Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory810.5 KiB
Average record size in memory83.0 B

Variable types

Numeric3
Text3
DateTime2
Boolean1

Dataset

Description세종특별자치시 공공자전거(어울링) 이용현황입니다. (기간 : 2022. 4.~2022. 7.) 행복도시의 쾌적한 도시 환경에 적합한 세종시 어울링 원하는 곳에서 쉽게 대여하고 사용 후 가까운 곳에 편리하게 반납하는, 자전거를 이용한 대중교통으로, 평일엔 출‧퇴근용으로 사용하고, 야간 및 휴일에는 호수공원 등에서 레저용으로 사용 할 수 있는 창조적인 녹색교통수단입니다.
URLhttps://www.data.go.kr/data/15106191/fileData.do

Alerts

환승 유무 has constant value ""Constant
주행거리 is highly overall correlated with 주행시간High correlation
주행시간 is highly overall correlated with 주행거리High correlation
주행거리 is highly skewed (γ1 = 40.38772935)Skewed
순번 has unique valuesUnique
주행거리 has 672 (6.7%) zerosZeros
주행시간 has 433 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-12 16:16:03.068729
Analysis finished2023-12-12 16:16:05.067268
Duration2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3721151.4
Minimum3672511
Maximum3771959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:16:05.143527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3672511
5-th percentile3677513.8
Q13696011.5
median3720055
Q33747920.2
95-th percentile3766994.4
Maximum3771959
Range99448
Interquartile range (IQR)51908.75

Descriptive statistics

Standard deviation28925.977
Coefficient of variation (CV)0.0077733944
Kurtosis-1.2013316
Mean3721151.4
Median Absolute Deviation (MAD)25884.5
Skewness0.083806214
Sum3.7211514 × 1010
Variance8.3671216 × 108
MonotonicityNot monotonic
2023-12-13T01:16:05.286204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3766384 1
 
< 0.1%
3721825 1
 
< 0.1%
3719148 1
 
< 0.1%
3747475 1
 
< 0.1%
3770850 1
 
< 0.1%
3754667 1
 
< 0.1%
3726475 1
 
< 0.1%
3678189 1
 
< 0.1%
3716034 1
 
< 0.1%
3716994 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3672511 1
< 0.1%
3672512 1
< 0.1%
3672517 1
< 0.1%
3672521 1
< 0.1%
3672539 1
< 0.1%
3672551 1
< 0.1%
3672570 1
< 0.1%
3672584 1
< 0.1%
3672589 1
< 0.1%
3672601 1
< 0.1%
ValueCountFrequency (%)
3771959 1
< 0.1%
3771957 1
< 0.1%
3771952 1
< 0.1%
3771941 1
< 0.1%
3771936 1
< 0.1%
3771927 1
< 0.1%
3771925 1
< 0.1%
3771908 1
< 0.1%
3771873 1
< 0.1%
3771867 1
< 0.1%

자전거고유번호
Text

Distinct2444
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:16:05.545271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters140000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique434 ?
Unique (%)4.3%

Sample

1st row001_0110_02338
2nd row001_0110_02676
3rd row001_0110_02771
4th row001_0110_02942
5th row001_0110_00580
ValueCountFrequency (%)
001_0110_02056 15
 
0.1%
001_0110_02839 15
 
0.1%
001_0110_02980 15
 
0.1%
001_0110_02981 13
 
0.1%
001_0110_02490 13
 
0.1%
001_0110_02617 13
 
0.1%
001_0110_02881 13
 
0.1%
001_0110_03009 13
 
0.1%
001_0110_02683 13
 
0.1%
001_0110_02710 12
 
0.1%
Other values (2434) 9865
98.7%
2023-12-13T01:16:05.945778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54386
38.8%
1 36127
25.8%
_ 20000
 
14.3%
2 8001
 
5.7%
8 3349
 
2.4%
9 3255
 
2.3%
3 3157
 
2.3%
7 3061
 
2.2%
6 2931
 
2.1%
5 2890
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120000
85.7%
Connector Punctuation 20000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54386
45.3%
1 36127
30.1%
2 8001
 
6.7%
8 3349
 
2.8%
9 3255
 
2.7%
3 3157
 
2.6%
7 3061
 
2.6%
6 2931
 
2.4%
5 2890
 
2.4%
4 2843
 
2.4%
Connector Punctuation
ValueCountFrequency (%)
_ 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54386
38.8%
1 36127
25.8%
_ 20000
 
14.3%
2 8001
 
5.7%
8 3349
 
2.4%
9 3255
 
2.3%
3 3157
 
2.3%
7 3061
 
2.2%
6 2931
 
2.1%
5 2890
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54386
38.8%
1 36127
25.8%
_ 20000
 
