Overview

Dataset statistics

Number of variables8
Number of observations752
Missing cells621
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.3 KiB
Average record size in memory71.2 B

Variable types

Numeric5
Text1
Categorical2

Dataset

Description경상북도 구미시 버스정보시스템의 노드 테이블 정보로 노드타입, 노드한글명칭, 위경도 위치좌표 등의 정보를 제공합니다.
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15049481/fileData.do

Alerts

링크수 has constant value ""Constant
노드표준식별자 is highly overall correlated with 동리코드High correlation
동리코드 is highly overall correlated with 노드표준식별자High correlation
유턴구분 is highly imbalanced (53.8%)Imbalance
노드한글명칭 has 621 (82.6%) missing valuesMissing
노드표준식별자 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:04:04.090371
Analysis finished2023-12-12 15:04:07.223202
Duration3.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노드표준식별자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct752
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5130102 × 109
Minimum1.5000001 × 109
Maximum3.6900223 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-13T00:04:07.302825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5000001 × 109
5-th percentile3.5300015 × 109
Q13.5500104 × 109
median3.5500292 × 109
Q33.5500481 × 109
95-th percentile3.690012 × 109
Maximum3.6900223 × 109
Range2.1900222 × 109
Interquartile range (IQR)37750

Descriptive statistics

Standard deviation3.7097221 × 108
Coefficient of variation (CV)0.10559953
Kurtosis23.911195
Mean3.5130102 × 109
Median Absolute Deviation (MAD)18900
Skewness-5.0045282
Sum2.6417837 × 1012
Variance1.3762038 × 1017
MonotonicityNot monotonic
2023-12-13T00:04:07.458869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3690014300 1
 
0.1%
3550001300 1
 
0.1%
3550030600 1
 
0.1%
3550030700 1
 
0.1%
3550000900 1
 
0.1%
3550001000 1
 
0.1%
3550000800 1
 
0.1%
3550000300 1
 
0.1%
3550001100 1
 
0.1%
3550000500 1
 
0.1%
Other values (742) 742
98.7%
ValueCountFrequency (%)
1500000100 1
0.1%
1500000200 1
0.1%
1520000100 1
0.1%
1520000200 1
0.1%
1520000300 1
0.1%
1520000400 1
0.1%
1520000500 1
0.1%
1520000600 1
0.1%
1520000700 1
0.1%
1540000100 1
0.1%
ValueCountFrequency (%)
3690022300 1
0.1%
3690022200 1
0.1%
3690022100 1
0.1%
3690022000 1
0.1%
3690020800 1
0.1%
3690020700 1
0.1%
3690018900 1
0.1%
3690018800 1
0.1%
3690018700 1
0.1%
3690018600 1
0.1%

노드타입
Real number (ℝ)

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.89894
Minimum101
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-13T00:04:07.594827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median101
Q3101
95-th percentile108
Maximum110
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9433793
Coefficient of variation (CV)0.019071635
Kurtosis4.4588244
Mean101.89894
Median Absolute Deviation (MAD)0
Skewness2.2753956
Sum76628
Variance3.7767232
MonotonicityNot monotonic
2023-12-13T00:04:07.700868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
101 587
78.1%
104 67
 
8.9%
108 35
 
4.7%
105 24
 
3.2%
103 21
 
2.8%
102 6
 
0.8%
110 5
 
0.7%
107 3
 
0.4%
106 3
 
0.4%
109 1
 
0.1%
ValueCountFrequency (%)
101 587
78.1%
102 6
 
0.8%
103 21
 
2.8%
104 67
 
8.9%
105 24
 
3.2%
106 3
 
0.4%
107 3
 
0.4%
108 35
 
4.7%
109 1
 
0.1%
110 5
 
0.7%
ValueCountFrequency (%)
110 5
 
0.7%
109 1
 
0.1%
108 35
 
4.7%
107 3
 
0.4%
106 3
 
0.4%
105 24
 
3.2%
104 67
 
8.9%
103 21
 
2.8%
102 6
 
0.8%
101 587
78.1%

노드한글명칭
Text

MISSING 

Distinct114
Distinct (%)87.0%
Missing621
Missing (%)82.6%
Memory size6.0 KiB
2023-12-13T00:04:07.989568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.6870229
Min length3

