Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

DateTime1
Text1
Numeric2

Dataset

Description서울특별시 시내버스의 노선별 일별 주행거리 데이터로 2018년 3월부터 2018년 5월까지의 일별, 노선번호, 운행거리 자료입니다.
Author서울특별시
URLhttps://www.data.go.kr/data/15051742/fileData.do

Alerts

운행거리(Km) is highly overall correlated with 운행건수High correlation
운행건수 is highly overall correlated with 운행거리(Km)High correlation

Reproduction

Analysis started2023-12-12 20:04:11.636965
Analysis finished2023-12-12 20:04:12.683866
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

운행일자
Date

Distinct92
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-03-01 00:00:00
Maximum2018-05-31 00:00:00
2023-12-13T05:04:12.790335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:12.990040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

노선
Text

Distinct381
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:04:13.514167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.7185
Min length1

Characters and Unicode

Total characters37185
Distinct characters43
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row653
2nd row7734
3rd row9711A
4th row6512
5th row6640B
ValueCountFrequency (%)
700 39
 
0.4%
2012 38
 
0.4%
5511 38
 
0.4%
6714 37
 
0.4%
720 37
 
0.4%
5714 37
 
0.4%
5513 37
 
0.4%
1164 36
 
0.4%
147 36
 
0.4%
4432 36
 
0.4%
Other values (371) 9629
96.3%
2023-12-13T05:04:14.236929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7827
21.0%
2 4877
13.1%
6 4137
11.1%
3 3635
9.8%
7 3614
9.7%
5 3473
9.3%
4 3295
8.9%
0 3195
8.6%
8 658
 
1.8%
9 651
 
1.8%
Other values (33) 1823
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35362
95.1%
Other Letter 972
 
2.6%
Uppercase Letter 851
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 61
 
6.3%
송 61
 
6.3%
계 50
 
5.1%
상 50
 
5.1%
천 48
 
4.9%
양 48
 
4.9%
사 35
 
3.6%
당 35
 
3.6%
관 33
 
3.4%
진 33
 
3.4%
Other values (20) 518
53.3%
Decimal Number
ValueCountFrequency (%)
1 7827
22.1%
2 4877
13.8%
6 4137
11.7%
3 3635
10.3%
7 3614
10.2%
5 3473
9.8%
4 3295
9.3%
0 3195
9.0%
8 658
 
1.9%
9 651
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
N 459
53.9%
A 211
24.8%
B 181
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
Common 35362
95.1%
Hangul 972
 
2.6%
Latin 851
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 61
 
6.3%
송 61
 
6.3%
계 50
 
5.1%
상 50
 
5.1%
천 48
 
4.9%
양 48
 
4.9%
사 35
 
3.6%
당 35
 
3.6%
관 33
 
3.4%
진 33
 
3.4%
Other values (20) 518
53.3%
Common
ValueCountFrequency (%)
1 7827
22.1%
2 4877
13.8%
6 4137
11.7%
3 3635
10.3%
7 3614
10.2%
5 3473
9.8%
4 3295
9.3%
0 3195
9.0%
8 658
 
1.9%
9 651
 
1.8%
Latin
ValueCountFrequency (%)
N 459
53.9%
A 211
24.8%
B 181
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36213
97.4%
Hangul 972
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7827
21.6%
2 4877
13.5%
6 4137
11.4%
3 3635
10.0%
7 3614
10.0%
5 3473
9.6%
4 3295
9.1%
0 3195
8.8%
8 658
 
1.8%
9 651
 
1.8%
Other values (3) 851
 
2.3%
Hangul
ValueCountFrequency (%)
파 61
 
6.3%
송 61
 
6.3%
계 50
 
5.1%
상 50
 
5.1%
천 48
 
4.9%
양 48
 
4.9%
사 35
 
3.6%
당 35
 
3.6%
관 33
 
3.4%
진 33
 
3.4%
Other values (20) 518
53.3%

운행거리(Km)
Real number (ℝ)

HIGH CORRELATION 

Distinct7513
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3984.2638
Minimum30.025
Maximum32872.885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:04:14.425270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.025
5-th percentile297.564
Q11964.9375
median3662.747
Q35614.9282
95-th percentile8558.188
Maximum32872.885
Range32842.86
Interquartile range (IQR)3649.9907