14.3%
2 8001
 
5.7%
8 3349
 
2.4%
9 3255
 
2.3%
3 3157
 
2.3%
7 3061
 
2.2%
6 2931
 
2.1%
5 2890
 
2.1%

시작 대여소
Text

Distinct570
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:16:06.333292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9942
Min length6

Characters and Unicode

Total characters79942
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.3%

Sample

1st rowSJ_00207
2nd rowSJ_00333
3rd rowSJ_00440
4th rowSJ_00543
5th rowSJ_00649
ValueCountFrequency (%)
sj_00400 172
 
1.7%
sj_00415 109
 
1.1%
sj_00273 105
 
1.1%
sj_00320 104
 
1.0%
sj_00440 93
 
0.9%
sj_00278 92
 
0.9%
sj_00480 84
 
0.8%
sj_00390 84
 
0.8%
sj_00646 78
 
0.8%
sj_00460 76
 
0.8%
Other values (560) 9003
90.0%
2023-12-13T01:16:06.893949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23746
29.7%
S 10000
12.5%
J 10000
12.5%
_ 10000
12.5%
4 4053
 
5.1%
3 3699
 
4.6%
2 3646
 
4.6%
5 3347
 
4.2%
1 3166
 
4.0%
6 2265
 
2.8%
Other values (3) 6020
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49942
62.5%
Uppercase Letter 20000
25.0%
Connector Punctuation 10000
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23746
47.5%
4 4053
 
8.1%
3 3699
 
7.4%
2 3646
 
7.3%
5 3347
 
6.7%
1 3166
 
6.3%
6 2265
 
4.5%
8 2188
 
4.4%
7 2049
 
4.1%
9 1783
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S 10000
50.0%
J 10000
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59942
75.0%
Latin 20000
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23746
39.6%
_ 10000
16.7%
4 4053
 
6.8%
3 3699
 
6.2%
2 3646
 
6.1%
5 3347
 
5.6%
1 3166
 
5.3%
6 2265
 
3.8%
8 2188
 
3.7%
7 2049
 
3.4%
Latin
ValueCountFrequency (%)
S 10000
50.0%
J 10000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23746
29.7%
S 10000
12.5%
J 10000
12.5%
_ 10000
12.5%
4 4053
 
5.1%
3 3699
 
4.6%
2 3646
 
4.6%
5 3347
 
4.2%
1 3166
 
4.0%
6 2265
 
2.8%
Other values (3) 6020
 
7.5%

대여시간
Date

Distinct9909
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-04-01 00:01:58
Maximum2022-04-15 13:10:24
2023-12-13T01:16:07.080400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:07.261775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반납 대여소
Text

Distinct565
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:16:07.664080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9928
Min length6

Characters and Unicode

Total characters79928
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st rowSJ_00583
2nd rowSJ_00065
3rd rowSJ_00085
4th rowSJ_00010
5th rowSJ_00649
ValueCountFrequency (%)
sj_00400 154
 
1.5%
sj_00415 129
 
1.3%
sj_00273 109
 
1.1%
sj_00440 106
 
1.1%
sj_00320 102
 
1.0%
sj_00278 95
 
0.9%
sj_00057 91
 
0.9%
sj_00480 87
 
0.9%
sj_00341 84
 
0.8%
sj_00646 77
 
0.8%
Other values (555) 8966
89.7%
2023-12-13T01:16:08.606582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23668
29.6%
S 10000
12.5%
J 10000
12.5%
_ 9998
12.5%
4 4154
 
5.2%
3 3708
 
4.6%
2 3575
 
4.5%
5 3500
 
4.4%
1 3160
 
4.0%
6 2231
 
2.8%
Other values (4) 5934
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49928
62.5%
Uppercase Letter 20000
25.0%
Connector Punctuation 9998
 
12.5%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23668
47.4%
4 4154
 
8.3%
3 3708
 
7.4%
2 3575
 
7.2%
5 3500
 
7.0%
1 3160
 
6.3%
6 2231
 
4.5%
8 2140
 
4.3%
7 2036
 
4.1%
9 1756
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
S 10000
50.0%
J 10000
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9998
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59928
75.0%
Latin 20000
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23668
39.5%
_ 9998
16.7%
4 4154
 
6.9%
3 3708
 
6.2%
2 3575
 
6.0%
5 3500
 
5.8%
1 3160
 
5.3%
6 2231
 
3.7%
8 2140
 
3.6%
7 2036
 
3.4%
Other values (2) 1758
 
2.9%
Latin
ValueCountFrequency (%)
S 10000
50.0%
J 10000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79928
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23668
29.6%
S 10000
12.5%
J 10000
12.5%
_ 9998
12.5%
4 4154
 