Characters and Unicode

Total characters745
Distinct characters143
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)79.4%

Sample

1st row상공회의소네거리
2nd row세무서사거리
3rd row대신교차로 북측
4th row길수교차로
5th row해평교차로
ValueCountFrequency (%)
길수교차로 4
 
2.9%
대신교차로 4
 
2.9%
죽전교차로 3
 
2.1%
상공회의소네거리 3
 
2.1%
월곡교차로 3
 
2.1%
해평교차로 3
 
2.1%
남측 3
 
2.1%
신평교 3
 
2.1%
다부원앞 3
 
2.1%
북측 2
 
1.4%
Other values (105) 109
77.9%
2023-12-13T00:04:08.408904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 80
 
10.7%
거 75
 
10.1%
교 47
 
6.3%
로 33
 
4.4%
삼 30
 
4.0%
차 30
 
4.0%
사 24
 
3.2%
네 21
 
2.8%
원 13
 
1.7%
곡 13
 
1.7%
Other values (133) 379
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 692
92.9%
Decimal Number 25
 
3.4%
Space Separator 9
 
1.2%
Uppercase Letter 7
 
0.9%
Dash Punctuation 4
 
0.5%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 80
 
11.6%
거 75
 
10.8%
교 47
 
6.8%
로 33
 
4.8%
삼 30
 
4.3%
차 30
 
4.3%
사 24
 
3.5%
네 21
 
3.0%
원 13
 
1.9%
곡 13
 
1.9%
Other values (116) 326
47.1%
Decimal Number
ValueCountFrequency (%)
2 10
40.0%
4 4
 
16.0%
1 3
 
12.0%
5 3
 
12.0%
7 2
 
8.0%
9 2
 
8.0%
8 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 2
28.6%
I 2
28.6%
S 1
14.3%
B 1
14.3%
K 1
14.3%
Space Separator
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 692
92.9%
Common 46
 
6.2%
Latin 7
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 80
 
11.6%
거 75
 
10.8%
교 47
 
6.8%
로 33
 
4.8%
삼 30
 
4.3%
차 30
 
4.3%
사 24
 
3.5%
네 21
 
3.0%
원 13
 
1.9%
곡 13
 
1.9%
Other values (116) 326
47.1%
Common
ValueCountFrequency (%)
2 10
21.7%
9
19.6%
- 4
 
8.7%
4 4
 
8.7%
( 3
 
6.5%
1 3
 
6.5%
5 3
 
6.5%
) 3
 
6.5%
7 2
 
4.3%
. 2
 
4.3%
Other values (2) 3
 
6.5%
Latin
ValueCountFrequency (%)
C 2
28.6%
I 2
28.6%
S 1
14.3%
B 1
14.3%
K 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 692
92.9%
ASCII 53
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 80
 
11.6%
거 75
 
10.8%
교 47
 
6.8%
로 33
 
4.8%
삼 30
 
4.3%
차 30
 
4.3%
사 24
 
3.5%
네 21
 
3.0%
원 13
 
1.9%
곡 13
 
1.9%
Other values (116) 326
47.1%
ASCII
ValueCountFrequency (%)
2 10
18.9%
9
17.0%
- 4
 
7.5%
4 4
 
7.5%
( 3
 
5.7%
1 3
 
5.7%
5 3
 
5.7%
) 3
 
5.7%
7 2
 
3.8%
. 2
 
3.8%
Other values (7) 10
18.9%

유턴구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
0
602 
1
149 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0039894
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 602
80.1%
1 149
 
