Overview

Dataset statistics

Number of variables15
Number of observations472
Missing cells1907
Missing cells (%)26.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.9 KiB
Average record size in memory134.3 B

Variable types

Text1
Numeric13
Categorical1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21236/F/1/datasetView.do

Alerts

동별 총 대수 is highly overall correlated with 휘발유 and 11 other fieldsHigh correlation
휘발유 is highly overall correlated with 동별 총 대수 and 10 other fieldsHigh correlation
경유 is highly overall correlated with 동별 총 대수 and 11 other fieldsHigh correlation
엘피지 is highly overall correlated with 동별 총 대수 and 11 other fieldsHigh correlation
전기 is highly overall correlated with 동별 총 대수 and 10 other fieldsHigh correlation
휘발유 (유연) is highly overall correlated with 동별 총 대수 and 10 other fieldsHigh correlation
휘발유 (무연) is highly overall correlated with 동별 총 대수 and 11 other fieldsHigh correlation
CNG is highly overall correlated with 동별 총 대수 and 5 other fieldsHigh correlation
하이브리드 (휘발유+전기) is highly overall correlated with 동별 총 대수 and 10 other fieldsHigh correlation
하이브리드 (LPG+전기) is highly overall correlated with 동별 총 대수 and 10 other fieldsHigh correlation
하이브리드 (CNG+전기) is highly overall correlated with 동별 총 대수 and 12 other fieldsHigh correlation
수소 is highly overall correlated with 하이브리드 (CNG+전기) and 1 other fieldsHigh correlation
기타연료 is highly overall correlated with 동별 총 대수 and 10 other fieldsHigh correlation
하이브리드 (경유+전기) is highly overall correlated with 동별 총 대수 and 12 other fieldsHigh correlation
하이브리드 (경유+전기) is highly imbalanced (81.4%)Imbalance
엘피지 has 25 (5.3%) missing valuesMissing
전기 has 137 (29.0%) missing valuesMissing
휘발유 (유연) has 246 (52.1%) missing valuesMissing
휘발유 (무연) has 5 (1.1%) missing valuesMissing
CNG has 265 (56.1%) missing valuesMissing
하이브리드 (휘발유+전기) has 23 (4.9%) missing valuesMissing
하이브리드 (LPG+전기) has 212 (44.9%) missing valuesMissing
하이브리드 (CNG+전기) has 464 (98.3%) missing valuesMissing
수소 has 384 (81.4%) missing valuesMissing
기타연료 has 140 (29.7%) missing valuesMissing
동별 총 대수 is highly skewed (γ1 = 21.54061721)Skewed
휘발유 is highly skewed (γ1 = 21.44086449)Skewed
경유 is highly skewed (γ1 = 21.4984852)Skewed
엘피지 is highly skewed (γ1 = 20.91740742)Skewed
휘발유 (무연) is highly skewed (γ1 = 21.42149853)Skewed
하이브리드 (휘발유+전기) is highly skewed (γ1 = 20.89943834)Skewed
사용본거지법정동명 has unique valuesUnique

Reproduction

Analysis started2024-04-06 12:32:28.201041
Analysis finished2024-04-06 12:33:07.821134
Duration39.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct472
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2024-04-06T21:33:08.052656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length13.559322
Min length2

Characters and Unicode

Total characters6400
Distinct characters220
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

Unique472 ?
Unique (%)100.0%

Sample

1st row서울특별시 종로구 청운동
2nd row서울특별시 종로구 신교동
3rd row서울특별시 종로구 궁정동
4th row서울특별시 종로구 효자동
5th row서울특별시 종로구 창성동
ValueCountFrequency (%)
서울특별시 471
33.3%
종로구 87
 
6.2%
중구 74
 
5.2%
성북구 39
 
2.8%
용산구 37
 
2.6%
영등포구 34
 
2.4%
마포구 26
 
1.8%
서대문구 20
 
1.4%
성동구 17
 
1.2%
강남구 16
 
1.1%
Other values (486) 593
41.9%
2024-04-06T21:33:08.845772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
942
14.7%
522
 
8.2%
489
 
7.6%
487
 
7.6%
472
 
7.4%
471
 
7.4%
471
 
7.4%
471
 
7.4%
145
 
2.3%
138
 
2.2%
Other values (210) 1792
28.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5319
83.1%
Space Separator 942
 
