Overview

Dataset statistics

Number of variables16
Number of observations3570
Missing cells64
Missing cells (%)0.1%
Duplicate rows14
Duplicate rows (%)0.4%
Total size in memory491.7 KiB
Average record size in memory141.0 B

Variable types

Categorical2
Text1
Numeric13

Dataset

Description서울시 자치구별 연료별 자동차 등록 현황 자료입니다. 사용보거지, 행정동, 연료, 자동차 종류, 자가용: 승용/승합/화물/특수, 영업용 : 승용/승합/화물/특수 구분으로 정보를 제공합니다.
Author서울특별시
URLhttps://www.data.go.kr/data/15100168/fileData.do

Alerts

Dataset has 14 (0.4%) duplicate rowsDuplicates
is highly overall correlated with 자가용승합 and 3 other fieldsHigh correlation
관용_화물 is highly overall correlated with 관용_특수High correlation
관용_특수 is highly overall correlated with 관용_화물High correlation
자가용_승용 is highly overall correlated with 자가용화물 and 3 other fieldsHigh correlation
자가용승합 is highly overall correlated with High correlation
자가용화물 is highly overall correlated with and 4 other fieldsHigh correlation
자가용특수 is highly overall correlated with and 4 other fieldsHigh correlation
영업용_승용 is highly overall correlated with 자가용_승용 and 3 other fieldsHigh correlation
영업용승합 is highly overall correlated with and 1 other fieldsHigh correlation
영업용특수 is highly overall correlated with 자가용_승용 and 4 other fieldsHigh correlation
has 64 (1.8%) missing valuesMissing
관용_승용 is highly skewed (γ1 = 40.15372204)Skewed
관용_승합 is highly skewed (γ1 = 55.81564972)Skewed
관용_화물 is highly skewed (γ1 = 52.90194054)Skewed
관용_승용 has 3470 (97.2%) zerosZeros
관용_승합 has 2966 (83.1%) zerosZeros
관용_화물 has 3303 (92.5%) zerosZeros
관용_특수 has 3236 (90.6%) zerosZeros
자가용_승용 has 2776 (77.8%) zerosZeros
자가용승합 has 537 (15.0%) zerosZeros
자가용화물 has 1853 (51.9%) zerosZeros
자가용특수 has 1309 (36.7%) zerosZeros
영업용_승용 has 3162 (88.6%) zerosZeros
영업용승합 has 2118 (59.3%) zerosZeros
영업용화물 has 2932 (82.1%) zerosZeros
영업용특수 has 2182 (61.1%) zerosZeros

Reproduction

Analysis started2023-12-12 09:36:08.730271
Analysis finished2023-12-12 09:36:31.266608
Duration22.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct25
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
서울특별시 송파구
 
230
서울특별시 강남구
 
194
서울특별시 강서구
 
168
서울특별시 강동구
 
165
서울특별시 관악구
 
165
Other values (20)
2648 

Length

Max length10
Median length9
Mean length9.070028
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 종로구
2nd row서울특별시 종로구
3rd row서울특별시 종로구
4th row서울특별시 종로구
5th row서울특별시 종로구

Common Values

ValueCountFrequency (%)
서울특별시 송파구 230
 
6.4%
서울특별시 강남구 194
 
5.4%
서울특별시 강서구 168
 
4.7%
서울특별시 강동구 165
 
4.6%
서울특별시 관악구 165
 
4.6%
서울특별시 성북구 158
 
4.4%
서울특별시 영등포구 158
 
4.4%
서울특별시 노원구 155
 
4.3%
서울특별시 마포구 152
 
4.3%
서울특별시 서초구 150
 
4.2%
Other values (15) 1875
52.5%

Length

2023-12-12T18:36:31.358698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 3570
50.0%
송파구 230
 
3.2%
강남구 194
 
2.7%
강서구 168
 
2.4%
강동구 165
 
2.3%
관악구 165
 
2.3%
성북구 158
 
2.2%
영등포구 158
 
2.2%
노원구 155
 
2.2%
마포구 152
 
2.1%
Other values (16) 2025
28.4%
Distinct517
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2023-12-12T18:36:31.760841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length9
Mean length8.7277311
Min length5

Characters and Unicode

Total characters31158
Distinct characters195
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

