Overview

Dataset statistics

Number of variables5
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory47.1 B

Variable types

Text1
Numeric4

Dataset

Description시군별 수입자동차 등록현황
Author국토교통부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=LY9TPELYSEJJYCBH6U3N34634952&infSeq=1

Alerts

승용차수 is highly overall correlated with 승합차수 and 1 other fieldsHigh correlation
승합차수 is highly overall correlated with 승용차수 and 2 other fieldsHigh correlation
화물차수 is highly overall correlated with 승용차수 and 2 other fieldsHigh correlation
특수차수 is highly overall correlated with 승합차수 and 1 other fieldsHigh correlation
시군구명 has unique valuesUnique
승용차수 has unique valuesUnique
화물차수 has unique valuesUnique

Reproduction

Analysis started2024-03-12 23:32:29.931790
Analysis finished2024-03-12 23:32:31.583632
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2024-03-13T08:32:31.708611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.7380952
Min length3

Characters and Unicode

Total characters199
Distinct characters59
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row고양시 덕양구
3rd row고양시 일산동구
4th row고양시 일산서구
5th row과천시
ValueCountFrequency (%)
수원시 4
 
6.8%
용인시 3
 
5.1%
성남시 3
 
5.1%
고양시 3
 
5.1%
안양시 2
 
3.4%
안산시 2
 
3.4%
파주시 1
 
1.7%
이천시 1
 
1.7%
하남시 1
 
1.7%
안성시 1
 
1.7%
Other values (38) 38
64.4%
2024-03-13T08:32:31.994138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 40
20.1%
구 18
 
9.0%
17
 
8.5%
양 9
 
4.5%
안 8
 
4.0%
천 6
 
3.0%
수 6
 
3.0%
원 6
 
3.0%
성 5
 
2.5%
남 5
 
2.5%
Other values (49) 79
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 182
91.5%
Space Separator 17
 
8.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 40
22.0%
구 18
 
9.9%
양 9
 
4.9%
안 8
 
4.4%
천 6
 
3.3%
수 6
 
3.3%
원 6
 
3.3%
성 5
 
2.7%
남 5
 
2.7%
주 5
 
2.7%
Other values (48) 74
40.7%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
91.5%
Common 17
 
8.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 40
22.0%
구 18
 
9.9%
양 9
 
4.9%
안 8
 
4.4%
천 6
 
3.3%
수 6
 
3.3%
원 6
 
3.3%
성 5
 
2.7%
남 5
 
2.7%
주 5
 
2.7%
Other values (48) 74
40.7%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 182
91.5%
ASCII 17
 
8.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 40
22.0%
구 18
 
9.9%
양 9
 
4.9%
안 8
 
4.4%
천 6
 
3.3%
수 6
 
3.3%
원 6
 
3.3%
성 5
 
2.7%
남 5
 
2.7%
주 5
 
2.7%
Other values (48) 74
40.7%
ASCII
ValueCountFrequency (%)
17
100.0%

승용차수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20705.286
Minimum1677
Maximum67848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-03-13T08:32:32.115245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1677
5-th percentile3596
Q110144
median14186.5
Q328816.75
95-th percentile43957.5
Maximum67848
Range66171
Interquartile range (IQR)18672.75

Descriptive statistics

Standard deviation15198.634
Coefficient of variation (CV)0.73404608
Kurtosis1.6157327
Mean20705.286
Median Absolute Deviation (MAD)6732
Skewness1.2788794
Sum869622
Variance2.3099847 × 108
MonotonicityNot monotonic
2024-03-13T08:32:32.222116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3481 1
 
2.4%
40984 1
 
2.4%
19942 1
 
2.4%
8737 1
 
2.4%
12830 1
 
2.4%
9336 1
 
2.4%
5781 1
 
2.4%
1677 1
 
2.4%
10826 1
 
2.4%
39978 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1677 1
2.4%
2813 1
2.4%
3481 1
2.4%
5781 1
2.4%
6993 1
2.4%
7916 1
2.4%
8705 1
2.4%
8737 1
2.4%
9336 1
2.4%
9605 1
2.4%
ValueCountFrequency (%)
67848 1
2.4%
61717 1
2.4%
44114 1
2.4%
40984 1
2.4%
39978 1
2.4%
36875 1
2.4%
36783 1
2.4%
33162 1
2.4%
31459 1
2.4%
30412 1
2.4%

승합차수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.95238
Minimum34
Maximum726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-03-13T08:32:32.326984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile47.05
Q189.5
median141.5
Q3208
95-th percentile453.9
Maximum726
Range692
Interquartile range (IQR)118.5

Descriptive statistics

Standard deviation149.82347
Coefficient of variation (CV)0.79291653
Kurtosis3.2140052
Mean188.95238
Median Absolute Deviation (MAD)59
Skewness1.7220424
Sum7936
Variance22447.071
MonotonicityNot monotonic
2024-03-13T08:32:32.429939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
414 2
 
