Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory996.1 KiB
Average record size in memory102.0 B

Variable types

Text3
DateTime2
Numeric6

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15182/F/1/datasetView.do

Alerts

대여 대여소번호 is highly overall correlated with 반납대여소번호High correlation
대여거치대 is highly overall correlated with 반납거치대 and 1 other fieldsHigh correlation
반납대여소번호 is highly overall correlated with 대여 대여소번호High correlation
반납거치대 is highly overall correlated with 대여거치대 and 1 other fieldsHigh correlation
이용거리 is highly overall correlated with 대여거치대 and 1 other fieldsHigh correlation
대여거치대 has 9073 (90.7%) zerosZeros
반납거치대 has 9073 (90.7%) zerosZeros
이용거리 has 9100 (91.0%) zerosZeros

Reproduction

Analysis started2023-12-11 07:31:19.338105
Analysis finished2023-12-11 07:31:26.184106
Duration6.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct6170
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:26.419030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters14
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

Unique3575 ?
Unique (%)35.8%

Sample

1st rowSPB-36216
2nd rowSPB-32317
3rd rowSPB-30702
4th rowSPB-42396
5th rowSPB-34019
ValueCountFrequency (%)
spb-32018 9
 
0.1%
spb-33637 7
 
0.1%
spb-31626 6
 
0.1%
spb-35024 6
 
0.1%
spb-32006 6
 
0.1%
spb-44632 6
 
0.1%
spb-32383 6
 
0.1%
spb-32191 6
 
0.1%
spb-33217 5
 
< 0.1%
spb-38146 5
 
< 0.1%
Other values (6160) 9938
99.4%
2023-12-11T16:31:27.017757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 10765
12.0%
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
4 7024
7.8%
1 4910
 
5.5%
2 4904
 
5.4%
0 4406
 
4.9%
5 3791
 
4.2%
Other values (4) 14200
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
55.6%
Uppercase Letter 30000
33.3%
Dash Punctuation 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 10765
21.5%
4 7024
14.0%
1 4910
9.8%
2 4904
9.8%
0 4406
8.8%
5 3791
 
7.6%
6 3780
 
7.6%
7 3527
 
7.1%
8 3509
 
7.0%
9 3384
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
S 10000
33.3%
P 10000
33.3%
B 10000
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
66.7%
Latin 30000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
3 10765
17.9%
- 10000
16.7%
4 7024
11.7%
1 4910
8.2%
2 4904
8.2%
0 4406
7.3%
5 3791
 
6.3%
6 3780
 
6.3%
7 3527
 
5.9%
8 3509
 
5.8%
Latin
ValueCountFrequency (%)
S 10000
33.3%
P 10000
33.3%
B 10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 10765
12.0%
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
4 7024
7.8%
1 4910
 
5.5%
2 4904
 
5.4%
0 4406
 
4.9%
5 3791
 
4.2%
Other values (4) 14200
15.8%

대여일시
Date

Distinct9171
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-10-01 00:00:02
Maximum2020-10-01 22:43:59
2023-12-11T16:31:27.195053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:27.358660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여 대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1799
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1372.0241
Minimum101
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:27.520311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile186
Q1591.75
median1209
Q32021
95-th percentile3124
Maximum9999
Range9898
Interquartile range (IQR)1429.25

Descriptive statistics

Standard deviation920.98532
Coefficient of variation (CV)0.67126031
Kurtosis0.96483562
Mean1372.0241
Median Absolute Deviation (MAD)697
Skewness0.72430841
Sum13720241
Variance848213.96
MonotonicityNot monotonic
2023-12-11T16:31:27.704195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 94
 