Unique26 ?
Unique (%)0.7%

Sample

1st row종로구 기타
2nd row종로구 기타
3rd row종로구 기타
4th row종로구 기타
5th row종로구 기타
ValueCountFrequency (%)
송파구 230
 
3.2%
강남구 194
 
2.7%
강서구 168
 
2.4%
관악구 165
 
2.3%
강동구 165
 
2.3%
성북구 158
 
2.2%
노원구 155
 
2.2%
마포구 152
 
2.1%
서초구 150
 
2.1%
영등포구 146
 
2.0%
Other values (471) 5458
76.4%
2023-12-12T18:36:32.319237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6904
22.2%
3894
 
12.5%
3793
 
12.2%
1 875
 
2.8%
2 779
 
2.5%
680
 
2.2%
508
 
1.6%
426
 
1.4%
386
 
1.2%
370
 
1.2%
Other values (185) 12543
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21725
69.7%
Space Separator 6904
 
22.2%
Decimal Number 2449
 
7.9%
Other Punctuation 80
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3894
 
17.9%
3793
 
17.5%
680
 
3.1%
508
 
2.3%
426
 
2.0%
386
 
1.8%
370
 
1.7%
369
 
1.7%
333
 
1.5%
319
 
1.5%
Other values (173) 10647
49.0%
Decimal Number
ValueCountFrequency (%)
1 875
35.7%
2 779
31.8%
3 353
14.4%
4 211
 
8.6%
5 87
 
3.6%
6 59
 
2.4%
7 46
 
1.9%
8 23
 
0.9%
0 8
 
0.3%
9 8
 
0.3%
Space Separator
ValueCountFrequency (%)
6904
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21725
69.7%
Common 9433
30.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3894
 
17.9%
3793
 
17.5%
680
 
3.1%
508
 
2.3%
426
 
2.0%
386
 
1.8%
370
 
1.7%
369
 
1.7%
333
 
1.5%
319
 
1.5%
Other values (173) 10647
49.0%
Common
ValueCountFrequency (%)
6904
73.2%
1 875
 
9.3%
2 779
 
8.3%
3 353
 
3.7%
4 211
 
2.2%
5 87
 
0.9%
. 80
 
0.8%
6 59
 
0.6%
7 46
 
0.5%
8 23
 
0.2%
Other values (2) 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21725
69.7%
ASCII 9433
30.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6904
73.2%
1 875
 
9.3%
2 779
 
8.3%
3 353
 
3.7%
4 211
 
2.2%
5 87
 
0.9%
. 80
 
0.8%
6 59
 
0.6%
7 46
 
0.5%
8 23
 
0.2%
Other values (2) 16
 
0.2%
Hangul
ValueCountFrequency (%)
3894
 
17.9%
3793
 
17.5%
680
 
3.1%
508
 
2.3%
426
 
2.0%
386
 
1.8%
370
 
1.7%
369
 
1.7%
333
 
1.5%
319
 
1.5%
Other values (173) 10647
49.0%

연료
Categorical

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
경유
551 
휘발유
549 
엘피지
463 
하이브리드
436 
기타연료
435 
Other values (4)
1136 

Length

Max length5
Median length4
Mean length2.9809524
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row휘발유
2nd row휘발유
3rd row휘발유
4th row경유
5th row경유

Common Values

ValueCountFrequency (%)
경유 551
15.4%
휘발유 549
15.4%
엘피지 463
13.0%
하이브리드 436
12.2%
기타연료 435
12.2%
전기 428
12.0%
수소 396
11.1%
CNG 311
8.7%
LNG 1
 
< 0.1%

Length

2023-12-12T18:36:32.484210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:36:32.633212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경유 551
15.4%
휘발유 549
15.4%
엘피지 463
13.0%
하이브리드 436
12.2%
기타연료 435
12.2%
전기 428
12.0%
수소 396
11.1%
cng 311
8.7%
lng 1
 
< 0.1%


Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1404
Distinct (%)40.0%
Missing64
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean907.62151
Minimum1
Maximum12076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:32.805945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median116.5
Q3958.25
95-th percentile4372.25
Maximum12076
Range12075
Interquartile range (IQR)948.25

Descriptive statistics

Standard deviation1588.3971
Coefficient of variation (CV)1.7500655
Kurtosis6.6808871
Mean907.62151
Median Absolute Deviation (MAD)114.5
Skewness2.4179426
Sum3182121
Variance2523005.3
MonotonicityNot monotonic
2023-12-12T18:36:32.968412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 228
 