19.8%
<NA> 1
 
0.1%

Length

2023-12-13T00:04:08.573498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:04:08.696707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 602
80.1%
1 149
 
19.8%
na 1
 
0.1%

링크수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
0
752 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 752
100.0%

Length

2023-12-13T00:04:08.801986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:04:08.896391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 752
100.0%

위도
Real number (ℝ)

UNIQUE 

Distinct752
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.36925
Minimum128.09133
Maximum128.60426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-13T00:04:08.994070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.09133
5-th percentile128.18959
Q1128.32867
median128.37202
Q3128.42404
95-th percentile128.53781
Maximum128.60426
Range0.512929
Interquartile range (IQR)0.09537095

Descriptive statistics

Standard deviation0.090125464
Coefficient of variation (CV)0.00070207983
Kurtosis0.41893899
Mean128.36925
Median Absolute Deviation (MAD)0.04961035
Skewness-0.25462391
Sum96533.679
Variance0.0081225993
MonotonicityNot monotonic
2023-12-13T00:04:09.126198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.3842264 1
 
0.1%
128.3591022 1
 
0.1%
128.3627172 1
 
0.1%
128.3855507 1
 
0.1%
128.3315212 1
 
0.1%
128.3317329 1
 
0.1%
128.3295765 1
 
0.1%
128.3298317 1
 
0.1%
128.3225737 1
 
0.1%
128.4344015 1
 
0.1%
Other values (742) 742
98.7%
ValueCountFrequency (%)
128.091327 1
0.1%
128.0956672 1
0.1%
128.117265 1
0.1%
128.117978 1
0.1%
128.1183616 1
0.1%
128.127427 1
0.1%
128.1361184 1
0.1%
128.1452304 1
0.1%
128.1478938 1
0.1%
128.149824 1
0.1%
ValueCountFrequency (%)
128.604256 1
0.1%
128.5846515 1
0.1%
128.5823287 1
0.1%
128.5822049 1
0.1%
128.5759741 1
0.1%
128.5744588 1
0.1%
128.5711076 1
0.1%
128.5676184 1
0.1%
128.5664147 1
0.1%
128.5647649 1
0.1%

경도
Real number (ℝ)

UNIQUE 

Distinct752
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.116659
Minimum35.853252
Maximum36.363156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-13T00:04:09.257858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.853252
5-th percentile35.932589
Q136.081764
median36.122428
Q336.162282
95-th percentile36.271456
Maximum36.363156
Range0.50990362
Interquartile range (IQR)0.080517982

Descriptive statistics

Standard deviation0.095580901
Coefficient of variation (CV)0.0026464491
Kurtosis0.30618952
Mean36.116659
Median Absolute Deviation (MAD)0.039912585
Skewness-0.31245118
Sum27159.728
Variance0.0091357086
MonotonicityNot monotonic
2023-12-13T00:04:09.421851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.980177 1
 
0.1%
36.10107921 1
 
0.1%
36.1190577 1
 
0.1%
36.11958109 1
 
0.1%
36.29903283 1
 
0.1%
36.29913774 1
 
0.1%
36.20900038 1
 
0.1%
36.20934163 1
 
0.1%
36.13315048 1
 
0.1%
36.16902363 1
 
0.1%
Other values (742) 742
98.7%
ValueCountFrequency (%)
35.85325248 1
0.1%
35.85819346 1
0.1%
35.86042068 1
0.1%
35.86251731 1
0.1%
35.86424924 1
0.1%
35.86485859 1
0.1%
35.87056248 1
0.1%
35.87227967 1
0.1%
35.87818337 1
0.1%
35.88050511 1
0.1%
ValueCountFrequency (%)
36.3631561 1
0.1%
36.36148134 1
0.1%
36.35512965 1
0.1%
36.34989623 1
0.1%
36.34319793 1
0.1%
36.34110256 1
0.1%
36.33875948 1
0.1%
36.33455465 1
0.1%
36.33074297 1
0.1%
36.32983726 1
0.1%