0.9%
502 78
 
0.8%
152 58
 
0.6%
583 56
 
0.6%
186 53
 
0.5%
272 46
 
0.5%
2102 46
 
0.5%
2622 43
 
0.4%
1268 43
 
0.4%
565 39
 
0.4%
Other values (1789) 9444
94.4%
ValueCountFrequency (%)
101 3
 
< 0.1%
102 8
0.1%
103 6
 
0.1%
104 3
 
< 0.1%
105 3
 
< 0.1%
106 19
0.2%
107 9
0.1%
108 4
 
< 0.1%
109 2
 
< 0.1%
111 6
 
0.1%
ValueCountFrequency (%)
9999 2
 
< 0.1%
4711 2
 
< 0.1%
4652 1
 
< 0.1%
3600 9
0.1%
3587 2
 
< 0.1%
3586 3
 
< 0.1%
3582 1
 
< 0.1%
3581 5
0.1%
3579 2
 
< 0.1%
3578 2
 
< 0.1%

대여 대여소명
Text

Distinct1798
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:28.029709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length9.9542
Min length2

Characters and Unicode

Total characters99542
Distinct characters536
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique303 ?
Unique (%)3.0%

Sample

1st row가락몰 업무동
2nd row하나로마트 창동점
3rd row천일초교 사거리
4th row구의삼성쉐르빌 앞
5th row안암골벽산아파트(후문)
ValueCountFrequency (%)
앞 2759
 
12.7%
1번출구 523
 
2.4%
출구 485
 
2.2%
옆 466
 
2.1%
3번출구 332
 
1.5%
뒤 240
 
1.1%
입구 227
 
1.0%
2번출구 194
 
0.9%
사거리 192
 
0.9%
교차로 172
 
0.8%
Other values (2128) 16201
74.3%
2023-12-11T16:31:28.524508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11816
 
11.9%
구 3698
 
3.7%
앞 3266
 
3.3%
역 3019
 
3.0%
번 2723
 
2.7%
출 2643
 
2.7%
동 1875
 
1.9%
교 1677
 
1.7%
1 1637
 
1.6%
리 1324
 
1.3%
Other values (526) 65864
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79156
79.5%
Space Separator 11816
 
11.9%
Decimal Number 5259
 
5.3%
Uppercase Letter 1200
 
1.2%
Close Punctuation 878
 
0.9%
Open Punctuation 878
 
0.9%
Lowercase Letter 126
 
0.1%
Dash Punctuation 101
 
0.1%
Other Punctuation 95
 
0.1%
Math Symbol 21
 
< 0.1%
Other values (2) 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3698
 
4.7%
앞 3266
 
4.1%
역 3019
 
3.8%
번 2723
 
3.4%
출 2643
 
3.3%
동 1875
 
2.4%
교 1677
 
2.1%
리 1324
 
1.7%
원 1279
 
1.6%
아 1085
 
1.4%
Other values (470) 56567
71.5%
Uppercase Letter
ValueCountFrequency (%)
S 164
13.7%
K 140
11.7%
C 110
9.2%
T 102
8.5%
L 88
 
7.3%
G 87
 
7.2%
B 77
 
6.4%
I 76
 
6.3%
A 67
 
5.6%
M 50
 
4.2%
Other values (14) 239
19.9%
Lowercase Letter
ValueCountFrequency (%)
e 44
34.9%
k 23
18.3%
t 17
 
13.5%
s 10
 
7.9%
v 8
 
6.3%
l 6
 
4.8%
o 4
 
3.2%
m 4
 
3.2%
c 4
 
3.2%
n 4
 
3.2%
Decimal Number
ValueCountFrequency (%)
1 1637
31.1%
2 951
18.1%
3 746
14.2%
4 515
 
9.8%
5 341
 
6.5%
0 307
 
5.8%
8 255
 
4.8%
7 201
 
3.8%
6 180
 
3.4%
9 126
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 83
87.4%
? 10
 
10.5%
& 2
 
2.1%
Math Symbol
ValueCountFrequency (%)
~ 13
61.9%
+ 8
38.1%
Space Separator
ValueCountFrequency (%)
11816
100.0%
Close Punctuation
ValueCountFrequency (%)
) 878
100.0%
Open Punctuation
ValueCountFrequency (%)
( 878
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79158
79.5%
Common 19058
 
19.1%
Latin 1326
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3698
 
4.7%
앞 3266
 
4.1%
역 3019
 
3.8%
번 2723
 
3.4%
출 2643
 
3.3%
동 1875
 
2.4%
교 1677
 
2.1%
리 1324
 
1.7%
원 1279
 
1.6%
아 1085
 
1.4%
Other values (471) 56569
71.5%
Latin
ValueCountFrequency (%)
S 164
12.4%
K 140
 