6.4%
2 141
 
3.9%
3 101
 
2.8%
4 94
 
2.6%
6 73
 
2.0%
5 70
 
2.0%
8 60
 
1.7%
7 55
 
1.5%
9 51
 
1.4%
10 46
 
1.3%
Other values (1394) 2587
72.5%
(Missing) 64
 
1.8%
ValueCountFrequency (%)
1 228
6.4%
2 141
3.9%
3 101
2.8%
4 94
2.6%
5 70
 
2.0%
6 73
 
2.0%
7 55
 
1.5%
8 60
 
1.7%
9 51
 
1.4%
10 46
 
1.3%
ValueCountFrequency (%)
12076 1
< 0.1%
11243 1
< 0.1%
10487 1
< 0.1%
10093 1
< 0.1%
9903 1
< 0.1%
9883 1
< 0.1%
9317 1
< 0.1%
9078 1
< 0.1%
8947 1
< 0.1%
8856 1
< 0.1%

관용_승용
Real number (ℝ)

SKEWED  ZEROS 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11148459
Minimum0
Maximum75
Zeros3470
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:33.124042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum75
Range75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4480477
Coefficient of variation (CV)12.988769
Kurtosis2013.899
Mean0.11148459
Median Absolute Deviation (MAD)0
Skewness40.153722
Sum398
Variance2.096842
MonotonicityNot monotonic
2023-12-12T18:36:33.256793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 3470
97.2%
1 38
 
1.1%
2 23
 
0.6%
4 7
 
0.2%
5 7
 
0.2%
6 6
 
0.2%
7 5
 
0.1%
9 4
 
0.1%
3 4
 
0.1%
10 3
 
0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
0 3470
97.2%
1 38
 
1.1%
2 23
 
0.6%
3 4
 
0.1%
4 7
 
0.2%
5 7
 
0.2%
6 6
 
0.2%
7 5
 
0.1%
9 4
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
75 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
10 3
0.1%
9 4
0.1%
7 5
0.1%
6 6
0.2%
5 7
0.2%
4 7
0.2%
3 4
0.1%

관용_승합
Real number (ℝ)

SKEWED  ZEROS 

Distinct44
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2966387
Minimum0
Maximum1283
Zeros2966
Zeros (%)83.1%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:33.385770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum1283
Range1283
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.962479
Coefficient of variation (CV)16.93801
Kurtosis3252.6934
Mean1.2966387
Median Absolute Deviation (MAD)0
Skewness55.81565
Sum4629
Variance482.35048
MonotonicityNot monotonic
2023-12-12T18:36:33.528163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 2966
83.1%
1 268
 
7.5%
2 86
 
2.4%
3 46
 
1.3%
4 33
 
0.9%
5 22
 
0.6%
6 21
 
0.6%
8 16
 
0.4%
7 16
 
0.4%
10 13
 
0.4%
Other values (34) 83
 
2.3%
ValueCountFrequency (%)
0 2966
83.1%
1 268
 
7.5%
2 86
 
2.4%
3 46
 
1.3%
4 33
 
0.9%
5 22
 
0.6%
6 21
 
0.6%
7 16
 
0.4%
8 16
 
0.4%
9 9
 
0.3%
ValueCountFrequency (%)
1283 1
 
< 0.1%
85 1
 
< 0.1%
84 1
 
< 0.1%
74 1
 
< 0.1%
67 1
 
< 0.1%
65 1
 
< 0.1%
63 1
 
< 0.1%
61 1
 
< 0.1%
43 4
0.1%
42 2
0.1%

관용_화물
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct33
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0546218
Minimum0
Maximum1514
Zeros3303
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:33.665104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1514
Range1514
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.517029
Coefficient of variation (CV)25.143637
Kurtosis2980.5987
Mean1.0546218
Median Absolute Deviation (MAD)0
Skewness52.901941
Sum3765
Variance703.1528
MonotonicityNot monotonic
2023-12-12T18:36:33.808856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 3303
92.5%
1 97
 