14.7%
Decimal Number 139
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
522
 
9.8%
489
 
9.2%
487
 
9.2%
472
 
8.9%
471
 
8.9%
471
 
8.9%
471
 
8.9%
145
 
2.7%
138
 
2.6%
95
 
1.8%
Other values (201) 1558
29.3%
Decimal Number
ValueCountFrequency (%)
1 37
26.6%
2 34
24.5%
3 23
16.5%
4 17
12.2%
5 14
 
10.1%
6 9
 
6.5%
7 4
 
2.9%
8 1
 
0.7%
Space Separator
ValueCountFrequency (%)
942
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5319
83.1%
Common 1081
 
16.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
522
 
9.8%
489
 
9.2%
487
 
9.2%
472
 
8.9%
471
 
8.9%
471
 
8.9%
471
 
8.9%
145
 
2.7%
138
 
2.6%
95
 
1.8%
Other values (201) 1558
29.3%
Common
ValueCountFrequency (%)
942
87.1%
1 37
 
3.4%
2 34
 
3.1%
3 23
 
2.1%
4 17
 
1.6%
5 14
 
1.3%
6 9
 
0.8%
7 4
 
0.4%
8 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5319
83.1%
ASCII 1081
 
16.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
942
87.1%
1 37
 
3.4%
2 34
 
3.1%
3 23
 
2.1%
4 17
 
1.6%
5 14
 
1.3%
6 9
 
0.8%
7 4
 
0.4%
8 1
 
0.1%
Hangul
ValueCountFrequency (%)
522
 
9.8%
489
 
9.2%
487
 
9.2%
472
 
8.9%
471
 
8.9%
471
 
8.9%
471
 
8.9%
145
 
2.7%
138
 
2.6%
95
 
1.8%
Other values (201) 1558
29.3%

동별 총 대수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct445
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13238.513
Minimum1
Maximum3124289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:09.134202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51.55
Q1299.75
median1408.5
Q39160.75
95-th percentile29867.75
Maximum3124289
Range3124288
Interquartile range (IQR)8861

Descriptive statistics

Standard deviation143911.96
Coefficient of variation (CV)10.870705
Kurtosis466.60706
Mean13238.513
Median Absolute Deviation (MAD)1320
Skewness21.540617
Sum6248578
Variance2.0710653 × 1010
MonotonicityNot monotonic
2024-04-06T21:33:09.451628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 3
 
0.6%
2 3
 
0.6%
224 2
 
0.4%
488 2
 
0.4%
80 2
 
0.4%
308 2
 
0.4%
987 2
 
0.4%
57 2
 
0.4%
45 2
 
0.4%
1 2
 
0.4%
Other values (435) 450
95.3%
ValueCountFrequency (%)
1 2
0.4%
2 3
0.6%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
13 1
 
0.2%
18 1
 
0.2%
19 1
 
0.2%
ValueCountFrequency (%)
3124289 1
0.2%
67157 1
0.2%
61146 1
0.2%
60162 1
0.2%
57465 1
0.2%
55928 1
0.2%
54485 1
0.2%
49272 1
0.2%
48283 1
0.2%
43592 1
0.2%

휘발유
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct378
Distinct (%)80.8%
Missing4
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2923.1966
Minimum1
Maximum684028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:09.793862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q166.75
median326
Q32059
95-th percentile6336.35
Maximum684028
Range684027
Interquartile range (IQR)1992.25

Descriptive statistics

Standard deviation31645.814
Coefficient of variation (CV)10.825756
Kurtosis462.41009
Mean2923.1966
Median Absolute Deviation (MAD)306
Skewness21.440864
Sum1368056
Variance1.0014575 × 109
MonotonicityNot monotonic
2024-04-06T21:33:10.114818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 6
 
1.3%
2 5
 
1.1%
34 4
 
0.8%
51 4
 
0.8%
16 4
 
0.8%
5 4
 
0.8%
76 3
 
0.6%
30 3
 
0.6%
48 3
 
0.6%
1 3
 
0.6%
Other values (368) 429
90.9%
(Missing) 4
 
0.8%
ValueCountFrequency (%)
1 3
0.6%
2 5
1.1%
3 2
 
0.4%
5 4
0.8%
7 3
0.6%
8 1
 
0.2%
9 3
0.6%
11 2
 
0.4%
12 2
 
0.4%
13 3
0.6%
ValueCountFrequency (%)
684028 1
0.2%
14601 1
0.2%
13727 1
0.2%
13309 1
0.2%
12968 1
0.2%
12029 1
0.2%
11861 1
0.2%
11653 1
0.2%
11559 1
0.2%
11343 1
0.2%

