Overview

Dataset statistics

Number of variables7
Number of observations216
Missing cells67
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 KiB
Average record size in memory61.6 B

Variable types

Categorical1
Text1
Numeric5

Dataset

Description대구광역시 구군별 읍면동별 자동차 등록현황 자료로 구군,읍면동별 승용,승합,화물,특수차의 등록현황입니다.
Author대구광역시
URLhttps://www.data.go.kr/data/15073712/fileData.do

Alerts

승용 is highly overall correlated with 승합 and 3 other fieldsHigh correlation
승합 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
화물 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
특수 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
소계 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
승합 has 8 (3.7%) missing valuesMissing
화물 has 3 (1.4%) missing valuesMissing
특수 has 55 (25.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 08:29:30.434895
Analysis finished2023-12-12 08:29:33.678050
Duration3.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구군
Categorical

Distinct9
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
중구
57 
동구
45 
북구
31 
수성구
27 
달서구
24 
Other values (4)
32 

Length

Max length3
Median length2
Mean length2.3287037
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남구
2nd row남구
3rd row남구
4th row달서구
5th row달서구

Common Values

ValueCountFrequency (%)
중구 57
26.4%
동구 45
20.8%
북구 31
14.4%
수성구 27
12.5%
달서구 24
11.1%
달성군 12
 
5.6%
서구 9
 
4.2%
군위군 8
 
3.7%
남구 3
 
1.4%

Length

2023-12-12T17:29:33.745787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:29:33.876075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 57
26.4%
동구 45
20.8%
북구 31
14.4%
수성구 27
12.5%
달서구 24
11.1%
달성군 12
 
5.6%
서구 9
 
4.2%
군위군 8
 
3.7%
남구 3
 
1.4%

읍면동
Text

Distinct213
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T17:29:34.197712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3009259
Min length2

Characters and Unicode

Total characters713
Distinct characters136
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

Unique210 ?
Unique (%)97.2%

Sample

1st row대명동
2nd row봉덕동
3rd row이천동
4th row갈산동
5th row감삼동
ValueCountFrequency (%)
이천동 2
 
0.9%
동호동 2
 
0.9%
도원동 2
 
0.9%
부계면 1
 
0.5%
효령면 1
 
0.5%
대신동 1
 
0.5%
지산동 1
 
0.5%
대명동 1
 
0.5%
황금동 1
 
0.5%
수성동2가 1
 
0.5%
Other values (203) 203
94.0%
2023-12-12T17:29:34.686862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 195
27.3%
가 42
 
5.9%
성 23
 
3.2%
산 17
 
2.4%
로 17
 
2.4%
1 14
 
2.0%
2 14
 
2.0%
대 14
 
2.0%
면 13
 
1.8%
서 11
 
1.5%
Other values (126) 353
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 675
94.7%
Decimal Number 38
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 195
28.9%
가 42
 
6.2%
성 23
 
3.4%
산 17
 
2.5%
로 17
 
2.5%
대 14
 
2.1%
면 13
 
1.9%
서 11
 
1.6%
신 10
 
1.5%
천 10
 
1.5%
Other values (122) 323
47.9%
Decimal Number
ValueCountFrequency (%)
1 14
36.8%
2 14
36.8%
3 8
21.1%
4 2
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 675
94.7%
Common 38
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 195
28.9%
가 42
 
6.2%
성 23
 
3.4%
산 17
 
2.5%
로 17
 
2.5%
대 14
 
2.1%
면 13
 
1.9%
서 11
 
1.6%
신 10
 
1.5%
천 10
 
1.5%
Other values (122) 323
47.9%
Common
ValueCountFrequency (%)
1 14
36.8%
2 14
36.8%
3 8
21.1%
4 2
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 675
94.7%
ASCII 38
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 195
28.9%
가 42
 
6.2%
성 23
 
3.4%
산 17
 
2.5%
로 17
 
2.5%
대 14
 
2.1%
면 13
 
1.9%
서 11
 
1.6%
신 10
 
1.5%
천 10
 
1.5%
Other values (122) 323
47.9%
ASCII
ValueCountFrequency (%)
1 14
36.8%
2 14
36.8%
3 8
21.1%
4 2
 
5.3%

승용
Real number (ℝ)

HIGH CORRELATION 

Distinct201
Distinct (%)93.5%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4931.8744
Minimum1
Maximum44747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T17:29:34.857503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q1131
median1424
Q37045.5
95-th percentile19365.2
Maximum44747
Range44746
Interquartile range (IQR)6914.5