2.7%
2 44
 
1.2%
3 23
 
0.6%
5 17
 
0.5%
4 16
 
0.4%
10 7
 
0.2%
12 6
 
0.2%
9 5
 
0.1%
18 5
 
0.1%
Other values (23) 47
 
1.3%
ValueCountFrequency (%)
0 3303
92.5%
1 97
 
2.7%
2 44
 
1.2%
3 23
 
0.6%
4 16
 
0.4%
5 17
 
0.5%
6 3
 
0.1%
7 5
 
0.1%
8 3
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
1514 1
< 0.1%
338 1
< 0.1%
239 1
< 0.1%
134 1
< 0.1%
79 1
< 0.1%
62 1
< 0.1%
59 1
< 0.1%
33 2
0.1%
30 1
< 0.1%
29 2
0.1%

관용_특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0893557
Minimum0
Maximum198
Zeros3236
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:33.959739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum198
Range198
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.6927641
Coefficient of variation (CV)7.0617557
Kurtosis207.98874
Mean1.0893557
Median Absolute Deviation (MAD)0
Skewness12.652341
Sum3889
Variance59.17862
MonotonicityNot monotonic
2023-12-12T18:36:34.097595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3236
90.6%
1 100
 
2.8%
2 42
 
1.2%
3 24
 
0.7%
4 19
 
0.5%
6 16
 
0.4%
5 14
 
0.4%
10 9
 
0.3%
9 9
 
0.3%
12 9
 
0.3%
Other values (45) 92
 
2.6%
ValueCountFrequency (%)
0 3236
90.6%
1 100
 
2.8%
2 42
 
1.2%
3 24
 
0.7%
4 19
 
0.5%
5 14
 
0.4%
6 16
 
0.4%
7 8
 
0.2%
8 8
 
0.2%
9 9
 
0.3%
ValueCountFrequency (%)
198 1
< 0.1%
123 1
< 0.1%
112 1
< 0.1%
97 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
92 1
< 0.1%
88 1
< 0.1%
85 2
0.1%
83 2
0.1%

자가용_승용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3056022
Minimum0
Maximum209
Zeros2776
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:34.225695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum209
Range209
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.8091817
Coefficient of variation (CV)4.4494269
Kurtosis524.08891
Mean1.3056022
Median Absolute Deviation (MAD)0
Skewness18.062061
Sum4661
Variance33.746592
MonotonicityNot monotonic
2023-12-12T18:36:34.340375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 2776
77.8%
1 241
 
6.8%
2 97
 
2.7%
3 89
 
2.5%
5 60
 
1.7%
4 59
 
1.7%
6 41
 
1.1%
7 38
 
1.1%
8 29
 
0.8%
10 22
 
0.6%
Other values (33) 118
 
3.3%
ValueCountFrequency (%)
0 2776
77.8%
1 241
 
6.8%
2 97
 
2.7%
3 89
 
2.5%
4 59
 
1.7%
5 60
 
1.7%
6 41
 
1.1%
7 38
 
1.1%
8 29
 
0.8%
9 18
 
0.5%
ValueCountFrequency (%)
209 1
< 0.1%
105 1
< 0.1%
95 1
< 0.1%
82 1
< 0.1%
58 1
< 0.1%
50 1
< 0.1%
45 1
< 0.1%
42 1
< 0.1%
40 2
0.1%
39 1
< 0.1%

자가용승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1284
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean733.25294
Minimum0
Maximum11539
Zeros537
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:34.464208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median60
Q3623.25
95-th percentile3879.65
Maximum11539
Range11539
Interquartile range (IQR)621.25

Descriptive statistics

Standard deviation1434.6826
Coefficient of variation (CV)1.9565999
Kurtosis9.963935
Mean733.25294
Median Absolute Deviation (MAD)60
Skewness2.9005016
Sum2617713
Variance2058314.3
MonotonicityNot monotonic
2023-12-12T18:36:34.594155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 537
 
15.0%
1 355
 
9.9%
2 134
 
3.8%
3 98
 
2.7%
4 63
 
1.8%
5 47
 
1.3%
7 40
 
1.1%
6 38
 
1.1%
8 32
 
0.9%
9 26
 
0.7%
Other values (1274) 2200
61.6%
ValueCountFrequency (%)
0 537
15.0%
1 355
9.9%
2 134
 
3.8%
3 98
 
2.7%
4 63
 
1.8%
5 47
 
1.3%
6 38
 
1.1%
7 40
 
1.1%
8 32
 
0.9%
9 26
 
0.7%
ValueCountFrequency (%)
11539 1
< 0.1%
11121 1
< 0.1%
10387 1
< 0.1%
10023 1
< 0.1%
9859 1
< 0.1%
9621 1
< 0.1%
9303 1
< 0.1%
8990 1
< 0.1%
8893 1
< 0.1%
8602 1
< 0.1%

