
1660 3
 
0.7%
1762 3
 
0.7%
1443 3
 
0.7%
1103 3
 
0.7%
635 2
 
0.4%
999 2
 
0.4%
1160 2
 
0.4%
1043 2
 
0.4%
Other values (386) 413
91.2%
(Missing) 13
 
2.9%
ValueCountFrequency (%)
1 4
0.9%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
11 2
0.4%
13 1
 
0.2%
54 1
 
0.2%
ValueCountFrequency (%)
6182 1
0.2%
5618 1
0.2%
5332 1
0.2%
5228 1
0.2%
4877 1
0.2%
4466 1
0.2%
4403 1
0.2%
4352 1
0.2%
4256 1
0.2%
4206 1
0.2%

휘발유(무연)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct412
Distinct (%)93.6%
Missing13
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean2208.3432
Minimum1
Maximum7193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:02.237133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile467.9
Q11440.25
median2063.5
Q32837.5
95-th percentile4434.45
Maximum7193
Range7192
Interquartile range (IQR)1397.25

Descriptive statistics

Standard deviation1172.7819
Coefficient of variation (CV)0.53106866
Kurtosis1.2704056
Mean2208.3432
Median Absolute Deviation (MAD)700.5
Skewness0.7852948
Sum971671
Variance1375417.3
MonotonicityNot monotonic
2023-12-13T03:15:02.381938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
1.1%
2764 3
 
0.7%
3 3
 
0.7%
1495 2
 
0.4%
3065 2
 
0.4%
1799 2
 
0.4%
1625 2
 
0.4%
3264 2
 
0.4%
1193 2
 
0.4%
3123 2
 
0.4%
Other values (402) 415
91.6%
(Missing) 13
 
2.9%
ValueCountFrequency (%)
1 5
1.1%
2 1
 
0.2%
3 3
0.7%
7 1
 
0.2%
9 1
 
0.2%
15 2
 
0.4%
72 1
 
0.2%
101 1
 
0.2%
231 1
 
0.2%
317 1
 
0.2%
ValueCountFrequency (%)
7193 1
0.2%
6616 1
0.2%
5964 1
0.2%
5879 1
0.2%
5835 1
0.2%
5613 1
0.2%
5440 1
0.2%
5297 1
0.2%
5257 1
0.2%
5091 1
0.2%

휘발유(유연)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)3.3%
Missing93
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean2.9694444
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:02.523895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile7
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0458967
Coefficient of variation (CV)0.68898298
Kurtosis3.3215297
Mean2.9694444
Median Absolute Deviation (MAD)1
Skewness1.6350677
Sum1069
Variance4.1856933
MonotonicityNot monotonic
2023-12-13T03:15:02.650452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 91
20.1%
1 91
20.1%
3 75
16.6%
4 44
9.7%
5 21
 
4.6%
6 11
 
2.4%
7 10
 
2.2%
8 9
 
2.0%
10 3
 
0.7%
9 3
 
0.7%
Other values (2) 2
 
0.4%
(Missing) 93
20.5%
ValueCountFrequency (%)
1 91
20.1%
2 91
20.1%
3 75
16.6%
4 44
9.7%
5 21
 
4.6%
6 11
 
2.4%
7 10
 
2.2%
8 9
 
2.0%
9 3
 
0.7%
10 3
 
0.7%
ValueCountFrequency (%)
13 1
 
0.2%
12 1
 
0.2%
10 3
 
0.7%
9 3
 
0.7%
8 9
 
2.0%
7 10
 
2.2%
6 11
 
2.4%
5 21
 
4.6%
4 44
9.7%
3 75
16.6%

총합계
Real number (ℝ)

HIGH CORRELATION 

Distinct436
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6997.8013
Minimum1
Maximum25200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2023-12-13T03:15:02.786933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41.6
Q14559
median6472
Q38837
95-th percentile13959.2
Maximum25200
Range25199
Interquartile range (IQR)4278

Descriptive statistics

Standard deviation3980.3778
Coefficient of variation (CV)0.56880407
Kurtosis1.6475947
Mean6997.8013
Median Absolute Deviation (MAD)2150
Skewness0.80867456
Sum3170004
Variance15843408
MonotonicityNot monotonic
2023-12-13T03:15:02.944287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
2.4%
7876 2
 
