Overview

Dataset statistics

Number of variables4
Number of observations2129
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.9 KiB
Average record size in memory35.1 B

Variable types

Numeric3
Text1

Dataset

Description경상북도 구미시 버스정보시스템DB의 버스정류장 테이블 데이터로 정류장식별자, 정류장 명칭, 단축명, 위치좌표등을 제공합니다.
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15049485/fileData.do

Alerts

정류장서비스식별자 is highly overall correlated with 경도High correlation
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 정류장서비스식별자 and 1 other fieldsHigh correlation
정류장서비스식별자 has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:54:20.175870
Analysis finished2023-12-11 22:54:21.301073
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정류장서비스식별자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2129
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11712.446
Minimum10001
Maximum24896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 KiB
2023-12-12T07:54:21.360119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10109.4
Q110542
median11086
Q312424
95-th percentile14269.6
Maximum24896
Range14895
Interquartile range (IQR)1882

Descriptive statistics

Standard deviation1526.3735
Coefficient of variation (CV)0.13032065
Kurtosis11.39508
Mean11712.446
Median Absolute Deviation (MAD)937
Skewness1.9897492
Sum24935797
Variance2329816.2
MonotonicityStrictly increasing
2023-12-12T07:54:21.535770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 1
 
< 0.1%
12232 1
 
< 0.1%
12248 1
 
< 0.1%
12247 1
 
< 0.1%
12246 1
 
< 0.1%
12245 1
 
< 0.1%
12244 1
 
< 0.1%
12243 1
 
< 0.1%
12242 1
 
< 0.1%
12241 1
 
< 0.1%
Other values (2119) 2119
99.5%
ValueCountFrequency (%)
10001 1
< 0.1%
10002 1
< 0.1%
10003 1
< 0.1%
10004 1
< 0.1%
10005 1
< 0.1%
10006 1
< 0.1%
10007 1
< 0.1%
10008 1
< 0.1%
10009 1
< 0.1%
10010 1
< 0.1%
ValueCountFrequency (%)
24896 1
< 0.1%
24871 1
< 0.1%
24870 1
< 0.1%
24795 1
< 0.1%
24792 1
< 0.1%
15178 1
< 0.1%
14372 1
< 0.1%
14371 1
< 0.1%
14370 1
< 0.1%
14369 1
< 0.1%

한글명칭
Text

Distinct2094
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size16.8 KiB
2023-12-12T07:54:21.772434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length8.3720056
Min length2

Characters and Unicode

Total characters17824
Distinct characters446
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

Unique2059 ?
Unique (%)96.7%

Sample

1st row4공단(현진아파트방면)
2nd row4공단(우미린더스카이건너방면)
3rd row4공단입구건너(인동농협옥계지점)
4th row거상빌딩
5th row4공단입구(옥계방면)
ValueCountFrequency (%)
건너 14
 
0.6%
입구 8
 
0.3%
종점 7
 
0.3%
앞 6
 
0.3%
월림2리 6
 
0.3%
초곡리 4
 
0.2%
도개2리 4
 
0.2%
지방리 4
 
0.2%
도개1리 4
 
0.2%
용신2리 4
 
0.2%
Other values (2156) 2289
97.4%
2023-12-12T07:54:22.141490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 916
 
5.1%
) 608
 
3.4%
( 607
 
3.4%
방 542
 
3.0%
면 524
 
2.9%
건 500
 
2.8%
너 498
 
2.8%
구 434
 
2.4%
동 315
 
1.8%
앞 311
 
1.7%
Other values (436) 12569
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15672
87.9%
Close Punctuation 608
 
3.4%
Open Punctuation 607
 
3.4%
Decimal Number 575
 
3.2%
Space Separator 223
 
1.3%
Uppercase Letter 122
 
0.7%
Lowercase Letter 10
 
0.1%
Other Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 916
 
5.8%
방 542
 
3.5%
면 524
 
3.3%
건 500
 
3.2%
너 498
 
3.2%
구 434
 
2.8%
동 315
 
2.0%
앞 311
 
2.0%
산 285
 
1.8%
입 262
 
1.7%
Other values (405) 11085
70.7%
Uppercase Letter
ValueCountFrequency (%)
A 19
15.6%
K 18
14.8%
L 15
12.3%
G 15
12.3%
T 13
10.7%
S 11
9.0%
P 8
6.6%
C 7
 