자가용화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.37479
Minimum0
Maximum673
Zeros1853
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:34.702645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile150
Maximum673
Range673
Interquartile range (IQR)7

Descriptive statistics

Standard deviation56.714724
Coefficient of variation (CV)2.5347601
Kurtosis17.213418
Mean22.37479
Median Absolute Deviation (MAD)0
Skewness3.6613012
Sum79878
Variance3216.56
MonotonicityNot monotonic
2023-12-12T18:36:35.114794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1853
51.9%
1 279
 
7.8%
2 199
 
5.6%
3 143
 
4.0%
4 83
 
2.3%
5 59
 
1.7%
7 32
 
0.9%
6 32
 
0.9%
25 22
 
0.6%
11 22
 
0.6%
Other values (245) 846
23.7%
ValueCountFrequency (%)
0 1853
51.9%
1 279
 
7.8%
2 199
 
5.6%
3 143
 
4.0%
4 83
 
2.3%
5 59
 
1.7%
6 32
 
0.9%
7 32
 
0.9%
8 18
 
0.5%
9 18
 
0.5%
ValueCountFrequency (%)
673 1
< 0.1%
535 1
< 0.1%
430 1
< 0.1%
408 1
< 0.1%
397 1
< 0.1%
396 1
< 0.1%
386 1
< 0.1%
384 1
< 0.1%
378 1
< 0.1%
373 1
< 0.1%

자가용특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct428
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.32577
Minimum0
Maximum5606
Zeros1309
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:35.244900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q317
95-th percentile519.65
Maximum5606
Range5606
Interquartile range (IQR)17

Descriptive statistics

Standard deviation229.26601
Coefficient of variation (CV)3.1266771
Kurtosis113.52346
Mean73.32577
Median Absolute Deviation (MAD)3
Skewness7.3934596
Sum261773
Variance52562.902
MonotonicityNot monotonic
2023-12-12T18:36:35.365794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1309
36.7%
1 164
 
4.6%
2 158
 
4.4%
3 155
 
4.3%
4 148
 
4.1%
5 142
 
4.0%
7 107
 
3.0%
6 106
 
3.0%
8 80
 
2.2%
9 72
 
2.0%
Other values (418) 1129
31.6%
ValueCountFrequency (%)
0 1309
36.7%
1 164
 
4.6%
2 158
 
4.4%
3 155
 
4.3%
4 148
 
4.1%
5 142
 
4.0%
6 106
 
3.0%
7 107
 
3.0%
8 80
 
2.2%
9 72
 
2.0%
ValueCountFrequency (%)
5606 1
< 0.1%
2636 1
< 0.1%
2500 1
< 0.1%
1908 1
< 0.1%
1871 1
< 0.1%
1713 1
< 0.1%
1684 1
< 0.1%
1657 1
< 0.1%
1581 1
< 0.1%
1535 1
< 0.1%

영업용_승용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4635854
Minimum0
Maximum233
Zeros3162
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:35.480166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.55
Maximum233
Range233
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.8129875
Coefficient of variation (CV)6.0215054
Kurtosis352.56987
Mean1.4635854
Median Absolute Deviation (MAD)0
Skewness16.36995
Sum5225
Variance77.668749
MonotonicityNot monotonic
2023-12-12T18:36:35.597095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3162
88.6%
3 40
 
1.1%
1 36
 
1.0%
5 36
 
1.0%
2 26
 
0.7%
6 25
 
0.7%
4 24
 
0.7%
7 22
 
0.6%
9 20
 
0.6%
8 20
 
0.6%
Other values (41) 159
 
4.5%
ValueCountFrequency (%)
0 3162
88.6%
1 36
 
1.0%
2 26
 
0.7%
3 40
 
1.1%
4 24
 
0.7%
5 36
 
1.0%
6 25
 
0.7%
7 22
 
0.6%
8 20
 
0.6%
9 20
 
0.6%
ValueCountFrequency (%)
233 1
< 0.1%
220 1
< 0.1%
195 1
< 0.1%
149 1
< 0.1%
137 1
< 0.1%
105 1
< 0.1%
80 1
< 0.1%
76 1
< 0.1%
70 1
< 0.1%
59 1
< 0.1%