10.6%
C 110
 
8.3%
T 102
 
7.7%
L 88
 
6.6%
G 87
 
6.6%
B 77
 
5.8%
I 76
 
5.7%
A 67
 
5.1%
M 50
 
3.8%
Other values (25) 365
27.5%
Common
ValueCountFrequency (%)
11816
62.0%
1 1637
 
8.6%
2 951
 
5.0%
) 878
 
4.6%
( 878
 
4.6%
3 746
 
3.9%
4 515
 
2.7%
5 341
 
1.8%
0 307
 
1.6%
8 255
 
1.3%
Other values (10) 734
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79156
79.5%
ASCII 20384
 
20.5%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11816
58.0%
1 1637
 
8.0%
2 951
 
4.7%
) 878
 
4.3%
( 878
 
4.3%
3 746
 
3.7%
4 515
 
2.5%
5 341
 
1.7%
0 307
 
1.5%
8 255
 
1.3%
Other values (45) 2060
 
10.1%
Hangul
ValueCountFrequency (%)
구 3698
 
4.7%
앞 3266
 
4.1%
역 3019
 
3.8%
번 2723
 
3.4%
출 2643
 
3.3%
동 1875
 
2.4%
교 1677
 
2.1%
리 1324
 
1.7%
원 1279
 
1.6%
아 1085
 
1.4%
Other values (470) 56567
71.5%
None
ValueCountFrequency (%)
㈜ 2
100.0%

대여거치대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6834
Minimum0
Maximum40
Zeros9073
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:28.715898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7747674
Coefficient of variation (CV)4.0602392
Kurtosis43.161901
Mean0.6834
Median Absolute Deviation (MAD)0
Skewness5.7429895
Sum6834
Variance7.6993344
MonotonicityNot monotonic
2023-12-11T16:31:28.923718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 9073
90.7%
1 98
 
1.0%
4 97
 
1.0%
2 89
 
0.9%
5 75
 
0.8%
6 75
 
0.8%
8 74
 
0.7%
7 67
 
0.7%
10 66
 
0.7%
3 56
 
0.6%
Other values (22) 230
 
2.3%
ValueCountFrequency (%)
0 9073
90.7%
1 98
 
1.0%
2 89
 
0.9%
3 56
 
0.6%
4 97
 
1.0%
5 75
 
0.8%
6 75
 
0.8%
7 67
 
0.7%
8 74
 
0.7%
9 40
 
0.4%
ValueCountFrequency (%)
40 2
 
< 0.1%
37 1
 
< 0.1%
36 2
 
< 0.1%
35 1
 
< 0.1%
30 2
 
< 0.1%
29 1
 
< 0.1%
27 3
< 0.1%
26 2
 
< 0.1%
25 6
0.1%
23 3
< 0.1%

반납일시
Date

Distinct9124
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-10-01 00:03:56
Maximum2020-10-01 22:48:32
2023-12-11T16:31:29.124898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:29.282060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반납대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1764
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1365.7969
Minimum101
Maximum4711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:29.449657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile185
Q1589
median1209
Q32002
95-th percentile3123
Maximum4711
Range4610
Interquartile range (IQR)1413

Descriptive statistics

Standard deviation914.93187
Coefficient of variation (CV)0.66988867
Kurtosis-0.40615598
Mean1365.7969
Median Absolute Deviation (MAD)697
Skewness0.59983559
Sum13657969
Variance837100.32
MonotonicityNot monotonic
2023-12-11T16:31:29.632760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 93
 
0.9%
502 85
 
0.9%
583 69
 
0.7%
152 67
 
0.7%
186 62
 
0.6%
272 53
 
0.5%
2622 49
 
0.5%
565 45
 
0.4%
1268 44
 
0.4%
907 43
 
0.4%
Other values (1754) 9390
93.9%
ValueCountFrequency (%)
101 4
 
< 0.1%
102 8
 
0.1%
103 10
0.1%
104 2
 
< 0.1%
105 4
 
< 0.1%
106 24
0.2%
107 4
 
< 0.1%
108 7
 
0.1%
109 6
 
0.1%
111 9
 
0.1%
ValueCountFrequency (%)
4711 5
 
0.1%
4652 2
 
< 0.1%
3600 14
0.1%
3588 2
 
< 0.1%
3587 2
 
< 0.1%
3586 3
 
< 0.1%
3582 2
 
< 0.1%
3581 3
 
< 0.1%
3578 1
 
< 0.1%
3575 2
 
< 0.1%

반납대여소명
Text

Distinct1763
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:29.948663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length25
Mean length10.0105
Min length2