경유
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct407
Distinct (%)86.6%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean4853.3277
Minimum1
Maximum1140532
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:10.473659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20.45
Q1128.75
median558.5
Q33516.75
95-th percentile10725.1
Maximum1140532
Range1140531
Interquartile range (IQR)3388

Descriptive statistics

Standard deviation52643.809
Coefficient of variation (CV)10.846951
Kurtosis464.73313
Mean4853.3277
Median Absolute Deviation (MAD)516
Skewness21.498485
Sum2281064
Variance2.7713707 × 109
MonotonicityNot monotonic
2024-04-06T21:33:10.820921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
1.1%
18 4
 
0.8%
65 4
 
0.8%
179 4
 
0.8%
89 3
 
0.6%
163 3
 
0.6%
105 3
 
0.6%
109 3
 
0.6%
48 3
 
0.6%
164 3
 
0.6%
Other values (397) 435
92.2%
ValueCountFrequency (%)
1 5
1.1%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
 
0.4%
9 1
 
0.2%
11 1
 
0.2%
13 1
 
0.2%
15 1
 
0.2%
16 2
 
0.4%
ValueCountFrequency (%)
1140532 1
0.2%
24790 1
0.2%
23085 1
0.2%
22729 1
0.2%
20507 1
0.2%
19953 1
0.2%
19216 1
0.2%
17245 1
0.2%
16272 1
0.2%
15994 1
0.2%

엘피지
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct290
Distinct (%)64.9%
Missing25
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean1251.5123
Minimum1
Maximum279713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:11.182220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3
Q119
median105
Q3827.5
95-th percentile2763
Maximum279713
Range279712
Interquartile range (IQR)808.5

Descriptive statistics

Standard deviation13247.64
Coefficient of variation (CV)10.585306
Kurtosis440.60331
Mean1251.5123
Median Absolute Deviation (MAD)101
Skewness20.917407
Sum559426
Variance1.7549997 × 108
MonotonicityNot monotonic
2024-04-06T21:33:11.627331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 9
 
1.9%
3 9
 
1.9%
14 9
 
1.9%
4 8
 
1.7%
2 7
 
1.5%
9 7
 
1.5%
1 7
 
1.5%
15 7
 
1.5%
18 6
 
1.3%
13 6
 
1.3%
Other values (280) 372
78.8%
(Missing) 25
 
5.3%
ValueCountFrequency (%)
1 7
1.5%
2 7
1.5%
3 9
1.9%
4 8
1.7%
5 6
1.3%
6 9
1.9%
7 5
1.1%
8 6
1.3%
9 7
1.5%
10 6
1.3%
ValueCountFrequency (%)
279713 1
0.2%
7765 1
0.2%
7261 1
0.2%
6610 1
0.2%
6390 1
0.2%
6240 1
0.2%
5138 1
0.2%
5125 1
0.2%
4818 1
0.2%
4568 1
0.2%

전기
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)20.0%
Missing137
Missing (%)29.0%
Infinite0
Infinite (%)0.0%
Mean60.746269
Minimum1
Maximum10175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:11.910555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q321
95-th percentile65.1
Maximum10175
Range10174
Interquartile range (IQR)19

Descriptive statistics

Standard deviation592.62572
Coefficient of variation (CV)9.7557551
Kurtosis260.30699
Mean60.746269
Median Absolute Deviation (MAD)6
Skewness15.716836
Sum20350
Variance351205.24
MonotonicityNot monotonic
2024-04-06T21:33:12.380539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57
12.1%
2 32
 
6.8%
3 24
 
5.1%
4 17
 
3.6%
7 15
 
3.2%
5 13
 
2.8%
6 13
 
2.8%
8 11
 
2.3%
13 9
 
1.9%
11 8
 
1.7%
Other values (57) 136
28.8%
(Missing) 137
29.0%
ValueCountFrequency (%)
1 57
12.1%
2 32
6.8%
3 24
5.1%
4 17
 