5.2%
3 3708
 
4.6%
2 3575
 
4.5%
5 3500
 
4.4%
1 3160
 
4.0%
6 2231
 
2.8%
Other values (4) 5934
 
7.4%

반납시간
Date

Distinct9905
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-04-01 00:11:25
Maximum2022-04-15 14:33:30
2023-12-13T01:16:08.780447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:08.938197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

주행거리
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4535
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2978.661
Minimum0
Maximum572186
Zeros672
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:16:09.094677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1656
median1254
Q32843
95-th percentile11215.3
Maximum572186
Range572186
Interquartile range (IQR)2187

Descriptive statistics

Standard deviation10237.142
Coefficient of variation (CV)3.4368268
Kurtosis2052.2362
Mean2978.661
Median Absolute Deviation (MAD)786
Skewness40.387729
Sum29786610
Variance1.0479907 × 108
MonotonicityNot monotonic
2023-12-13T01:16:09.251543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 672
 
6.7%
606 17
 
0.2%
1015 15
 
0.1%
765 14
 
0.1%
458 13
 
0.1%
1067 13
 
0.1%
1014 13
 
0.1%
1024 12
 
0.1%
879 12
 
0.1%
482 12
 
0.1%
Other values (4525) 9207
92.1%
ValueCountFrequency (%)
0 672
6.7%
2 1
 
< 0.1%
4 1
 
< 0.1%
6 2
 
< 0.1%
9 3
 
< 0.1%
10 2
 
< 0.1%
12 2
 
< 0.1%
14 2
 
< 0.1%
16 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
572186 1
< 0.1%
533471 1
< 0.1%
451765 1
< 0.1%
153115 1
< 0.1%
111584 1
< 0.1%
88935 1
< 0.1%
60512 1
< 0.1%
58329 1
< 0.1%
57614 1
< 0.1%
55230 1
< 0.1%

주행시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct174
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.9508
Minimum0
Maximum915
Zeros433
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:16:09.386488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q324
95-th percentile72
Maximum915
Range915
Interquartile range (IQR)18

Descriptive statistics

Standard deviation35.018815
Coefficient of variation (CV)1.6714787
Kurtosis205.41833
Mean20.9508
Median Absolute Deviation (MAD)7
Skewness11.046656
Sum209508
Variance1226.3174
MonotonicityNot monotonic
2023-12-13T01:16:09.564077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 608
 
6.1%
7 553
 
5.5%
4 539
 
5.4%
5 534
 
5.3%
8 492
 
4.9%
0 433
 
4.3%
9 424
 
4.2%
3 411
 
4.1%
10 403
 
4.0%
11 338
 
3.4%
Other values (164) 5265
52.6%
ValueCountFrequency (%)
0 433
4.3%
1 198
 
2.0%
2 307
3.1%
3 411
4.1%
4 539
5.4%
5 534
5.3%
6 608
6.1%
7 553
5.5%
8 492
4.9%
9 424
4.2%
ValueCountFrequency (%)
915 1
< 0.1%
898 1
< 0.1%
832 1
< 0.1%
730 1
< 0.1%
716 1
< 0.1%
620 1
< 0.1%
591 1
< 0.1%
579 1
< 0.1%
565 1
< 0.1%
546 1
< 0.1%

환승 유무
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
10000 
ValueCountFrequency (%)
False 10000
100.0%
2023-12-13T01:16:09.674234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-13T01:16:04.462209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:03.791004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:04.120224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:04.571069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:03.918435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:04.223732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:04.690552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:04.030543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:16:04.341908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:16:09.733748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번주행거리주행시간
순번1.0000.0000.062
주행거리0.0001.0000.322
주행시간0.0620.3221.000
2023-12-13T01:16:09.834610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번주행거리주행시간
순번1.000-0.034-0.047
주행거리-0.0341.0000.755
주행시간-0.0470.7551.000

Missing values

2023-12-13T01:16:04.828874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:16:05.004832image/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

순번자전거고유번호시작 대여소대여시간반납 대여소반납시간주행거리주행시간환승 유무
897103766384001_0110_02338SJ_002072022-04-14 18:10:05SJ_005832022-04-14 18:19:1615889N
415623714091001_0110_02676SJ_003332022-04-07 08:35:46SJ_000652022-04-07 08:44:0712718N
284023700922001_0110_02771SJ_004402022-04-05 12:39:48SJ_000852022-04-05 12:45:167845N
288403701362001_0110_02942SJ_005432022-04-05 14:06:22SJ_000102022-04-05 14:13:596737N
166803689195001_0110_00580SJ_006492022-04-03 17:16:01SJ_006492022-04-03 17:44:05028N
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