0.4%
2 2
 
0.4%
8720 2
 
0.4%
6377 2
 
0.4%
11850 2
 
0.4%
8384 2
 
0.4%
4639 2
 
0.4%
8944 1
 
0.2%
6826 1
 
0.2%
Other values (426) 426
94.0%
ValueCountFrequency (%)
1 11
2.4%
2 2
 
0.4%
3 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
11 1
 
0.2%
12 1
 
0.2%
16 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
25200 1
0.2%
21372 1
0.2%
21321 1
0.2%
21157 1
0.2%
20104 1
0.2%
18782 1
0.2%
18022 1
0.2%
17714 1
0.2%
17323 1
0.2%
16814 1
0.2%

Interactions

2023-12-13T03:14:56.244125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:34.669637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.260514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:37.879468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:39.606317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:41.695864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:43.334951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.030188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.639082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:48.272591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.840660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:51.265122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.905098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.657763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.326197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:34.788805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.375120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.002441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:39.726353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:41.817682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:43.451911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.145514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.752668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:48.668076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.928009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:51.365410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:53.025467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.739455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.403632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:34.888468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.471869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.109069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:39.849138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:41.935570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:43.580158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.269297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.863518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:48.745861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.019536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:51.500216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:53.141368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.825234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.491985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:34.997339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.569189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.213494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:39.957244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.065780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:43.703706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.382390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.990006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:48.833819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.128255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:51.634296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:53.266817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.921940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.582022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:35.101844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.656595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.345299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:40.056789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.185833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:43.816483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.491550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:47.125090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:48.915524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.228564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:51.772701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:53.406423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:55.016615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.672510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:35.208948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.745308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.471534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:40.164548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.296492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:43.934725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.609063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:47.231622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:48.997895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.328934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:51.889668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:53.564619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:55.413758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.772394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:35.326922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.877093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.610736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:40.300516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.419055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:44.049502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.732444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:47.356017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.098608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.426248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.004003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:53.716629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:55.503130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.852510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:35.439154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.980606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.733260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:40.405711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.525647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:44.134941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.853224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:47.467832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.191428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.510900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.102297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:53.842752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:55.583291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.954910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:35.552336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:37.099878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.866704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:40.897026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.652965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:44.255788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:45.955711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:47.616355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.285520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.617428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.199567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:53.957075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:55.681599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:57.045703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:35.681632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:37.214903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:38.988717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:41.003341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.758232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:44.349818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.082897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:47.735172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.374979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.721770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.302546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.070566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:55.766149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:57.139759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:35.802821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:37.334663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:39.091947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:41.157526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.867248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:44.468342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.181583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:47.851271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.470345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.835569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.421261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.177421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:55.861773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:57.224266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:35.907206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:37.450050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:39.219733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:41.319251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:42.980859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:44.608899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.310652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:47.955814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.568702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:50.939956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.547731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.275158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:55.952170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:57.310627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.016142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:37.617133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:39.359086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:41.445341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:43.126279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:44.765704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.418272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:48.071480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.661274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:51.047510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.675167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.417987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.048873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:57.390105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:36.142568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:37.734967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:39.480046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:41.569762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:43.231541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:44.899473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:46.532632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:48.165268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:49.745037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:51.149309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:52.785834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:54.539917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:14:56.151556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:15:03.069201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CNG경유기타연료수소엘피지전기하이브리드(CNG-전기)하이브리드(LPG-전기)하이브리드(경유-전기)하이브리드(휘발유-전기)휘발유휘발유(무연)휘발유(유연)총합계
CNG1.0000.0000.0000.4170.3600.2141.0000.2830.0000.0000.0000.0000.0000.000
경유0.0001.0000.6070.5640.6720.6250.7490.6670.5380.6510.7040.8570.5750.891
기타연료0.0000.6071.0000.5430.4190.4180.4310.3990.5420.7660.7530.7100.5240.744
수소0.4170.5640.5431.0000.2190.7910.0000.2920.2300.6060.4670.6210.2300.587
엘피지0.3600.6720.4190.2191.0000.4160.5280.7240.2810.3880.5950.7570.2760.819
전기0.2140.6250.4180.7910.4161.000NaN0.3820.2650.5390.5490.4810.5910.512
하이브리드(CNG-전기)1.0000.7490.4310.0000.528NaN1.0000.4650.0000.4240.2390.2030.0000.662
하이브리드(LPG-전기)0.2830.6670.3990.2920.7240.3820.4651.0000.2680.2390.3440.5550.2990.567
하이브리드(경유-전기)0.0000.5380.5420.2300.2810.2650.0000.2681.0000.6820.6100.5960.4350.715
하이브리드(휘발유-전기)0.0000.6510.7660.6060.3880.5390.4240.2390.6821.0000.9090.8940.4450.956
휘발유0.0000.7040.7530.4670.5950.5490.2390.3440.6100.9091.0000.9040.5310.892
휘발유(무연)0.0000.8570.7100.6210.7570.4810.2030.5550.5960.8940.9041.0000.5350.970
휘발유(유연)0.0000.5750.5240.2300.2760.5910.0000.2990.4350.4450.5310.5351.0000.505
총합계0.0000.8910.7440.5870.8190.5120.6620.5670.7150.9560.8920.9700.5051.000
2023-12-13T03:15:03.241661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CNG경유기타연료수소엘피지전기하이브리드(CNG-전기)하이브리드(LPG-전기)하이브리드(경유-전기)하이브리드(휘발유-전기)휘발유휘발유(무연)휘발유(유연)총합계
CNG1.0000.2490.1770.1600.2310.3130.6490.2120.1820.1350.1420.1830.0860.224
경유0.2491.0000.7520.5130.6950.7420.3960.4900.5110.7720.8590.9160.4690.966
기타연료0.1770.7521.0000.4730.3450.7230.3550.3360.5440.7480.7480.7340.3170.768
수소0.1600.5130.4731.0000.2300.569-0.3270.2160.4270.6400.6090.5750.3200.583
엘피지0.2310.6950.3450.2301.0000.3520.3600.4750.0320.3150.4260.6350.3100.636
전기0.3130.7420.7230.5690.3521.0000.3060.3500.6040.7830.7890.7260.3520.797
하이브리드(CNG-전기)0.6490.3960.355-0.3270.3600.3061.0000.367-0.441-0.2520.0360.1980.8320.270
하이브리드(LPG-전기)0.2120.4900.3360.2160.4750.3500.3671.0000.1260.3310.3480.4400.2300.456
하이브리드(경유-전기)0.1820.5110.5440.4270.0320.604-0.4410.1261.0000.6800.6620.5260.3170.575
하이브리드(휘발유-전기)0.1350.7720.7480.6400.3150.783-0.2520.3310.6801.0000.9630.8780.4620.885
휘발유0.1420.8590.7480.6090.4260.7890.0360.3480.6620.9631.0000.9230.4740.944
휘발유(무연)0.1830.9160.7340.5750.6350.7260.1980.4400.5260.8780.9231.0000.5150.969
휘발유(유연)0.0860.4690.3170.3200.3100.3520.8320.2300.3170.4620.4740.5151.0000.495
총합계0.2240.9660.7680.5830.6360.7970.2700.4560.5750.8850.9440.9690.4951.000