3.6%
5 13
 
2.8%
6 13
 
2.8%
7 15
 
3.2%
8 11
 
2.3%
9 5
 
1.1%
10 6
 
1.3%
ValueCountFrequency (%)
10175 1
0.2%
3776 1
0.2%
552 1
0.2%
294 1
0.2%
264 1
0.2%
239 1
0.2%
208 1
0.2%
112 1
0.2%
98 1
0.2%
97 1
0.2%

휘발유 (유연)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)10.6%
Missing246
Missing (%)52.1%
Infinite0
Infinite (%)0.0%
Mean9.9380531
Minimum1
Maximum1123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:12.663233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile15
Maximum1123
Range1122
Interquartile range (IQR)6

Descriptive statistics

Standard deviation74.528716
Coefficient of variation (CV)7.4993276
Kurtosis224.04118
Mean9.9380531
Median Absolute Deviation (MAD)2
Skewness14.936445
Sum2246
Variance5554.5295
MonotonicityNot monotonic
2024-04-06T21:33:12.909549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 64
 
13.6%
3 29
 
6.1%
2 24
 
5.1%
5 19
 
4.0%
4 19
 
4.0%
10 12
 
2.5%
6 12
 
2.5%
7 9
 
1.9%
8 8
 
1.7%
14 5
 
1.1%
Other values (14) 25
 
5.3%
(Missing) 246
52.1%
ValueCountFrequency (%)
1 64
13.6%
2 24
 
5.1%
3 29
6.1%
4 19
 
4.0%
5 19
 
4.0%
6 12
 
2.5%
7 9
 
1.9%
8 8
 
1.7%
9 4
 
0.8%
10 12
 
2.5%
ValueCountFrequency (%)
1123 1
 
0.2%
29 1
 
0.2%
28 1
 
0.2%
21 1
 
0.2%
20 1
 
0.2%
19 1
 
0.2%
18 1
 
0.2%
17 2
0.4%
16 1
 
0.2%
15 4
0.8%

휘발유 (무연)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct389
Distinct (%)83.3%
Missing5
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean3907.7944
Minimum1
Maximum912470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:13.303420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q189
median399
Q32761.5
95-th percentile8377.8
Maximum912470
Range912469
Interquartile range (IQR)2672.5

Descriptive statistics

Standard deviation42257.06
Coefficient of variation (CV)10.813532
Kurtosis461.52548
Mean3907.7944
Median Absolute Deviation (MAD)372
Skewness21.421499
Sum1824940
Variance1.7856591 × 109
MonotonicityNot monotonic
2024-04-06T21:33:13.599801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 5
 
1.1%
31 4
 
0.8%
43 4
 
0.8%
34 4
 
0.8%
131 4
 
0.8%
42 4
 
0.8%
1 3
 
0.6%
13 3
 
0.6%
87 3
 
0.6%
174 3
 
0.6%
Other values (379) 430
91.1%
(Missing) 5
 
1.1%
ValueCountFrequency (%)
1 3
0.6%
2 1
 
0.2%
3 2
0.4%
4 2
0.4%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 2
0.4%
10 3
0.6%
11 1
 
0.2%
ValueCountFrequency (%)
912470 1
0.2%
20469 1
0.2%
18640 1
0.2%
18233 1
0.2%
17975 1
0.2%
17208 1
0.2%
16239 1
0.2%
15520 1
0.2%
13980 1
0.2%
11876 1
0.2%

CNG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70
Distinct (%)33.8%
Missing265
Missing (%)56.1%
Infinite0
Infinite (%)0.0%
Mean92.821256
Minimum1
Maximum9607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:13.875813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q322.5
95-th percentile284
Maximum9607
Range9606
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation672.0379
Coefficient of variation (CV)7.2401293
Kurtosis197.72912
Mean92.821256
Median Absolute Deviation (MAD)6
Skewness13.915103
Sum19214
Variance451634.93
MonotonicityNot monotonic
2024-04-06T21:33:14.724396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 34
 