5.7%
E 5
 
4.1%
I 4
 
3.3%
Other values (5) 7
 
5.7%
Decimal Number
ValueCountFrequency (%)
2 257
44.7%
1 202
35.1%
3 69
 
12.0%
4 34
 
5.9%
5 6
 
1.0%
9 3
 
0.5%
7 2
 
0.3%
0 1
 
0.2%
8 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
, 4
57.1%
& 2
28.6%
/ 1
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 608
100.0%
Open Punctuation
ValueCountFrequency (%)
( 607
100.0%
Space Separator
ValueCountFrequency (%)
223
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15672
87.9%
Common 2020
 
11.3%
Latin 132
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 916
 
5.8%
방 542
 
3.5%
면 524
 
3.3%
건 500
 
3.2%
너 498
 
3.2%
구 434
 
2.8%
동 315
 
2.0%
앞 311
 
2.0%
산 285
 
1.8%
입 262
 
1.7%
Other values (405) 11085
70.7%
Latin
ValueCountFrequency (%)
A 19
14.4%
K 18
13.6%
L 15
11.4%
G 15
11.4%
T 13
9.8%
S 11
8.3%
e 10
7.6%
P 8
6.1%
C 7
 
5.3%
E 5
 
3.8%
Other values (6) 11
8.3%
Common
ValueCountFrequency (%)
) 608
30.1%
( 607
30.0%
2 257
12.7%
223
 
11.0%
1 202
 
10.0%
3 69
 
3.4%
4 34
 
1.7%
5 6
 
0.3%
, 4
 
0.2%
9 3
 
0.1%
Other values (5) 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15672
87.9%
ASCII 2152
 
12.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 916
 
5.8%
방 542
 
3.5%
면 524
 
3.3%
건 500
 
3.2%
너 498
 
3.2%
구 434
 
2.8%
동 315
 
2.0%
앞 311
 
2.0%
산 285
 
1.8%
입 262
 
1.7%
Other values (405) 11085
70.7%
ASCII
ValueCountFrequency (%)
) 608
28.3%
( 607
28.2%
2 257
11.9%
223
 
10.4%
1 202
 
9.4%
3 69
 
3.2%
4 34
 
1.6%
A 19
 
0.9%
K 18
 
0.8%
L 15
 
0.7%
Other values (21) 100
 
4.6%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct2121
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.3853
Minimum128.09177
Maximum128.61625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 KiB
2023-12-12T07:54:22.286672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.09177
5-th percentile128.22186
Q1128.3292
median128.38403
Q3128.44073
95-th percentile128.55105
Maximum128.61625
Range0.5244784
Interquartile range (IQR)0.1115286

Descriptive statistics

Standard deviation0.096706742
Coefficient of variation (CV)0.000753254
Kurtosis0.086245438
Mean128.3853
Median Absolute Deviation (MAD)0.0561902
Skewness-0.13706646
Sum273332.31
Variance0.009352194
MonotonicityNot monotonic
2023-12-12T07:54:22.432958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.3642489 2
 