Descriptive statistics

Standard deviation7327.4628
Coefficient of variation (CV)1.4857359
Kurtosis7.1558108
Mean4931.8744
Median Absolute Deviation (MAD)1393
Skewness2.3776924
Sum1060353
Variance53691711
MonotonicityNot monotonic
2023-12-12T17:29:35.021290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 3
 
1.4%
292 2
 
0.9%
96 2
 
0.9%
245 2
 
0.9%
57 2
 
0.9%
25 2
 
0.9%
5 2
 
0.9%
52 2
 
0.9%
20 2
 
0.9%
120 2
 
0.9%
Other values (191) 194
89.8%
ValueCountFrequency (%)
1 1
 
0.5%
4 3
1.4%
5 2
0.9%
10 1
 
0.5%
11 1
 
0.5%
12 1
 
0.5%
18 1
 
0.5%
20 2
0.9%
22 1
 
0.5%
25 2
0.9%
ValueCountFrequency (%)
44747 1
0.5%
40733 1
0.5%
30171 1
0.5%
30036 1
0.5%
26392 1
0.5%
25081 1
0.5%
24611 1
0.5%
22902 1
0.5%
22034 1
0.5%
22015 1
0.5%

승합
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct128
Distinct (%)61.5%
Missing8
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean131.89423
Minimum1
Maximum1185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T17:29:35.498672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18.75
median50.5
Q3189
95-th percentile476.9
Maximum1185
Range1184
Interquartile range (IQR)180.25

Descriptive statistics

Standard deviation179.60006
Coefficient of variation (CV)1.3616976
Kurtosis7.2019598
Mean131.89423
Median Absolute Deviation (MAD)47.5
Skewness2.2821844
Sum27434
Variance32256.182
MonotonicityNot monotonic
2023-12-12T17:29:35.712170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
5.6%
3 9
 
4.2%
2 8
 
3.7%
5 7
 
3.2%
4 7
 
3.2%
7 4
 
1.9%
13 4
 
1.9%
12 4
 
1.9%
17 4
 
1.9%
6 4
 
1.9%
Other values (118) 145
67.1%
(Missing) 8
 
3.7%
ValueCountFrequency (%)
1 12
5.6%
2 8
3.7%
3 9
4.2%
4 7
3.2%
5 7
3.2%
6 4
 
1.9%
7 4
 
1.9%
8 1
 
0.5%
9 2
 
0.9%
11 2
 
0.9%
ValueCountFrequency (%)
1185 1
0.5%
912 1
0.5%
740 1
0.5%
668 1
0.5%
596 1
0.5%
588 1
0.5%
540 1
0.5%
517 1
0.5%
500 1
0.5%
487 1
0.5%

화물
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct188
Distinct (%)88.3%
Missing3
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean760.76526
Minimum1
Maximum5974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T17:29:35.916023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q142
median333
Q31101
95-th percentile2943
Maximum5974
Range5973
Interquartile range (IQR)1059

Descriptive statistics

Standard deviation1005.1693
Coefficient of variation (CV)1.3212608
Kurtosis5.0131947
Mean760.76526
Median Absolute Deviation (MAD)323
Skewness2.0292926
Sum162043
Variance1010365.3
MonotonicityNot monotonic
2023-12-12T17:29:36.126392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
1.9%
5 4
 
1.9%
3 3
 
1.4%
4 3
 
1.4%
55 2
 
0.9%
299 2
 
0.9%
6 2
 
0.9%
41 2
 
0.9%
57 2
 
0.9%
16 2
 
0.9%
Other values (178) 187
86.6%
(Missing) 3
 
1.4%
ValueCountFrequency (%)
1 4
1.9%
2 2
0.9%
3 3
1.4%
4 3
1.4%
5 4
1.9%
6 2
0.9%
7 2
0.9%
8 1
 
0.5%
9 1
 
0.5%
10 2
0.9%
ValueCountFrequency (%)
5974 1
0.5%
4712 1
0.5%
4379 1
0.5%
4133 1
0.5%
3519 1
0.5%
3461 1
0.5%
3296 1
0.5%
3217 1
0.5%
3215 1
0.5%
3173 1
0.5%

특수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70
Distinct (%)43.5%
Missing55
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean31.279503
Minimum1
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T17:29:36.349082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median17
Q345
95-th percentile101
Maximum195
Range194
Interquartile range (IQR)41