7.2%
2 24
 
5.1%
3 12
 
2.5%
8 10
 
2.1%
10 10
 
2.1%
5 9
 
1.9%
6 8
 
1.7%
4 8
 
1.7%
11 7
 
1.5%
12 7
 
1.5%
Other values (60) 78
 
16.5%
(Missing) 265
56.1%
ValueCountFrequency (%)
1 34
7.2%
2 24
5.1%
3 12
 
2.5%
4 8
 
1.7%
5 9
 
1.9%
6 8
 
1.7%
7 2
 
0.4%
8 10
 
2.1%
9 4
 
0.8%
10 10
 
2.1%
ValueCountFrequency (%)
9607 1
0.2%
665 1
0.2%
659 1
0.2%
413 1
0.2%
373 1
0.2%
331 1
0.2%
312 1
0.2%
301 1
0.2%
299 1
0.2%
294 1
0.2%

하이브리드 (휘발유+전기)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct214
Distinct (%)47.7%
Missing23
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean350.90423
Minimum1
Maximum78778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:15.167987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median43
Q3208
95-th percentile713.8
Maximum78778
Range78777
Interquartile range (IQR)200

Descriptive statistics

Standard deviation3726.7952
Coefficient of variation (CV)10.620548
Kurtosis440.65495
Mean350.90423
Median Absolute Deviation (MAD)40
Skewness20.899438
Sum157556
Variance13889003
MonotonicityNot monotonic
2024-04-06T21:33:15.605634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 29
 
6.1%
3 19
 
4.0%
9 15
 
3.2%
5 14
 
3.0%
4 14
 
3.0%
7 12
 
2.5%
6 11
 
2.3%
10 9
 
1.9%
2 9
 
1.9%
8 7
 
1.5%
Other values (204) 310
65.7%
(Missing) 23
 
4.9%
ValueCountFrequency (%)
1 29
6.1%
2 9
 
1.9%
3 19
4.0%
4 14
3.0%
5 14
3.0%
6 11
 
2.3%
7 12
2.5%
8 7
 
1.5%
9 15
3.2%
10 9
 
1.9%
ValueCountFrequency (%)
78778 1
0.2%
4583 1
0.2%
2734 1
0.2%
1611 1
0.2%
1497 1
0.2%
1395 1
0.2%
1381 1
0.2%
1318 1
0.2%
1301 1
0.2%
1291 1
0.2%

하이브리드 (경유+전기)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
<NA>
444 
1
 
23
2
 
4
31
 
1

Length

Max length4
Median length4
Mean length3.8241525
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 444
94.1%
1 23
 
4.9%
2 4
 
0.8%
31 1
 
0.2%

Length

2024-04-06T21:33:15.894405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T21:33:16.105042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 444
94.1%
1 23
 
4.9%
2 4
 
0.8%
31 1
 
0.2%

하이브리드 (LPG+전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)12.7%
Missing212
Missing (%)44.9%
Infinite0
Infinite (%)0.0%
Mean14.915385
Minimum1
Maximum1939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:16.352950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q310
95-th percentile23
Maximum1939
Range1938
Interquartile range (IQR)8

Descriptive statistics

Standard deviation120.10462
Coefficient of variation (CV)8.0523986
Kurtosis257.23214
Mean14.915385
Median Absolute Deviation (MAD)3
Skewness15.997131
Sum3878
Variance14425.12
MonotonicityNot monotonic
2024-04-06T21:33:16.661714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 54
 
11.4%
2 40
 
8.5%
3 25
 
5.3%
4 15
 
3.2%
9 14
 
3.0%
10 12
 
2.5%
7 12
 
2.5%
5 12
 
2.5%
6 9
 
1.9%
12 8
 
1.7%
Other values (23) 59
 
12.5%
(Missing) 212
44.9%
ValueCountFrequency (%)
1 54
11.4%
2 40
8.5%
3 25
5.3%
4 15
 
3.2%
5 12
 
2.5%
6 9
 
1.9%
7 12
 
2.5%
8 7
 
1.5%
9 14
 
3.0%
10 12
 
2.5%
ValueCountFrequency (%)
1939 1
0.2%
60 1
0.2%
50 1
0.2%
49 1
0.2%
45 1
0.2%
42 1
0.2%
35 1
0.2%
30 1
0.2%
26 1
0.2%
25 2
0.4%

