Overview

Dataset statistics

Number of variables9
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory83.3 B

Variable types

Text1
Numeric3
Categorical4
DateTime1

Dataset

Description대구광역시 달서구 도로표지설치정보에 관한 내용(구분, 방향표지, 이정표지, 경계표지, 노선표지, 기타, 계 등)을 반영하고 있습니다.
URLhttps://www.data.go.kr/data/15099912/fileData.do

Alerts

관리부서 has constant value ""Constant
기준일자 has constant value ""Constant
방향표지 is highly overall correlated with 계High correlation
계 is highly overall correlated with 방향표지High correlation
경계표지 is highly imbalanced (59.6%)Imbalance
노선표지 is highly imbalanced (75.8%)Imbalance
구분 has unique valuesUnique
기타 has 8 (32.0%) zerosZeros

Reproduction

Analysis started2023-12-12 18:25:01.727375
Analysis finished2023-12-12 18:25:03.351449
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-13T03:25:03.480084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.76
Min length9

Characters and Unicode

Total characters319
Distinct characters41
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

Unique25 ?
Unique (%)100.0%

Sample

1st row대구광역시 달서구
2nd row대구광역시 달서구 성당동
3rd row대구광역시 달서구 두류동
4th row대구광역시 달서구 파호동
5th row대구광역시 달서구 호림동
ValueCountFrequency (%)
대구광역시 25
33.8%
달서구 25
33.8%
본동 1
 
1.4%
대곡동 1
 
1.4%
송현동 1
 
1.4%
월암동 1
 
1.4%
월성동 1
 
1.4%
대천동 1
 
1.4%
유천동 1
 
1.4%
진천동 1
 
1.4%
Other values (16) 16
21.6%
2023-12-13T03:25:03.839835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 50
15.7%
49
15.4%
대 27
8.5%
광 25
7.8%
역 25
7.8%
시 25
7.8%
달 25
7.8%
서 25
7.8%
동 24
7.5%
산 3
 
0.9%
Other values (31) 41
12.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
84.6%
Space Separator 49
 
15.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 50
18.5%
대 27
10.0%
광 25
9.3%
역 25
9.3%
시 25
9.3%
달 25
9.3%
서 25
9.3%
동 24
8.9%
산 3
 
1.1%
천 3
 
1.1%
Other values (30) 38
14.1%
Space Separator
ValueCountFrequency (%)
49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
84.6%
Common 49
 
15.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 50
18.5%
대 27
10.0%
광 25
9.3%
역 25
9.3%
시 25
9.3%
달 25
9.3%
서 25
9.3%
동 24
8.9%
산 3
 
1.1%
천 3
 
1.1%
Other values (30) 38
14.1%
Common
ValueCountFrequency (%)
49
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
84.6%
ASCII 49
 
15.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 50
18.5%
대 27
10.0%
광 25
9.3%
역 25
9.3%
시 25
9.3%
달 25
9.3%
서 25
9.3%
동 24
8.9%
산 3
 
1.1%
천 3
 
1.1%
Other values (30) 38
14.1%
ASCII
ValueCountFrequency (%)
49
100.0%

방향표지
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.68
Minimum3
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T03:25:03.960796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q114
median22
Q336
95-th percentile70.2
Maximum74
Range71
Interquartile range (IQR)22

Descriptive statistics

Standard deviation20.483164
Coefficient of variation (CV)0.71419679
Kurtosis0.054951181
Mean28.68
Median Absolute Deviation (MAD)11
Skewness0.94581297
Sum717
Variance419.56
MonotonicityNot monotonic
2023-12-13T03:25:04.076439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
12 3
 
12.0%
14 3
 
12.0%
6 2
 
8.0%
36 2
 
8.0%
41 2
 
8.0%
74 1
 
4.0%
16 1
 
4.0%
28 1
 
4.0%
30 1
 
4.0%
22 1
 
4.0%
Other values (8) 8
32.0%
ValueCountFrequency (%)
3 1
 
4.0%
6 2
8.0%
12 3
12.0%
14 3
12.0%
16 1
 
4.0%
17 1
 
4.0%
21 1
 
4.0%
22 1
 
4.0%
28 1
 
4.0%
30 1
 
4.0%
ValueCountFrequency (%)
74 1
4.0%
72 1
4.0%
63 1
4.0%
59 1
4.0%
41 2
8.0%
36 2
8.0%
35 1
4.0%
33 1
4.0%
30 1
4.0%
28 1
4.0%

이정표지
Categorical

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
0
17 
1
6 
2
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 17
68.0%
1 6
 
24.0%
2 2
 
8.0%

Length

2023-12-13T03:25:04.208703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:25:04.315105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17
68.0%
1 6
 
24.0%
2 2
 
8.0%

경계표지
Categorical

IMBALANCE 

Distinct3
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
0
22 
2
 
2
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 22
88.0%
2 2
 
8.0%
1 1
 
4.0%

Length

2023-12-13T03:25:04.441978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:25:04.539452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
88.0%
2 2
 
8.0%
1 1
 
4.0%

노선표지
Categorical

IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
0
24 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24
96.0%
2 1
 
4.0%

Length

2023-12-13T03:25:04.646354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:25:04.738940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24
96.0%
2 1
 
4.0%

기타
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.92
Minimum0
Maximum16
Zeros8
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T03:25:04.818535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10.4
Maximum16
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8935845
Coefficient of variation (CV)1.3334193
Kurtosis4.7241513
Mean2.92
Median Absolute Deviation (MAD)2
Skewness2.0772752
Sum73
Variance15.16
MonotonicityNot monotonic
2023-12-13T03:25:04.928576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 8
32.0%
1 4
16.0%
2 3
 