4.8%
162 1
 
2.4%
86 1
 
2.4%
54 1
 
2.4%
160 1
 
2.4%
95 1
 
2.4%
101 1
 
2.4%
41 1
 
2.4%
128 1
 
2.4%
199 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
34 1
2.4%
41 1
2.4%
47 1
2.4%
48 1
2.4%
53 1
2.4%
54 1
2.4%
56 1
2.4%
79 1
2.4%
85 1
2.4%
86 1
2.4%
ValueCountFrequency (%)
726 1
2.4%
543 1
2.4%
456 1
2.4%
414 2
4.8%
366 1
2.4%
353 1
2.4%
339 1
2.4%
258 1
2.4%
253 1
2.4%
209 1
2.4%

화물차수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean461.59524
Minimum53
Maximum1705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-03-13T08:32:32.537818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile140.45
Q1233.5
median405
Q3583.75
95-th percentile1012.65
Maximum1705
Range1652
Interquartile range (IQR)350.25

Descriptive statistics

Standard deviation332.7658
Coefficient of variation (CV)0.72090388
Kurtosis4.5738909
Mean461.59524
Median Absolute Deviation (MAD)178.5
Skewness1.8706591
Sum19387
Variance110733.08
MonotonicityNot monotonic
2024-03-13T08:32:32.640230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
223 1
 
2.4%
249 1
 
2.4%
304 1
 
2.4%
168 1
 
2.4%
470 1
 
2.4%
389 1
 
2.4%
583 1
 
2.4%
478 1
 
2.4%
321 1
 
2.4%
494 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
53 1
2.4%
137 1
2.4%
140 1
2.4%
149 1
2.4%
164 1
2.4%
166 1
2.4%
168 1
2.4%
179 1
2.4%
181 1
2.4%
223 1
2.4%
ValueCountFrequency (%)
1705 1
2.4%
1389 1
2.4%
1022 1
2.4%
835 1
2.4%
826 1
2.4%
748 1
2.4%
688 1
2.4%
687 1
2.4%
634 1
2.4%
613 1
2.4%

특수차수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.33333
Minimum9
Maximum607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-03-13T08:32:32.743702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile29.2
Q172.75
median115
Q3191.25
95-th percentile472.1
Maximum607
Range598
Interquartile range (IQR)118.5

Descriptive statistics

Standard deviation135.98201
Coefficient of variation (CV)0.85344355
Kurtosis3.6243383
Mean159.33333
Median Absolute Deviation (MAD)59
Skewness1.8511559
Sum6692
Variance18491.106
MonotonicityNot monotonic
2024-03-13T08:32:32.868482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
235 2
 
4.8%
167 2
 
4.8%
116 2
 
4.8%
56 2
 
4.8%
101 2
 
4.8%
170 2
 
4.8%
93 2
 
4.8%
166 1
 
2.4%
45 1
 
2.4%
114 1
 
2.4%
Other values (25) 25
59.5%
ValueCountFrequency (%)
9 1
2.4%
25 1
2.4%
29 1
2.4%
33 1
2.4%
36 1
2.4%
45 1
2.4%
49 1
2.4%
56 2
4.8%
57 1
2.4%
68 1
2.4%
ValueCountFrequency (%)
607 1
2.4%
569 1
2.4%
479 1
2.4%
341 1
2.4%
284 1
2.4%
281 1
2.4%
269 1
2.4%
235 2
4.8%
195 1
2.4%
193 1
2.4%

Interactions

2024-03-13T08:32:31.161053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.265081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.544343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.868497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:31.230720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.336311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.635500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.938770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:31.302664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.406901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.717016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:31.017972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:31.390086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.473999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:30.790969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:32:31.088711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:32:32.945301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명승용차수승합차수화물차수특수차수
시군구명1.0001.0001.0001.0001.000
승용차수1.0001.0000.8000.8320.671
승합차수1.0000.8001.0000.8100.816
화물차수1.0000.8320.8101.0000.902
특수차수1.0000.6710.8160.9021.000
2024-03-13T08:32:33.021679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용차수승합차수화물차수특수차수
승용차수1.0000.8500.5160.477
승합차수0.8501.0000.7380.631
화물차수0.5160.7381.0000.824
특수차수0.4770.6310.8241.000

Missing values

2024-03-13T08:32:31.479658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:32:31.554708image/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

시군구명승용차수승합차수화물차수특수차수
0가평군34814722333
1고양시 덕양구2819820245493
2고양시 일산동구27499209421116
3고양시 일산서구2025214823156
4과천시7916485325
5광명시12483127264101
6광주시26748339688170
7구리시100998518157
8군포시10702135243127
9김포시36875543835479
시군구명승용차수승합차수화물차수특수차수
32용인시 수지구4098416224995
33용인시 처인구16842414687167
34의왕시1027979166193
35의정부시1912717938697
36이천시11979165584284
37파주시29023353826170
38평택시367833661389569
39포천시699394499235
40하남시2716620543691
41화성시678487261705607