Characters and Unicode

Total characters100105
Distinct characters534
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)3.1%

Sample

1st row잠실나들목
2nd row겸재교 진입부
3rd row건강보험 강동지사kt
4th row광진경찰서
5th row마장동 주민센터
ValueCountFrequency (%)
앞 2803
 
12.8%
1번출구 560
 
2.6%
출구 476
 
2.2%
옆 439
 
2.0%
3번출구 302
 
1.4%
뒤 234
 
1.1%
입구 213
 
1.0%
2번출구 206
 
0.9%
사거리 187
 
0.9%
4번출구 184
 
0.8%
Other values (2102) 16326
74.4%
2023-12-11T16:31:30.510068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11955
 
11.9%
구 3763
 
3.8%
앞 3324
 
3.3%
역 3121
 
3.1%
번 2831
 
2.8%
출 2731
 
2.7%
동 1909
 
1.9%
1 1685
 
1.7%
교 1644
 
1.6%
리 1319
 
1.3%
Other values (524) 65823
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79300
79.2%
Space Separator 11955
 
11.9%
Decimal Number 5466
 
5.5%
Uppercase Letter 1230
 
1.2%
Open Punctuation 900
 
0.9%
Close Punctuation 900
 
0.9%
Lowercase Letter 130
 
0.1%
Dash Punctuation 111
 
0.1%
Other Punctuation 89
 
0.1%
Math Symbol 18
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3763
 
4.7%
앞 3324
 
4.2%
역 3121
 
3.9%
번 2831
 
3.6%
출 2731
 
3.4%
동 1909
 
2.4%
교 1644
 
2.1%
리 1319
 
1.7%
원 1223
 
1.5%
아 1122
 
1.4%
Other values (469) 56313
71.0%
Uppercase Letter
ValueCountFrequency (%)
S 147
12.0%
C 129
10.5%
K 126
10.2%
G 99
 
8.0%
L 94
 
7.6%
T 86
 
7.0%
I 82
 
6.7%
A 77
 
6.3%
B 69
 
5.6%
J 55
 
4.5%
Other values (13) 266
21.6%
Lowercase Letter
ValueCountFrequency (%)
e 47
36.2%
k 19
14.6%
t 16
 
12.3%
s 9
 
6.9%
l 8
 
6.2%
v 7
 
5.4%
m 6
 
4.6%
o 6
 
4.6%
c 6
 
4.6%
n 4
 
3.1%
Decimal Number
ValueCountFrequency (%)
1 1685
30.8%
2 1026
18.8%
3 713
13.0%
4 539
 
9.9%
5 378
 
6.9%
0 318
 
5.8%
8 255
 
4.7%
7 216
 
4.0%
6 198
 
3.6%
9 138
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 82
92.1%
? 5
 
5.6%
& 2
 
2.2%
Math Symbol
ValueCountFrequency (%)
~ 15
83.3%
+ 3
 
16.7%
Space Separator
ValueCountFrequency (%)
11955
100.0%
Open Punctuation
ValueCountFrequency (%)
( 900
100.0%
Close Punctuation
ValueCountFrequency (%)
) 900
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 111
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79301
79.2%
Common 19444
 
19.4%
Latin 1360
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3763
 
4.7%
앞 3324
 
4.2%
역 3121
 
3.9%
번 2831
 
3.6%
출 2731
 
3.4%
동 1909
 
2.4%
교 1644
 
2.1%
리 1319
 
1.7%
원 1223
 
1.5%
아 1122
 
1.4%
Other values (470) 56314
71.0%
Latin
ValueCountFrequency (%)
S 147
 
10.8%
C 129
 
9.5%
K 126
 
9.3%
G 99
 
7.3%
L 94
 
6.9%
T 86
 
6.3%
I 82
 
6.0%
A 77
 
5.7%
B 69
 
5.1%
J 55
 
4.0%
Other values (24) 396
29.1%
Common
ValueCountFrequency (%)
11955
61.5%
1 1685
 