영업용승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct298
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.119888
Minimum0
Maximum5208
Zeros2118
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:35.716815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile167.55
Maximum5208
Range5208
Interquartile range (IQR)7

Descriptive statistics

Standard deviation164.19753
Coefficient of variation (CV)4.6753431
Kurtosis363.54516
Mean35.119888
Median Absolute Deviation (MAD)0
Skewness15.363448
Sum125378
Variance26960.827
MonotonicityNot monotonic
2023-12-12T18:36:35.852053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2118
59.3%
1 197
 
5.5%
2 100
 
2.8%
4 64
 
1.8%
3 63
 
1.8%
6 48
 
1.3%
5 48
 
1.3%
7 43
 
1.2%
11 35
 
1.0%
8 33
 
0.9%
Other values (288) 821
 
23.0%
ValueCountFrequency (%)
0 2118
59.3%
1 197
 
5.5%
2 100
 
2.8%
3 63
 
1.8%
4 64
 
1.8%
5 48
 
1.3%
6 48
 
1.3%
7 43
 
1.2%
8 33
 
0.9%
9 22
 
0.6%
ValueCountFrequency (%)
5208 1
< 0.1%
3150 1
< 0.1%
2901 1
< 0.1%
2340 1
< 0.1%
1558 1
< 0.1%
1527 1
< 0.1%
1495 1
< 0.1%
1488 1
< 0.1%
1482 1
< 0.1%
1343 1
< 0.1%

영업용화물
Real number (ℝ)

ZEROS 

Distinct107
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2140056
Minimum0
Maximum564
Zeros2932
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:35.961265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16
Maximum564
Range564
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.276756
Coefficient of variation (CV)5.5236652
Kurtosis191.77333
Mean4.2140056
Median Absolute Deviation (MAD)0
Skewness11.639714
Sum15044
Variance541.80737
MonotonicityNot monotonic
2023-12-12T18:36:36.082884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2932
82.1%
1 153
 
4.3%
2 68
 
1.9%
3 36
 
1.0%
4 31
 
0.9%
5 23
 
0.6%
6 23
 
0.6%
8 19
 
0.5%
10 16
 
0.4%
9 16
 
0.4%
Other values (97) 253
 
7.1%
ValueCountFrequency (%)
0 2932
82.1%
1 153
 
4.3%
2 68
 
1.9%
3 36
 
1.0%
4 31
 
0.9%
5 23
 
0.6%
6 23
 
0.6%
7 15
 
0.4%
8 19
 
0.5%
9 16
 
0.4%
ValueCountFrequency (%)
564 1
< 0.1%
481 1
< 0.1%
339 1
< 0.1%
293 1
< 0.1%
284 1
< 0.1%
282 1
< 0.1%
264 1
< 0.1%
213 1
< 0.1%
208 1
< 0.1%
202 1
< 0.1%

영업용특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct204
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.779272
Minimum0
Maximum1418
Zeros2182
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-12T18:36:36.208828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile87
Maximum1418
Range1418
Interquartile range (IQR)7

Descriptive statistics

Standard deviation67.228853
Coefficient of variation (CV)4.006661
Kurtosis160.32726
Mean16.779272
Median Absolute Deviation (MAD)0
Skewness10.641063
Sum59902
Variance4519.7187
MonotonicityNot monotonic
2023-12-12T18:36:36.340727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2182
61.1%
1 120
 
3.4%
2 92
 
2.6%
3 79
 
2.2%
4 69
 
1.9%
6 68
 
1.9%
7 64
 
1.8%
5 61
 
1.7%
9 49
 
1.4%
11 47
 
1.3%
Other values (194) 739
 
20.7%
ValueCountFrequency (%)
0 2182
61.1%
1 120
 
3.4%
2 92
 
2.6%
3 79
 
2.2%
4 69
 
1.9%
5 61
 
1.7%
6 68
 
1.9%
7 64
 
1.8%
8 37
 
1.0%
9 49
 
1.4%
ValueCountFrequency (%)
1418 1
< 0.1%
1371 1
< 0.1%
1007 1
< 0.1%
968 1
< 0.1%
944 1
< 0.1%
882 1
< 0.1%
866 1
< 0.1%
756 1
< 0.1%
638 1
< 0.1%
589 1
< 0.1%