하이브리드 (CNG+전기)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)87.5%
Missing464
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean12.75
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:16.958056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.35
Q12
median3.5
Q313.75
95-th percentile44
Maximum51
Range50
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation18.359505
Coefficient of variation (CV)1.4399612
Kurtosis2.0788084
Mean12.75
Median Absolute Deviation (MAD)2
Skewness1.7307085
Sum102
Variance337.07143
MonotonicityNot monotonic
2024-04-06T21:33:17.199576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 2
 
0.4%
3 1
 
0.2%
8 1
 
0.2%
31 1
 
0.2%
1 1
 
0.2%
4 1
 
0.2%
51 1
 
0.2%
(Missing) 464
98.3%
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%
51 1
0.2%
ValueCountFrequency (%)
51 1
0.2%
31 1
0.2%
8 1
0.2%
4 1
0.2%
3 1
0.2%
2 2
0.4%
1 1
0.2%

수소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)8.0%
Missing384
Missing (%)81.4%
Infinite0
Infinite (%)0.0%
Mean3.25
Minimum1
Maximum143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:17.474267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3.65
Maximum143
Range142
Interquartile range (IQR)1

Descriptive statistics

Standard deviation15.170487
Coefficient of variation (CV)4.6678422
Kurtosis85.584197
Mean3.25
Median Absolute Deviation (MAD)0
Skewness9.1990548
Sum286
Variance230.14368
MonotonicityNot monotonic
2024-04-06T21:33:17.695879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 57
 
12.1%
2 22
 
4.7%
3 4
 
0.8%
5 2
 
0.4%
16 1
 
0.2%
4 1
 
0.2%
143 1
 
0.2%
(Missing) 384
81.4%
ValueCountFrequency (%)
1 57
12.1%
2 22
 
4.7%
3 4
 
0.8%
4 1
 
0.2%
5 2
 
0.4%
16 1
 
0.2%
143 1
 
0.2%
ValueCountFrequency (%)
143 1
 
0.2%
16 1
 
0.2%
5 2
 
0.4%
4 1
 
0.2%
3 4
 
0.8%
2 22
 
4.7%
1 57
12.1%

기타연료
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)20.2%
Missing140
Missing (%)29.7%
Infinite0
Infinite (%)0.0%
Mean34.331325
Minimum1
Maximum5699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T21:33:18.006211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q321.25
95-th percentile65.5
Maximum5699
Range5698
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation312.80113
Coefficient of variation (CV)9.1112455
Kurtosis327.85213
Mean34.331325
Median Absolute Deviation (MAD)7
Skewness18.05194
Sum11398
Variance97844.549
MonotonicityNot monotonic
2024-04-06T21:33:18.338954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 62
13.1%
2 36
 
7.6%
4 16
 
3.4%
5 16
 
3.4%
3 16
 
3.4%
20 12
 
2.5%
7 10
 
2.1%
8 9
 
1.9%
13 9
 
1.9%
19 8
 
1.7%
Other values (57) 138
29.2%
(Missing) 140
29.7%
ValueCountFrequency (%)
1 62
13.1%
2 36
7.6%
3 16
 
3.4%
4 16
 
3.4%
5 16
 
3.4%
6 6
 
1.3%
7 10
 
2.1%
8 9
 
1.9%
9 7
 
1.5%
10 6
 
1.3%
ValueCountFrequency (%)
5699 1
0.2%
193 1
0.2%
177 1
0.2%
131 1
0.2%
101 1
0.2%
98 1
0.2%
95 1
0.2%
94 1
0.2%
93 1
0.2%
90 1
0.2%