Missing values

2023-12-13T03:14:57.508389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:14:57.698503image/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.
2023-12-13T03:14:57.920406image/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

행정동-연료별 분류CNG경유기타연료수소엘피지전기하이브리드(CNG-전기)하이브리드(LPG-전기)하이브리드(경유-전기)하이브리드(휘발유-전기)휘발유휘발유(무연)휘발유(유연)총합계
0경기도 군포시 금정동<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA>1
1경기도 시흥시 정왕본동<NA>2<NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA>3
2경기도 안양시 만안구 석수1동<NA>1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1
3경기도 평택시 서정동<NA>1<NA><NA><NA><NA><NA><NA><NA><NA>1<NA><NA>2
4서울특별시 강남구 개포1동89117810113020<NA>31190917105823599
5서울특별시 강남구 개포2동792714129484111<NA>4106242949294759948
6서울특별시 강남구 개포4동3272218943064<NA>264642230261238563
7서울특별시 강남구 논현1동3360831742061<NA>1941626613309310529
8서울특별시 강남구 논현2동63646226355103<NA>71537629642791410295
9서울특별시 강남구 논현동<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA>1
행정동-연료별 분류CNG경유기타연료수소엘피지전기하이브리드(CNG-전기)하이브리드(LPG-전기)하이브리드(경유-전기)하이브리드(휘발유-전기)휘발유휘발유(무연)휘발유(유연)총합계
443서울특별시 중랑구 면목7동<NA>222411558632<NA>3318610681881<NA>5999
444서울특별시 중랑구 면목본동1320610178026<NA>321921366246928058
445서울특별시 중랑구 묵1동<NA>393724588953<NA>3635920663373210717
446서울특별시 중랑구 묵2동322654256319<NA><NA><NA>127948166915601
447서울특별시 중랑구 상봉1동4291814399244<NA>382681222265658137
448서울특별시 중랑구 상봉2동119448148832<NA>141661008178735443
449서울특별시 중랑구 신내1동1074806455136986<NA>6642121263821312801
450서울특별시 중랑구 신내2동5237013294365<NA>822191157213036917
451서울특별시 중랑구 중화1동<NA>211210255323<NA>22142965180025613
452서울특별시 중랑구 중화2동<NA>24328370630<NA>3<NA>1331073181226202