12.0%
3 3
 
12.0%
4 2
 
8.0%
11 1
 
4.0%
16 1
 
4.0%
6 1
 
4.0%
8 1
 
4.0%
5 1
 
4.0%
ValueCountFrequency (%)
0 8
32.0%
1 4
16.0%
2 3
 
12.0%
3 3
 
12.0%
4 2
 
8.0%
5 1
 
4.0%
6 1
 
4.0%
8 1
 
4.0%
11 1
 
4.0%
16 1
 
4.0%
ValueCountFrequency (%)
16 1
 
4.0%
11 1
 
4.0%
8 1
 
4.0%
6 1
 
4.0%
5 1
 
4.0%
4 2
 
8.0%
3 3
 
12.0%
2 3
 
12.0%
1 4
16.0%
0 8
32.0%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.28
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T03:25:05.045658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.4
Q116
median30
Q344
95-th percentile73
Maximum76
Range71
Interquartile range (IQR)28

Descriptive statistics

Standard deviation21.4
Coefficient of variation (CV)0.66294919
Kurtosis-0.49052841
Mean32.28
Median Absolute Deviation (MAD)14
Skewness0.71293886
Sum807
Variance457.96
MonotonicityNot monotonic
2023-12-13T03:25:05.186859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
16 2
 
8.0%
14 2
 
8.0%
8 1
 
4.0%
69 1
 
4.0%
12 1
 
4.0%
17 1
 
4.0%
34 1
 
4.0%
33 1
 
4.0%
44 1
 
4.0%
45 1
 
4.0%
Other values (13) 13
52.0%
ValueCountFrequency (%)
5 1
4.0%
6 1
4.0%
8 1
4.0%
12 1
4.0%
14 2
8.0%
16 2
8.0%
17 1
4.0%
18 1
4.0%
22 1
4.0%
24 1
4.0%
ValueCountFrequency (%)
76 1
4.0%
74 1
4.0%
69 1
4.0%
59 1
4.0%
57 1
4.0%
45 1
4.0%
44 1
4.0%
41 1
4.0%
37 1
4.0%
36 1
4.0%

관리부서
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
교통행정과
25 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교통행정과 25
100.0%

Length

2023-12-13T03:25:05.312973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:25:05.414371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교통행정과 25
100.0%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
Minimum2023-03-06 00:00:00
Maximum2023-03-06 00:00:00
2023-12-13T03:25:05.546717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:05.679295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T03:25:02.787869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:02.030037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:02.416307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:02.897516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:02.147703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:02.547979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:03.011397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:02.281394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:25:02.691621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:25:05.761015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분방향표지이정표지경계표지노선표지기타계
구분1.0001.0001.0001.0001.0001.0001.000
방향표지1.0001.0000.0000.0000.0000.0000.888
이정표지1.0000.0001.0000.0000.1440.4170.399
경계표지1.0000.0000.0001.0000.0000.0000.000
노선표지1.0000.0000.1440.0001.0000.5220.251
기타1.0000.0000.4170.0000.5221.0000.416
계1.0000.8880.3990.0000.2510.4161.000
2023-12-13T03:25:05.906881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선표지이정표지경계표지
노선표지1.0000.2250.000
이정표지0.2251.0000.000
경계표지0.0000.0001.000
2023-12-13T03:25:06.011825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향표지기타계이정표지경계표지노선표지
방향표지1.0000.1770.9840.0000.0000.000
기타0.1771.0000.3030.2610.0000.489
계0.9840.3031.0000.2180.0000.120
이정표지0.0000.2610.2181.0000.0000.225
경계표지0.0000.0000.0000.0001.0000.000
노선표지0.0000.4890.1200.2250.0001.000

Missing values

2023-12-13T03:25:03.160445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:25:03.297720image/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

구분방향표지이정표지경계표지노선표지기타계관리부서기준일자
0대구광역시 달서구600028교통행정과2023-03-06
1대구광역시 달서구 성당동14000014교통행정과2023-03-06
2대구광역시 달서구 두류동172001130교통행정과2023-03-06
3대구광역시 달서구 파호동12010316교통행정과2023-03-06
4대구광역시 달서구 호림동310015교통행정과2023-03-06
5대구광역시 달서구 갈산동600006교통행정과2023-03-06
6대구광역시 달서구 신당동36000036교통행정과2023-03-06
7대구광역시 달서구 이곡동59000059교통행정과2023-03-06
8대구광역시 달서구 장동14000418교통행정과2023-03-06
9대구광역시 달서구 장기동410001657교통행정과2023-03-06
구분방향표지이정표지경계표지노선표지기타계관리부서기준일자
15대구광역시 달서구 도원동74000074교통행정과2023-03-06
16대구광역시 달서구 진천동33000841교통행정과2023-03-06
17대구광역시 달서구 유천동22000022교통행정과2023-03-06
18대구광역시 달서구 대천동41100345교통행정과2023-03-06
19대구광역시 달서구 월성동36102544교통행정과2023-03-06
20대구광역시 달서구 월암동30000333교통행정과2023-03-06
21대구광역시 달서구 송현동14000216교통행정과2023-03-06
22대구광역시 달서구 대곡동28020434교통행정과2023-03-06
23대구광역시 달서구 본동16100017교통행정과2023-03-06
24대구광역시 달서구 호산동12000012교통행정과2023-03-06