Interactions

2024-04-06T21:33:03.173949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:29.624990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:31.926152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:34.368517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:37.261031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:40.697970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:43.418561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:46.213391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:49.307881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:52.201499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:55.488927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:58.060806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:00.338005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:03.384146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:29.793823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:32.093156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:34.584197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:37.452261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:40.916721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:43.725881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:46.439215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:49.496979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:52.872429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:55.676399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:58.243510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:00.618309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:03.615861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:29.990438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:32.287952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:34.841833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:37.725350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:41.133202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:44.004099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:46.666022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:49.686513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:53.126572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:55.857430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:58.411321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:00.844252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:03.850434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:30.149378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:32.465084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:35.034174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:38.437401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:41.318503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:44.231997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:46.932315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:49.925085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:53.426733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:56.041391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:58.557695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:01.051323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:04.621687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:30.348169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:32.628840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:35.260934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:38.661949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:41.530437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:44.409524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:47.215325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:50.170662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:53.646192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:56.236489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:58.723786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:01.249351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:04.836279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:30.517005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:32.797596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:35.524568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:38.869931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:41.705791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:44.591776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:47.413666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:50.420860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:53.861052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:56.402880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:58.885320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:01.418206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:05.066371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:30.684306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:32.981289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:35.729399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:39.048632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:41.861160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:44.743882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:47.612570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:50.681351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:54.058998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:56.567816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:59.028250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:01.612612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:05.260725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:30.867033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:33.174096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:35.925478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:39.230363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:42.033047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:44.893277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:47.750280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:50.959689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:54.255113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:56.730321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:59.177589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:01.779784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:05.443931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:31.041126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:33.347712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:36.157095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:39.545973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:42.198545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:45.068252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:47.961380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:51.140828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:54.443999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:56.925029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:59.343813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:02.051000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:05.690471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:31.232116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:33.555285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:36.448104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:39.795612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:42.437186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:45.276476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:48.264739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:51.352372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:54.676711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:57.150782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:59.545371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:02.276028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:05.906842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:31.403426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:33.784795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:36.631298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:39.996196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:42.662419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:45.491839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:48.553205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:51.629107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:54.920821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:57.363175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:59.749636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:02.496549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:06.080351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:31.554626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:33.972996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:36.804930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:40.188929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:42.829078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:45.712827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:48.830097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:51.803281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:55.125222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:57.541637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:59.940061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:02.668369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:06.267559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:31.731889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:34.168168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:36.988708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:40.509576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:43.063531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:45.987056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:49.011631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:52.015932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:55.302763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:32:57.770427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:00.155316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T21:33:02.991262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T21:33:18.592818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동별 총 대수휘발유경유엘피지전기휘발유 (유연)휘발유 (무연)CNG하이브리드 (휘발유+전기)하이브리드 (경유+전기)하이브리드 (LPG+전기)하이브리드 (CNG+전기)수소기타연료
동별 총 대수1.0000.7040.7040.7041.0000.7010.7040.7000.7041.0000.7021.0001.0000.703
휘발유0.7041.0000.7040.7041.0000.7010.7040.7000.7041.0000.7021.0001.0000.703
경유0.7040.7041.0000.7041.0000.7010.7040.7000.7041.0000.7021.0001.0000.703
엘피지0.7040.7040.7041.0001.0000.7010.7040.7000.7041.0000.7021.0001.0000.703
전기1.0001.0001.0001.0001.0001.0001.0001.0001.0000.9321.0001.0000.9391.000
휘발유\n(유연)0.7010.7010.7010.7011.0001.0000.7010.6990.7011.0000.7001.0001.0000.700
휘발유\n(무연)0.7040.7040.7040.7041.0000.7011.0000.7000.7041.0000.7021.0001.0000.703
CNG0.7000.7000.7000.7001.0000.6990.7001.0000.7001.0000.6991.0001.0000.700
하이브리드\n(휘발유+전기)0.7040.7040.7040.7041.0000.7010.7040.7001.0001.0000.7021.0001.0000.703
하이브리드\n(경유+전기)1.0001.0001.0001.0000.9321.0001.0001.0001.0001.0001.0000.0001.0001.000
하이브리드\n(LPG+전기)0.7020.7020.7020.7021.0000.7000.7020.6990.7021.0001.0001.0001.0000.701
하이브리드\n(CNG+전기)1.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0000.0001.000
수소1.0001.0001.0001.0000.9391.0001.0001.0001.0001.0001.0000.0001.0001.000
기타연료0.7030.7030.7030.7031.0000.7000.7030.7000.7031.0000.7011.0001.0001.000
2024-04-06T21:33:18.890872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동별 총 대수휘발유경유엘피지전기휘발유 (유연)휘발유 (무연)CNG하이브리드 (휘발유+전기)하이브리드 (LPG+전기)하이브리드 (CNG+전기)수소기타연료하이브리드 (경유+전기)
동별 총 대수1.0000.9910.9950.9690.8410.8540.9940.5460.9720.8680.8980.1670.8420.981
휘발유0.9911.0000.9760.9500.8370.8440.9900.4970.9770.8350.8140.2150.8330.981
경유0.9950.9761.0000.9660.8440.8500.9830.5560.9570.8770.9220.1480.8400.981
엘피지0.9690.9500.9661.0000.7860.8000.9650.5670.9300.8660.9220.0500.7730.981
전기0.8410.8370.8440.7861.0000.7590.8250.4930.8500.7270.7310.1920.7330.680
휘발유\n(유연)0.8540.8440.8500.8000.7591.0000.8550.4930.8420.7410.8270.1220.7330.978
휘발유\n(무연)0.9940.9900.9830.9650.8250.8551.0000.5340.9710.8670.8140.1800.8340.981
CNG0.5460.4970.5560.5670.4930.4930.5341.0000.4960.4980.7660.0500.4750.976
하이브리드\n(휘발유+전기)0.9720.9770.9570.9300.8500.8420.9710.4961.0000.8340.8140.2400.8260.981
하이브리드\n(LPG+전기)0.8680.8350.8770.8660.7270.7410.8670.4980.8341.0000.9340.1920.7390.979
하이브리드\n(CNG+전기)0.8980.8140.9220.9220.7310.8270.8140.7660.8140.9341.0001.0000.8141.000
수소0.1670.2150.1480.0500.1920.1220.1800.0500.2400.1921.0001.0000.2110.957
기타연료0.8420.8330.8400.7730.7330.7330.8340.4750.8260.7390.8140.2111.0000.980
하이브리드\n(경유+전기)0.9810.9810.9810.9810.6800.9780.9810.9760.9810.9791.0000.9570.9801.000

