Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory419.9 KiB
Average record size in memory43.0 B

Variable types

Numeric3
Text1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15067/S/1/datasetView.do

Alerts

정류소번호 is highly overall correlated with Y좌표High correlation
Y좌표 is highly overall correlated with 정류소번호High correlation
정류소번호 has unique valuesUnique

Reproduction

Analysis started2024-04-29 17:03:29.528278
Analysis finished2024-04-29 17:03:31.422050
Duration1.89 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정류소번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14158.12
Minimum1001
Maximum25990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T02:03:31.525873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2517.95
Q18580.75
median14292.5
Q320504.25
95-th percentile24318.05
Maximum25990
Range24989
Interquartile range (IQR)11923.5

Descriptive statistics

Standard deviation6912.0286
Coefficient of variation (CV)0.48820244
Kurtosis-1.1156037
Mean14158.12
Median Absolute Deviation (MAD)5844
Skewness-0.12510271
Sum1.415812 × 108
Variance47776140
MonotonicityNot monotonic
2024-04-30T02:03:31.675090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10017 1
 
< 0.1%
8128 1
 
< 0.1%
24360 1
 
< 0.1%
19984 1
 
< 0.1%
12223 1
 
< 0.1%
21183 1
 
< 0.1%
5168 1
 
< 0.1%
22920 1
 
< 0.1%
3594 1
 
< 0.1%
11830 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
ValueCountFrequency (%)
25990 1
< 0.1%
25989 1
< 0.1%
25988 1
< 0.1%
25784 1
< 0.1%
25783 1
< 0.1%
25782 1
< 0.1%
25781 1
< 0.1%
25760 1
< 0.1%
25758 1
< 0.1%
25754 1
< 0.1%

정류소명
Text

Distinct6363
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-30T02:03:31.921104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length7.2949
Min length2

Characters and Unicode

Total characters72949
Distinct characters650
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3684 ?
Unique (%)36.8%

Sample

1st row우이1교앞
2nd row신목중학교
3rd row답십리초등학교.현대시장
4th row개포동역
5th row믿음슈퍼
ValueCountFrequency (%)
현대아파트 13
 
0.1%
벽산아파트 12
 
0.1%
국민은행 12
 
0.1%
새마을금고 11
 
0.1%
경남아파트 11
 
0.1%
북서울꿈의숲 11
 
0.1%
삼성래미안아파트 11
 
0.1%
우성아파트 10
 
0.1%
성원아파트 9
 
0.1%
합정역 9
 
0.1%
Other values (6357) 9901
98.9%
2024-04-30T02:03:32.316190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2263
 
3.1%
아 2075
 
2.8%
파 2025
 
2.8%
트 2007
 
2.8%
. 1806
 
2.5%
교 1706
 
2.3%
구 1448
 
2.0%
역 1395
 
1.9%
대 1253
 
1.7%
지 1215
 
1.7%
Other values (640) 55756
76.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68211
93.5%
Decimal Number 2161
 
3.0%
Other Punctuation 1818
 
2.5%
Uppercase Letter 636
 
0.9%
Open Punctuation 42
 
0.1%
Close Punctuation 42
 
0.1%
Lowercase Letter 19
 
< 0.1%
Space Separator 10
 
< 0.1%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2263
 
3.3%
아 2075
 
3.0%
파 2025
 
3.0%
트 2007
 
2.9%
교 1706
 
2.5%
구 1448
 
2.1%
역 1395
 
2.0%
대 1253
 
1.8%
지 1215
 
1.8%
학 1180
 
1.7%
Other values (597) 51644
75.7%
Uppercase Letter
ValueCountFrequency (%)
T 107
16.8%
K 87
13.7%
A 65
10.2%
S 62
9.7%
C 52
8.2%
P 49
7.7%
G 39
 
6.1%
B 33
 
5.2%
M 29
 
4.6%
L 22
 
3.5%
Other values (13) 91
14.3%
Decimal Number
ValueCountFrequency (%)
1 622
28.8%
2 448
20.7%
3 316
14.6%
4 175
 