Descriptive statistics

Standard deviation2664.5444
Coefficient of variation (CV)0.66876705
Kurtosis4.2597777
Mean3984.2638
Median Absolute Deviation (MAD)1762.8235
Skewness1.2028616
Sum39842638
Variance7099796.6
MonotonicityNot monotonic
2023-12-13T05:04:14.594388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
296.32 33
 
0.3%
204.732 29
 
0.3%
285.512 27
 
0.3%
284.584 25
 
0.2%
64.74 25
 
0.2%
286.952 24
 
0.2%
442.995 23
 
0.2%
302.18 22
 
0.2%
598.364 21
 
0.2%
289.8 21
 
0.2%
Other values (7503) 9750
97.5%
ValueCountFrequency (%)
30.025 1
 
< 0.1%
30.951 1
 
< 0.1%
38.404 1
 
< 0.1%
38.58 3
< 0.1%
38.803 1
 
< 0.1%
39.473 1
 
< 0.1%
40.577 1
 
< 0.1%
40.721 1
 
< 0.1%
40.76 3
< 0.1%
40.762 1
 
< 0.1%
ValueCountFrequency (%)
32872.885 1
< 0.1%
21698.583 1
< 0.1%
21698.096 1
< 0.1%
21696.886 1
< 0.1%
21696.326 1
< 0.1%
21696.148 1
< 0.1%
21693.178 1
< 0.1%
21693.021 1
< 0.1%
21691.873 1
< 0.1%
21690.943 1
< 0.1%

운행건수
Real number (ℝ)

HIGH CORRELATION 

Distinct199
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.723
Minimum3
Maximum304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:04:14.792552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8.95
Q178
median104
Q3133
95-th percentile183
Maximum304
Range301
Interquartile range (IQR)55

Descriptive statistics

Standard deviation46.0146
Coefficient of variation (CV)0.43523736
Kurtosis0.4302501
Mean105.723
Median Absolute Deviation (MAD)28
Skewness0.010150311
Sum1057230
Variance2117.3434
MonotonicityNot monotonic
2023-12-13T05:04:14.959147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 404
 
4.0%
72 206
 
2.1%
84 164
 
1.6%
88 162
 
1.6%
91 157
 
1.6%
120 152
 
1.5%
78 143
 
1.4%
108 143
 
1.4%
118 137
 
1.4%
96 131
 
1.3%
Other values (189) 8201
82.0%
ValueCountFrequency (%)
3 22
 
0.2%
4 404
4.0%
5 53
 
0.5%
6 1
 
< 0.1%
8 20
 
0.2%
9 1
 
< 0.1%
10 33
 
0.3%
11 6
 
0.1%
12 74
 
0.7%
18 6
 
0.1%
ValueCountFrequency (%)
304 2
 
< 0.1%
303 12
0.1%
302 1
 
< 0.1%
255 1
 
< 0.1%
254 6
 
0.1%
252 2
 
< 0.1%
222 17
0.2%
216 2
 
< 0.1%
215 14
0.1%
214 1
 
< 0.1%

Interactions

2023-12-13T05:04:12.192068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:11.931266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:12.318516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:04:12.065666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:04:15.059197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행일자운행거리(Km)운행건수
운행일자1.0000.1230.197
운행거리(Km)0.1231.0000.770
운행건수0.1970.7701.000
2023-12-13T05:04:15.155972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행거리(Km)운행건수
운행거리(Km)1.0000.684
운행건수0.6841.000

Missing values

2023-12-13T05:04:12.474643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:04:12.630774image/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

운행일자노선운행거리(Km)운행건수
92222018-03-256531587.02654
239692018-05-0377342485.0575
145902018-04-089711A5142.04760
32332018-03-0965124530.69119
167722018-04-146640B1827.27388
23322018-03-0722214170.594170
246502018-05-0566251816.03863
104632018-03-28N15우이295.384
241192018-05-0424154077.768160
304752018-05-212312A2105.71448
운행일자노선운행거리(Km)운행건수
186072018-04-196025954.809152
81962018-03-22877130.9519
217122018-04-2777153008.451115
22522018-03-071066173.952128
43932018-03-126723102.43181
283182018-05-1555131709.604117
116332018-04-0111271781.1288
81672018-03-227534533.77994
251752018-05-0711561528.99671
258442018-05-08855240.72112