Interactions

2023-12-12T18:36:29.502238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:11.134943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:12.706213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:14.164099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.935106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.374394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.891563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.146681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:21.982490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:23.453370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.881395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:26.191686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.711377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:29.648600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:11.248271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:12.840578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:14.259049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.041972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.497780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.987367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.262470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.099702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:23.563855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.975845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:26.305316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.820087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:29.768081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:11.374469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:12.948369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:14.354901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.150707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.624259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.088754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.359357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.217862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:23.679364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.074105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:26.466133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.937938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:29.879678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:11.492956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.079615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:14.452455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.252601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.753629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.193612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.453389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.329296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:23.797208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.175710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:26.599699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:28.060564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.007790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:11.623043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.188821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:14.575400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.375340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.858097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.292154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.582735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.437246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:23.927600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.278187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:26.717904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:28.200694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.136693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:11.739662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.313583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.006022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.482125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.971938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.409127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.723641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.560147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.059062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.398681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:26.846311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:28.330394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.233136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:11.847797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.423621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.105260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.576760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.071692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.487985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.845715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.657805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.154503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.495944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:26.948478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:28.426893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.326414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:11.969347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.533868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.210110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.688107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.205685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.593341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.963583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.766697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.260908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.590172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.056827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:28.544816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.416376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:12.094740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.647497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.335990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.816966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.323648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.688365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:21.088717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.872117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.384439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.682138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.174223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:28.650788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.524058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:12.205541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.747550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.469023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:16.928153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.454423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.776310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:21.228104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:22.974496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.487864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.786747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.272668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:29.046527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.630388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:12.323470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.862723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.572546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.039241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.578598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.851878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:21.324135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:23.072225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.574492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.877993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.368679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:29.151136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.733261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:12.447593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:13.972331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.708182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.159968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.677663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:19.936479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:21.443636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:23.183717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.668104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:25.973174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.469066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:29.243652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:30.827816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:12.571411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:14.065933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:15.831570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:17.265534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:18.774721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:20.026029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:21.881147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:23.323031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:24.770955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:26.071828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:27.575988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:36:29.356233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:36:36.437388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용본거지 시군구연료관용_승용관용_승합관용_화물관용_특수자가용_승용자가용승합자가용화물자가용특수영업용_승용영업용승합영업용화물영업용특수
사용본거지 시군구1.0000.0000.1560.0000.0160.0150.0000.0000.0590.1240.0980.0570.0000.0000.000
연료0.0001.0000.5810.1420.0000.0000.1600.1370.5940.6870.4080.2430.1230.2720.372
0.1560.5811.0000.1040.0870.0000.1620.2710.9800.5820.4930.1960.2430.0500.396
관용_승용0.0000.1420.1041.0000.0000.6760.8490.1620.0640.2380.1080.1440.0000.0000.000
관용_승합0.0160.0000.0870.0001.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.000
관용_화물0.0150.0000.0000.6760.0001.0000.9170.1720.0000.2950.0000.4250.0000.0000.000
관용_특수0.0000.1600.1620.8490.0000.9171.0000.1820.1360.4100.4450.2150.0000.0750.320
자가용_승용0.0000.1370.2710.1620.0000.1720.1821.0000.1810.6020.5380.7380.0000.1980.578
자가용승합0.0590.5940.9800.0640.0680.0000.1360.1811.0000.5100.4020.1800.0000.0000.267
자가용화물0.1240.6870.5820.2380.0000.2950.4100.6020.5101.0000.8020.7240.0000.1880.851
자가용특수0.0980.4080.4930.1080.0000.0000.4450.5380.4020.8021.0000.4580.0000.1280.746
영업용_승용0.0570.2430.1960.1440.0000.4250.2150.7380.1800.7240.4581.0000.0000.0900.720
영업용승합0.0000.1230.2430.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
영업용화물0.0000.2720.0500.0000.0000.0000.0750.1980.0000.1880.1280.0900.0001.0000.251
영업용특수0.0000.3720.3960.0000.0000.0000.3200.5780.2670.8510.7460.7200.0000.2511.000
2023-12-12T18:36:36.568024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료사용본거지 시군구
연료1.0000.000
사용본거지 시군구0.0001.000
2023-12-12T18:36:36.658340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관용_승용관용_승합관용_화물관용_특수자가용_승용자가용승합자가용화물자가용특수영업용_승용영업용승합영업용화물영업용특수사용본거지 시군구연료
1.0000.1640.3550.2650.2190.2750.9230.5820.6020.4370.5110.2960.4170.0550.324
관용_승용0.1641.0000.2760.4110.4740.2690.1360.2060.2240.3320.0660.2090.2510.0000.062
관용_승합0.3550.2761.0000.2290.2100.0110.3660.0660.1170.1120.1900.0290.0570.0140.000
관용_화물0.2650.4110.2291.0000.5150.3430.2350.3690.3510.4640.1630.3120.3680.0080.000
관용_특수0.2190.4740.2100.5151.0000.3050.1700.3230.3320.4080.1680.2920.3590.0000.079
자가용_승용0.2750.2690.0110.3430.3051.0000.1160.6360.6040.706-0.0130.3150.5110.0000.072
자가용승합0.9230.1360.3660.2350.1700.1161.0000.4260.4330.4100.4960.1990.3380.0210.320
자가용화물0.5820.2060.0660.3690.3230.6360.4261.0000.8700.5880.3760.3680.6200.0470.282
자가용특수0.6020.2240.1170.3510.3320.6040.4330.8701.0000.5590.4800.3490.7490.0440.216
영업용_승용0.4370.3320.1120.4640.4080.7060.4100.5880.5591.0000.0940.4440.6100.0220.080
영업용승합0.5110.0660.1900.1630.168-0.0130.4960.3760.4800.0941.0000.2760.5160.0000.065
영업용화물0.2960.2090.0290.3120.2920.3150.1990.3680.3490.4440.2761.0000.4280.0000.090
영업용특수0.4170.2510.0570.3680.3590.5110.3380.6200.7490.6100.5160.4281.0000.0000.127
사용본거지 시군구0.0550.0000.0140.0080.0000.0000.0210.0470.0440.0220.0000.0000.0001.0000.000
연료0.3240.0620.0000.0000.0790.0720.3200.2820.2160.0800.0650.0900.1270.0001.000