8.1%
5 149
 
6.9%
0 132
 
6.1%
7 107
 
5.0%
6 91
 
4.2%
9 77
 
3.6%
8 44
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 1806
99.3%
& 8
 
0.4%
· 4
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 15
78.9%
t 2
 
10.5%
k 2
 
10.5%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68211
93.5%
Common 4083
 
5.6%
Latin 655
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2263
 
3.3%
아 2075
 
3.0%
파 2025
 
3.0%
트 2007
 
2.9%
교 1706
 
2.5%
구 1448
 
2.1%
역 1395
 
2.0%
대 1253
 
1.8%
지 1215
 
1.8%
학 1180
 
1.7%
Other values (597) 51644
75.7%
Latin
ValueCountFrequency (%)
T 107
16.3%
K 87
13.3%
A 65
9.9%
S 62
9.5%
C 52
7.9%
P 49
7.5%
G 39
 
6.0%
B 33
 
5.0%
M 29
 
4.4%
L 22
 
3.4%
Other values (16) 110
16.8%
Common
ValueCountFrequency (%)
. 1806
44.2%
1 622
 
15.2%
2 448
 
11.0%
3 316
 
7.7%
4 175
 
4.3%
5 149
 
3.6%
0 132
 
3.2%
7 107
 
2.6%
6 91
 
2.2%
9 77
 
1.9%
Other values (7) 160
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68211
93.5%
ASCII 4734
 
6.5%
None 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2263
 
3.3%
아 2075
 
3.0%
파 2025
 
3.0%
트 2007
 
2.9%
교 1706
 
2.5%
구 1448
 
2.1%
역 1395
 
2.0%
대 1253
 
1.8%
지 1215
 
1.8%
학 1180
 
1.7%
Other values (597) 51644
75.7%
ASCII
ValueCountFrequency (%)
. 1806
38.1%
1 622
 
13.1%
2 448
 
9.5%
3 316
 
6.7%
4 175
 
3.7%
5 149
 
3.1%
0 132
 
2.8%
T 107
 
2.3%
7 107
 
2.3%
6 91
 
1.9%
Other values (32) 781
16.5%
None
ValueCountFrequency (%)
· 4
100.0%

X좌표
Real number (ℝ)

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98552
Minimum126.79781
Maximum127.18014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T02:03:32.451983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.79781
5-th percentile126.84473
Q1126.91748
median126.99493
Q3127.04951
95-th percentile127.12239
Maximum127.18014
Range0.38232694
Interquartile range (IQR)0.13202872

Descriptive statistics

Standard deviation0.084107682
Coefficient of variation (CV)0.00066234073
Kurtosis-0.8543201
Mean126.98552
Median Absolute Deviation (MAD)0.066646842
Skewness-0.051190259
Sum1269855.2
Variance0.0070741022
MonotonicityNot monotonic
2024-04-30T02:03:32.585536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.013138 2
 
< 0.1%
127.1444514585 2
 
< 0.1%
127.1441619343 2
 
< 0.1%
126.9373247519 1
 
< 0.1%
126.9034753573 1
 
< 0.1%
126.9187489917 1
 
< 0.1%
126.9456223075 1
 
< 0.1%
127.1030692112 1
 
< 0.1%
127.0394643451 1
 
< 0.1%
126.9720388292 1
 
< 0.1%
Other values (9987) 9987
99.9%
ValueCountFrequency (%)
126.797811 1
< 0.1%
126.797978 1
< 0.1%
126.798335 1
< 0.1%
126.798872 1
< 0.1%
126.7997884498 1
< 0.1%
126.800345 1
< 0.1%
126.801112 1
< 0.1%
126.80181 1
< 0.1%
126.801935005 1
< 0.1%
126.8021756851 1
< 0.1%
ValueCountFrequency (%)
127.18013794 1
< 0.1%
127.1799002887 1
< 0.1%
127.1797196537 1
< 0.1%
127.1794626974 1
< 0.1%
127.1794 1
< 0.1%
127.1784008104 1
< 0.1%
127.177967 1
< 0.1%
127.177918 1
< 0.1%
127.1777180853 1
< 0.1%
127.1774839621 1
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.551423
Minimum37.430712
Maximum37.690177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T02:03:32.713063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.430712
5-th percentile37.471818
Q137.503351
median37.550225
Q337.592551
95-th percentile37.647842
Maximum37.690177
Range0.25946532
Interquartile range (IQR)0.089200155