Descriptive statistics

Standard deviation36.346632
Coefficient of variation (CV)1.1619952
Kurtosis5.1953367
Mean31.279503
Median Absolute Deviation (MAD)15
Skewness2.0060732
Sum5036
Variance1321.0776
MonotonicityNot monotonic
2023-12-12T17:29:36.570159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
6.5%
3 13
 
6.0%
2 8
 
3.7%
7 8
 
3.7%
4 6
 
2.8%
13 5
 
2.3%
58 4
 
1.9%
6 4
 
1.9%
16 4
 
1.9%
5 4
 
1.9%
Other values (60) 91
42.1%
(Missing) 55
25.5%
ValueCountFrequency (%)
1 14
6.5%
2 8
3.7%
3 13
6.0%
4 6
2.8%
5 4
 
1.9%
6 4
 
1.9%
7 8
3.7%
8 3
 
1.4%
9 2
 
0.9%
10 2
 
0.9%
ValueCountFrequency (%)
195 1
0.5%
194 1
0.5%
172 1
0.5%
131 1
0.5%
121 1
0.5%
107 1
0.5%
102 1
0.5%
101 2
0.9%
99 1
0.5%
95 1
0.5%

소계
Real number (ℝ)

HIGH CORRELATION 

Distinct204
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5809.5648
Minimum3
Maximum48935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T17:29:36.744920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29.25
Q1189.25
median1776.5
Q38292.5
95-th percentile22667.75
Maximum48935
Range48932
Interquartile range (IQR)8103.25

Descriptive statistics

Standard deviation8398.6611
Coefficient of variation (CV)1.445661
Kurtosis6.490766
Mean5809.5648
Median Absolute Deviation (MAD)1732
Skewness2.2794293
Sum1254866
Variance70537508
MonotonicityNot monotonic
2023-12-12T17:29:36.910009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 2
 
0.9%
121 2
 
0.9%
72 2
 
0.9%
32 2
 
0.9%
4 2
 
0.9%
118 2
 
0.9%
1771 2
 
0.9%
15890 2
 
0.9%
13 2
 
0.9%
208 2
 
0.9%
Other values (194) 196
90.7%
ValueCountFrequency (%)
3 1
0.5%
4 2
0.9%
5 1
0.5%
6 1
0.5%
7 1
0.5%
10 1
0.5%
13 2
0.9%
21 1
0.5%
24 1
0.5%
31 1
0.5%
ValueCountFrequency (%)
48935 1
0.5%
47814 1
0.5%
36054 1
0.5%
32232 1
0.5%
29772 1
0.5%
28911 1
0.5%
28592 1
0.5%
26636 1
0.5%
25161 1
0.5%
25081 1
0.5%

Interactions

2023-12-12T17:29:32.855727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:30.757457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.296316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.850046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.320795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.961208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:30.839072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.410245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.943027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.400805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:33.054939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:30.950843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.535461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.046128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.510253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:33.144706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.063261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.643136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.137002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.641993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:33.248304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.182008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:31.758546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.235531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:29:32.751323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:29:37.022894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구군승용승합화물특수소계
구군1.0000.4920.7080.6810.5050.674
승용0.4921.0000.8430.7890.7700.945
승합0.7080.8431.0000.9510.9180.952
화물0.6810.7890.9511.0000.9360.932
특수0.5050.7700.9180.9361.0000.903
소계0.6740.9450.9520.9320.9031.000
2023-12-12T17:29:37.146821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용승합화물특수소계구군
승용1.0000.9520.9460.8450.9980.267
승합0.9521.0000.9420.8730.9580.297
화물0.9460.9421.0000.9050.9610.278
특수0.8450.8730.9051.0000.8600.182
소계0.9980.9580.9610.8601.0000.274
구군0.2670.2970.2780.1820.2741.000

Missing values

2023-12-12T17:29:33.385915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:29:33.510512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T17:29:33.617063image/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

구군읍면동승용승합화물특수소계
0남구대명동300361185471212136054
1남구봉덕동1435136217454216500
2남구이천동4639107522185286
3달서구갈산동135213185552343
4달서구감삼동1363128020066315980
5달서구대곡동92892759263010520
6달서구대천동5153219885346291
7달서구도원동14272268155510116196
8달서구두류동78992291185409353
9달서구본동4913177839635992
구군읍면동승용승합화물특수소계
206중구향촌동5729<NA>68
207중구화전동1812<NA>21
208군위군군위읍37991841716415740
209군위군소보면9614760821618
210군위군효령면173572100272816
211군위군부계면9444546441457
212군위군우보면7673643681247
213군위군의흥면9193255961516
214군위군산성면465162995785
215군위군삼국유사면500193373859