0.1%
128.3770209 2
 
0.1%
128.1809772 2
 
0.1%
128.3833577 2
 
0.1%
128.4235725 2
 
0.1%
128.4030187 2
 
0.1%
128.3819002 2
 
0.1%
128.4422349 2
 
0.1%
128.4827923 1
 
< 0.1%
128.4433919 1
 
< 0.1%
Other values (2111) 2111
99.2%
ValueCountFrequency (%)
128.0917728 1
< 0.1%
128.0918679 1
< 0.1%
128.0936202 1
< 0.1%
128.0985681 1
< 0.1%
128.0987307 1
< 0.1%
128.1151807 1
< 0.1%
128.1154554 1
< 0.1%
128.1178823 1
< 0.1%
128.1182139 1
< 0.1%
128.1199257 1
< 0.1%
ValueCountFrequency (%)
128.6162512 1
< 0.1%
128.6159368 1
< 0.1%
128.6133945 1
< 0.1%
128.6133797 1
< 0.1%
128.6129063 1
< 0.1%
128.6128214 1
< 0.1%
128.6127638 1
< 0.1%
128.6125317 1
< 0.1%
128.6082048 1
< 0.1%
128.6080965 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct2118
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.100077
Minimum35.848649
Maximum36.36297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.8 KiB
2023-12-12T07:54:22.573567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.848649
5-th percentile35.891907
Q136.033614
median36.11172
Q336.159739
95-th percentile36.285561
Maximum36.36297
Range0.51432031
Interquartile range (IQR)0.12612528

Descriptive statistics

Standard deviation0.11156919
Coefficient of variation (CV)0.0030905527
Kurtosis-0.32299595
Mean36.100077
Median Absolute Deviation (MAD)0.05851551
Skewness-0.20351343
Sum76857.065
Variance0.012447685
MonotonicityNot monotonic
2023-12-12T07:54:22.698271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.10067197 2
 
0.1%
36.11937541 2
 
0.1%
36.1613464 2
 
0.1%
36.11976207 2
 
0.1%
35.98367567 2
 
0.1%
36.246087 2
 
0.1%
36.25990911 2
 
0.1%
36.28282929 2
 
0.1%
36.09748567 2
 
0.1%
35.97586287 2
 
0.1%
Other values (2108) 2109
99.1%
ValueCountFrequency (%)
35.84864933 1
< 0.1%
35.84920994 1
< 0.1%
35.84922041 1
< 0.1%
35.84974538 1
< 0.1%
35.85015683 1
< 0.1%
35.85027565 1
< 0.1%
35.85055554 1
< 0.1%
35.85078639 1
< 0.1%
35.85098558 1
< 0.1%
35.851117 1
< 0.1%
ValueCountFrequency (%)
36.36296964 1
< 0.1%
36.36287848 1
< 0.1%
36.34671204 1
< 0.1%
36.3466227 1
< 0.1%
36.34343437 1
< 0.1%
36.34343123 1
< 0.1%
36.34111548 1
< 0.1%
36.34101906 1
< 0.1%
36.33998255 1
< 0.1%
36.33987006 1
< 0.1%

Interactions

2023-12-12T07:54:20.925938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:54:20.484941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:54:20.706286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:54:21.009532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:54:20.560878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:54:20.784876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:54:21.081381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:54:20.632766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:54:20.854715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:54:22.799163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류장서비스식별자위도경도
정류장서비스식별자1.0000.6290.845
위도0.6291.0000.687
경도0.8450.6871.000
2023-12-12T07:54:22.913745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류장서비스식별자위도경도
정류장서비스식별자1.0000.233-0.545
위도0.2331.000-0.548
경도-0.545-0.5481.000

Missing values

2023-12-12T07:54:21.180353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:54:21.269706image/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

정류장서비스식별자한글명칭위도경도
0100014공단(현진아파트방면)128.42956936.137361
1100024공단(우미린더스카이건너방면)128.42958536.136961
2100034공단입구건너(인동농협옥계지점)128.41735736.137816
310004거상빌딩128.41704336.136046
4100054공단입구(옥계방면)128.41776636.137739
510006우미린더스카이앞128.43282936.137983
610007우미린더스카이건너128.43236936.137558
710008가네골(백자리방면)128.22271836.246613
810009가네골(무이리방면)128.22281936.246655
910010가라골건너128.43054536.132325
정류장서비스식별자한글명칭위도경도
211914369덕곡동건너128.15903236.118843
212014370야동(야동인2리방면)건너128.25481136.172195
212114371인3리(야동방면)128.25650936.169113
212214372인3리(아포방면)128.25638436.16905
212315178월드메르디앙128.15111936.117361
212424792영남종합전기128.55554835.959431
212524795풋살경기장(50사단)128.56604235.957647
212624870동명교통128.55357335.984979
212724871동명교통128.55373935.984849
212824896칠곡경대병원역건너128.5606135.960058