동리코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean351.2992
Minimum150
Maximum369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-12-13T00:04:09.612784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile353
Q1355
median355
Q3355
95-th percentile369
Maximum369
Range219
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37.096981
Coefficient of variation (CV)0.10559939
Kurtosis23.910927
Mean351.2992
Median Absolute Deviation (MAD)0
Skewness-5.0044858
Sum264177
Variance1376.186
MonotonicityNot monotonic
2023-12-13T00:04:09.724750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
355 490
65.2%
369 156
 
20.7%
353 61
 
8.1%
368 11
 
1.5%
154 9
 
1.2%
152 7
 
0.9%
157 6
 
0.8%
358 5
 
0.7%
361 4
 
0.5%
150 2
 
0.3%
ValueCountFrequency (%)
150 2
 
0.3%
152 7
 
0.9%
154 9
 
1.2%
156 1
 
0.1%
157 6
 
0.8%
353 61
 
8.1%
355 490
65.2%
358 5
 
0.7%
361 4
 
0.5%
368 11
 
1.5%
ValueCountFrequency (%)
369 156
 
20.7%
368 11
 
1.5%
361 4
 
0.5%
358 5
 
0.7%
355 490
65.2%
353 61
 
8.1%
157 6
 
0.8%
156 1
 
0.1%
154 9
 
1.2%
152 7
 
0.9%

Interactions

2023-12-13T00:04:06.478856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.412190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.891948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:05.313933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.015719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.565727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.501060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.984709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:05.394963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.102906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.649507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.614193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:05.077688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:05.493963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.192165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.743543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.703542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:05.162545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:05.606061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.327641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.872129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.796100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:05.236210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:05.687634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:06.402865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:04:09.828045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드표준식별자노드타입유턴구분위도경도동리코드
노드표준식별자1.0000.0000.2810.7530.8960.999
노드타입0.0001.0000.2730.2720.2790.000
유턴구분0.2810.2731.0000.2630.2730.264
위도0.7530.2720.2631.0000.6770.734
경도0.8960.2790.2730.6771.0000.885
동리코드0.9990.0000.2640.7340.8851.000
2023-12-13T00:04:09.945272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노드표준식별자노드타입위도경도동리코드유턴구분
노드표준식별자1.000-0.0370.259-0.3640.8450.174
노드타입-0.0371.0000.0040.0410.0760.208
위도0.2590.0041.000-0.4670.3270.195
경도-0.3640.041-0.4671.000-0.4330.208
동리코드0.8450.0760.327-0.4331.0000.174
유턴구분0.1740.2080.1950.2080.1741.000

Missing values

2023-12-13T00:04:07.012140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:04:07.157074image/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

노드표준식별자노드타입노드한글명칭유턴구분링크수위도경도동리코드
03690014300108<NA>00128.38422635.980177369
13690014200101<NA>00128.38314535.978681369
23550032400101<NA>00128.3151336.111868355
33550032300101상공회의소네거리10128.35316136.116884355
43550030800101<NA>00128.36422236.110861355
53550030900101세무서사거리10128.3853936.110927355
63530002100101<NA>00128.24973136.159592353
73530002200101<NA>00128.24666436.15922353
83530002300108<NA>00128.23384836.157656353
93530002400108<NA>00128.23042236.157407353
노드표준식별자노드타입노드한글명칭유턴구분링크수위도경도동리코드
7423530006300101국사리924-2500128.25220736.158445353
7433530006400101국사리924-2500128.25334136.156205353
7443530006500101아포스마트시티아파트00128.26441936.154331353
7453550048700101<NA>00128.15074236.272193355
7463550048800101<NA>00128.14982436.275973355
7473550049000101<NA>00128.37498336.079717355
7483550048900101<NA>00128.37602336.07997355
7493550049200101<NA>00128.36189536.131177355
7503550049100101<NA>00128.36579236.13833355
7513550049300101진줄교차로00128.30079636.141097355