Descriptive statistics

Standard deviation0.055206361
Coefficient of variation (CV)0.0014701536
Kurtosis-0.80814313
Mean37.551423
Median Absolute Deviation (MAD)0.045061315
Skewness0.24326961
Sum375514.23
Variance0.0030477423
MonotonicityNot monotonic
2024-04-30T02:03:32.844571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.477818 2
 
< 0.1%
37.5536748887 2
 
< 0.1%
37.5537905826 2
 
< 0.1%
37.5490958683 1
 
< 0.1%
37.4973147822 1
 
< 0.1%
37.5117732064 1
 
< 0.1%
37.6261494835 1
 
< 0.1%
37.4792126295 1
 
< 0.1%
37.5434728132 1
 
< 0.1%
37.4782102141 1
 
< 0.1%
Other values (9987) 9987
99.9%
ValueCountFrequency (%)
37.4307116816 1
< 0.1%
37.4337174251 1
< 0.1%
37.434643292 1
< 0.1%
37.4347964213 1
< 0.1%
37.434809195 1
< 0.1%
37.4349830389 1
< 0.1%
37.4350042057 1
< 0.1%
37.4355241561 1
< 0.1%
37.4368629524 1
< 0.1%
37.4373210738 1
< 0.1%
ValueCountFrequency (%)
37.690177 1
< 0.1%
37.6899483575 1
< 0.1%
37.6893500743 1
< 0.1%
37.6893310475 1
< 0.1%
37.6891946492 1
< 0.1%
37.6890118581 1
< 0.1%
37.688568 1
< 0.1%
37.6879883235 1
< 0.1%
37.6879397664 1
< 0.1%
37.687443 1
< 0.1%

Interactions

2024-04-30T02:03:30.982521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:03:30.446673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:03:30.700116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:03:31.082926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:03:30.524492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:03:30.784461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:03:31.192909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:03:30.610126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:03:30.883338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T02:03:32.928961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소번호X좌표Y좌표
정류소번호1.0000.9040.861
X좌표0.9041.0000.585
Y좌표0.8610.5851.000
2024-04-30T02:03:33.009164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소번호X좌표Y좌표
정류소번호1.000-0.098-0.672
X좌표-0.0981.0000.247
Y좌표-0.6720.2471.000

Missing values

2024-04-30T02:03:31.302579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T02:03:31.381671image/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

정류소번호정류소명X좌표Y좌표
344110017우이1교앞127.03265237.645293
588015114신목중학교126.87221837.537345
17696198답십리초등학교.현대시장127.057237.568381
996923354개포동역127.06640937.488693
31779520믿음슈퍼127.03212237.627089
7483270금호베스트빌앞126.99057637.522619
19666756서울시립대입구127.05380337.582856
763519002문래동남성아파트126.89231237.511202
6773198서울역126.9737837.553563
661816569롯데3차APT126.84657737.552872
정류소번호정류소명X좌표Y좌표
377910800성황당127.04362837.676949
892821595가족생활동126.95776237.466927
944122502동문빌라126.9917637.482901
10784252도선고등학교.꽃재교회127.02845437.566955
361710302방학3동주민센터127.02790437.659168
624816016공항중학교.공항초등학교126.81580137.560725
735018149남문시장.신한은행독산지점126.89863137.472965
510613543보영약국126.95203337.561134
13304832왕십리도선동주민센터127.02591637.567708
1045124293마천동금호어울림1차아파트127.15428537.497043