Overview

Dataset statistics

Number of variables6
Number of observations70
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory53.9 B

Variable types

Categorical2
Numeric4

Dataset

Description경기도 포천시에서 제공하는 연도별(2016년, 2017년, 2018년, 2019년, 2020년) 읍면동별 승용차, 승합차, 화물차, 특수차 등록 대수 현황 데이터
Author경기도 포천시
URLhttps://www.data.go.kr/data/15090231/fileData.do

Alerts

승용 is highly overall correlated with 승합 and 1 other fieldsHigh correlation
승합 is highly overall correlated with 승용 and 2 other fieldsHigh correlation
화물 is highly overall correlated with 승합 and 2 other fieldsHigh correlation
특수 is highly overall correlated with 화물 and 1 other fieldsHigh correlation
읍면동 is highly overall correlated with 승용 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 08:20:38.349252
Analysis finished2023-12-12 08:20:41.019986
Duration2.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct5
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size692.0 B
2016년
14 
2017년
14 
2018년
14 
2019년
14 
2020년
14 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016년
2nd row2016년
3rd row2016년
4th row2016년
5th row2016년

Common Values

ValueCountFrequency (%)
2016년 14
20.0%
2017년 14
20.0%
2018년 14
20.0%
2019년 14
20.0%
2020년 14
20.0%

Length

2023-12-12T17:20:41.119333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:20:41.275176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016년 14
20.0%
2017년 14
20.0%
2018년 14
20.0%
2019년 14
20.0%
2020년 14
20.0%

읍면동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size692.0 B
소흘읍
5 
군내면
5 
내촌면
5 
가산면
5 
신북면
5 
Other values (9)
45 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소흘읍
2nd row군내면
3rd row내촌면
4th row가산면
5th row신북면

Common Values

ValueCountFrequency (%)
소흘읍 5
 
7.1%
군내면 5
 
7.1%
내촌면 5
 
7.1%
가산면 5
 
7.1%
신북면 5
 
7.1%
창수면 5
 
7.1%
영중면 5
 
7.1%
일동면 5
 
7.1%
이동면 5
 
7.1%
영북면 5
 
7.1%
Other values (4) 20
28.6%

Length

2023-12-12T17:20:41.502753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소흘읍 5
 
7.1%
군내면 5
 
7.1%
내촌면 5
 
7.1%
가산면 5
 
7.1%
신북면 5
 
7.1%
창수면 5
 
7.1%
영중면 5
 
7.1%
일동면 5
 
7.1%
이동면 5
 
7.1%
영북면 5
 
7.1%
Other values (4) 20
28.6%

승용
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4581.6857
Minimum907
Maximum18461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-12T17:20:41.720266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum907
5-th percentile956.25
Q12270.75
median3590
Q35505
95-th percentile17027.45
Maximum18461
Range17554
Interquartile range (IQR)3234.25

Descriptive statistics

Standard deviation4066.9686
Coefficient of variation (CV)0.88765769
Kurtosis5.4913593
Mean4581.6857
Median Absolute Deviation (MAD)1640
Skewness2.3515588
Sum320718
Variance16540233
MonotonicityNot monotonic
2023-12-12T17:20:41.934298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6553 2
 
2.9%
16797 1
 
1.4%
2278 1
 
1.4%
2349 1
 
1.4%
3531 1
 
1.4%
1977 1
 
1.4%
945 1
 
1.4%
5496 1
 
1.4%
3878 1
 
1.4%
3827 1
 
1.4%
Other values (59) 59
84.3%
ValueCountFrequency (%)
907 1
1.4%
928 1
1.4%
931 1
1.4%
945 1
1.4%
970 1
1.4%
1120 1
1.4%
1130 1
1.4%
1170 1
1.4%
1194 1
1.4%
1248 1
1.4%
ValueCountFrequency (%)
18461 1
1.4%
17977 1
1.4%
17732 1
1.4%
17216 1
1.4%
16797 1
1.4%
7318 1
1.4%
6871 1
1.4%
6748 1
1.4%
6683 1
1.4%
6664 1
1.4%

승합
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314.92857
Minimum47
Maximum1194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-12T17:20:42.133773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile56.8
Q1177.75
median270.5
Q3366
95-th percentile1092.7
Maximum1194
Range1147
Interquartile range (IQR)188.25

Descriptive statistics

Standard deviation260.17967
Coefficient of variation (CV)0.82615453
Kurtosis5.5128923
Mean314.92857
Median Absolute Deviation (MAD)95.5
Skewness2.3323416
Sum22045
Variance67693.459
MonotonicityNot monotonic
2023-12-12T17:20:42.349916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
278 2
 
2.9%
317 2
 
2.9%
366 2
 
2.9%
107 2
 
2.9%
276 2
 
2.9%
440 2
 
2.9%
1187 1
 
1.4%
454 1
 
1.4%
184 1
 
1.4%
50 1
 
1.4%
Other values (54) 54
77.1%
ValueCountFrequency (%)
47 1
1.4%
50 1
1.4%
53 1
1.4%
55 1
1.4%
59 1
1.4%
91 1
1.4%
102 1
1.4%
107 2
2.9%
119 1
1.4%
120 1
1.4%
ValueCountFrequency (%)
1194 1
1.4%
1187 1
1.4%
1179 1
1.4%
1117 1
1.4%
1063 1
1.4%
485 1
1.4%
471 1
1.4%
464 1
1.4%
458 1
1.4%
454 1
1.4%