Missing values

2024-04-06T21:33:06.635838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T21:33:07.074833image/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.
2024-04-06T21:33:07.443308image/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

사용본거지법정동명동별 총 대수휘발유경유엘피지전기휘발유 (유연)휘발유 (무연)CNG하이브리드 (휘발유+전기)하이브리드 (경유+전기)하이브리드 (LPG+전기)하이브리드 (CNG+전기)수소기타연료
0서울특별시 종로구 청운동7642312442641229<NA>27<NA>1<NA><NA>1
1서울특별시 종로구 신교동36083128212<NA>116<NA>10<NA><NA><NA><NA><NA>
2서울특별시 종로구 궁정동4811162<NA><NA>17<NA>2<NA><NA><NA><NA><NA>
3서울특별시 종로구 효자동208647981<NA>50<NA>6<NA><NA><NA><NA><NA>
4서울특별시 종로구 창성동13841436<NA><NA>43<NA>5<NA><NA><NA><NA><NA>
5서울특별시 종로구 통의동15534637<NA><NA>48<NA>3<NA><NA><NA><NA><NA>
6서울특별시 종로구 적선동19042846<NA><NA>51<NA>7<NA><NA><NA><NA><NA>
7서울특별시 종로구 통인동214626914<NA><NA>65<NA>4<NA><NA><NA><NA><NA>
8서울특별시 종로구 누상동8271822907211255<NA>23<NA><NA><NA><NA>3
9서울특별시 종로구 누하동213528514<NA><NA>58<NA>4<NA><NA><NA><NA><NA>
사용본거지법정동명동별 총 대수휘발유경유엘피지전기휘발유 (유연)휘발유 (무연)CNG하이브리드 (휘발유+전기)하이브리드 (경유+전기)하이브리드 (LPG+전기)하이브리드 (CNG+전기)수소기타연료
462서울특별시 강동구 명일동125782967413093714541625339<NA>5<NA><NA>14
463서울특별시 강동구 고덕동114392667409087828334135326<NA>9<NA><NA>20
464서울특별시 강동구 상일동6402135124025977318483170<NA>4<NA><NA>17
465서울특별시 강동구 길동15830287356412796186416010303<NA>4<NA><NA>19
466서울특별시 강동구 둔촌동954220153368108815128063225<NA>7<NA>113
467서울특별시 강동구 암사동2164145887688215919866045520<NA>19<NA>130
468서울특별시 강동구 성내동22477432585112413591066201946416<NA>148
469서울특별시 강동구 천호동270775325105703009201075365529<NA>16<NA><NA>57
470서울특별시 강동구 강일동80321449276411452132240197181<NA>92<NA>21
471합계31242896840281140532279713101751123912470960778778311939511435699