Missing values

2023-12-12T18:36:30.985190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:36:31.182073image/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

사용본거지 시군구읍면동 (행정동)연료관용_승용관용_승합관용_화물관용_특수자가용_승용자가용승합자가용화물자가용특수영업용_승용영업용승합영업용화물영업용특수
0서울특별시 종로구종로구 기타휘발유1000001000000
1서울특별시 종로구종로구 기타휘발유2000002000000
2서울특별시 종로구종로구 기타휘발유1000001000000
3서울특별시 종로구종로구 기타경유<NA>000000100000
4서울특별시 종로구종로구 기타경유<NA>000001000000
5서울특별시 종로구종로구 기타휘발유2000002000000
6서울특별시 종로구종로구 기타휘발유1000001000000
7서울특별시 종로구종로구 청운효자동휘발유197106718001881230000
8서울특별시 종로구종로구 청운효자동경유11532479140756651811402711
9서울특별시 종로구종로구 청운효자동엘피지1650000011813901915
사용본거지 시군구읍면동 (행정동)연료관용_승용관용_승합관용_화물관용_특수자가용_승용자가용승합자가용화물자가용특수영업용_승용영업용승합영업용화물영업용특수
3560서울특별시 강동구강동구 둔촌2동휘발유4222000004211290000
3561서울특별시 강동구강동구 둔촌2동경유2750010081854137637151790
3562서울특별시 강동구강동구 둔촌2동엘피지575000023883738097211
3563서울특별시 강동구강동구 둔촌2동전기5300000320504012
3564서울특별시 강동구강동구 둔촌2동CNG1000000010000
3565서울특별시 강동구강동구 둔촌2동하이브리드37700000376000100
3566서울특별시 강동구강동구 둔촌2동수소8000008000000
3567서울특별시 강동구강동구 둔촌2동기타연료12000030260001
3568서울특별시 강동구강동구 기타1휘발유280000025120000
3569서울특별시 강동구강동구 기타1경유180000004140000

Duplicate rows

Most frequently occurring

사용본거지 시군구읍면동 (행정동)연료관용_승용관용_승합관용_화물관용_특수자가용_승용자가용승합자가용화물자가용특수영업용_승용영업용승합영업용화물영업용특수# duplicates
13서울특별시 중구중구 기타1휘발유<NA>0000010000005
0서울특별시 강남구강남구 기타경유10000010000003
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