화물
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1580.6857
Minimum490
Maximum4689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-12T17:20:42.522968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum490
5-th percentile502.25
Q1736.75
median1308
Q31952.5
95-th percentile4548.1
Maximum4689
Range4199
Interquartile range (IQR)1215.75

Descriptive statistics

Standard deviation1063.966
Coefficient of variation (CV)0.67310407
Kurtosis2.3327929
Mean1580.6857
Median Absolute Deviation (MAD)604.5
Skewness1.5667102
Sum110648
Variance1132023.6
MonotonicityNot monotonic
2023-12-12T17:20:42.695869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1145 2
 
2.9%
527 2
 
2.9%
1963 2
 
2.9%
500 2
 
2.9%
675 1
 
1.4%
2788 1
 
1.4%
722 1
 
1.4%
1931 1
 
1.4%
2349 1
 
1.4%
4514 1
 
1.4%
Other values (56) 56
80.0%
ValueCountFrequency (%)
490 1
1.4%
491 1
1.4%
500 2
2.9%
505 1
1.4%
527 2
2.9%
534 1
1.4%
547 1
1.4%
553 1
1.4%
672 1
1.4%
674 1
1.4%
ValueCountFrequency (%)
4689 1
1.4%
4616 1
1.4%
4578 1
1.4%
4576 1
1.4%
4514 1
1.4%
2788 1
1.4%
2758 1
1.4%
2755 1
1.4%
2754 1
1.4%
2516 1
1.4%

특수
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.585714
Minimum1
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-12T17:20:42.877792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median24
Q352.75
95-th percentile134.65
Maximum147
Range146
Interquartile range (IQR)40.75

Descriptive statistics

Standard deviation40.276757
Coefficient of variation (CV)1.0174569
Kurtosis0.59608695
Mean39.585714
Median Absolute Deviation (MAD)18.5
Skewness1.2890272
Sum2771
Variance1622.2172
MonotonicityNot monotonic
2023-12-12T17:20:43.090427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 6
 
8.6%
12 4
 
5.7%
24 4
 
5.7%
101 2
 
2.9%
5 2
 
2.9%
45 2
 
2.9%
15 2
 
2.9%
19 2
 
2.9%
20 2
 
2.9%
26 2
 
2.9%
Other values (39) 42
60.0%
ValueCountFrequency (%)
1 1
 
1.4%
2 6
8.6%
3 2
 
2.9%
4 1
 
1.4%
5 2
 
2.9%
6 1
 
1.4%
7 2
 
2.9%
10 1
 
1.4%
11 1
 
1.4%
12 4
5.7%
ValueCountFrequency (%)
147 1
1.4%
139 1
1.4%
136 2
2.9%
133 1
1.4%
106 1
1.4%
105 1
1.4%
104 1
1.4%
101 2
2.9%
97 1
1.4%
92 1
1.4%

Interactions

2023-12-12T17:20:39.864516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:38.586882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:38.986271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:39.404765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:40.100365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:38.679348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:39.084485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:39.507815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:40.402965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:38.789393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:39.192062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:39.638547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:40.569416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:38.881229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:39.291673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:20:39.733127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:20:43.227747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도읍면동승용승합화물특수
연도1.0000.0000.0000.0000.0000.000
읍면동0.0001.0000.9850.9060.9970.890
승용0.0000.9851.0000.7720.8610.762
승합0.0000.9060.7721.0000.8330.826
화물0.0000.9970.8610.8331.0000.821
특수0.0000.8900.7620.8260.8211.000
2023-12-12T17:20:43.382702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도읍면동
연도1.0000.000
읍면동0.0001.000
2023-12-12T17:20:43.515007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용승합화물특수연도읍면동
승용1.0000.6640.4320.1350.0000.894
승합0.6641.0000.7130.3030.0000.692
화물0.4320.7131.0000.7220.0000.866
특수0.1350.3030.7221.0000.0000.625
연도0.0000.0000.0000.0001.0000.000
읍면동0.8940.6920.8660.6250.0001.000

Missing values

2023-12-12T17:20:40.812441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:20:40.964497image/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

연도읍면동승용승합화물특수
02016년소흘읍167971187451463
12016년군내면2543317125619
22016년내촌면2346199121212
32016년가산면3713301224824
42016년신북면5252464194843
52016년창수면90759983106
62016년영중면19422272516133
72016년일동면3471276114910
82016년이동면22692816762
92016년영북면287921097926
연도읍면동승용승합화물특수
602020년신북면5725440196355
612020년창수면97055731104
622020년영중면20202112754139
632020년일동면3616266114118
642020년이동면25032606725
652020년영북면316214593611
662020년관인면6553915345
672020년화현면12481424904
682020년포천동7318366160156
692020년선단동4456237111323