8.7%
2 1026
 
5.3%
( 900
 
4.6%
) 900
 
4.6%
3 713
 
3.7%
4 539
 
2.8%
5 378
 
1.9%
0 318
 
1.6%
8 255
 
1.3%
Other values (10) 775
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79300
79.2%
ASCII 20804
 
20.8%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11955
57.5%
1 1685
 
8.1%
2 1026
 
4.9%
( 900
 
4.3%
) 900
 
4.3%
3 713
 
3.4%
4 539
 
2.6%
5 378
 
1.8%
0 318
 
1.5%
8 255
 
1.2%
Other values (44) 2135
 
10.3%
Hangul
ValueCountFrequency (%)
구 3763
 
4.7%
앞 3324
 
4.2%
역 3121
 
3.9%
번 2831
 
3.6%
출 2731
 
3.4%
동 1909
 
2.4%
교 1644
 
2.1%
리 1319
 
1.7%
원 1223
 
1.5%
아 1122
 
1.4%
Other values (469) 56313
71.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

반납거치대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.707
Minimum0
Maximum40
Zeros9073
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:30.707540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9516474
Coefficient of variation (CV)4.1748902
Kurtosis48.5164
Mean0.707
Median Absolute Deviation (MAD)0
Skewness6.112397
Sum7070
Variance8.7122222
MonotonicityNot monotonic
2023-12-11T16:31:30.897854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 9073
90.7%
1 106
 
1.1%
3 84
 
0.8%
2 78
 
0.8%
7 73
 
0.7%
5 72
 
0.7%
6 70
 
0.7%
4 69
 
0.7%
8 66
 
0.7%
9 62
 
0.6%
Other values (28) 247
 
2.5%
ValueCountFrequency (%)
0 9073
90.7%
1 106
 
1.1%
2 78
 
0.8%
3 84
 
0.8%
4 69
 
0.7%
5 72
 
0.7%
6 70
 
0.7%
7 73
 
0.7%
8 66
 
0.7%
9 62
 
0.6%
ValueCountFrequency (%)
40 3
< 0.1%
39 2
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
34 2
< 0.1%
33 1
 
< 0.1%
32 2
< 0.1%
31 1
 
< 0.1%
30 2
< 0.1%
29 2
< 0.1%

이용시간
Real number (ℝ)

Distinct211
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.3342
Minimum1
Maximum563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:31.105575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q113
median31
Q358
95-th percentile113
Maximum563
Range562
Interquartile range (IQR)45

Descriptive statistics

Standard deviation36.646105
Coefficient of variation (CV)0.88658072
Kurtosis8.9946837
Mean41.3342
Median Absolute Deviation (MAD)21
Skewness1.8290427
Sum413342
Variance1342.937
MonotonicityNot monotonic
2023-12-11T16:31:31.266426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 277
 
2.8%
7 271
 
2.7%
6 267
 
2.7%
4 250
 
2.5%
8 249
 
2.5%
9 242
 
2.4%
5 237
 
2.4%
12 218
 
2.2%
13 210
 
2.1%
15 199
 
2.0%
Other values (201) 7580
75.8%
ValueCountFrequency (%)
1 9
 
0.1%
2 137
1.4%
3 147
1.5%
4 250
2.5%
5 237
2.4%
6 267
2.7%
7 271
2.7%
8 249
2.5%
9 242
2.4%
10 277
2.8%
ValueCountFrequency (%)
563 1
< 0.1%
500 1
< 0.1%
322 1
< 0.1%
286 1
< 0.1%
279 1
< 0.1%
275 1
< 0.1%
273 1
< 0.1%
262 1
< 0.1%
259 1
< 0.1%
247 1
< 0.1%

이용거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct697
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean579.21788
Minimum0
Maximum70040
Zeros9100
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:31.494594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3311
Maximum70040
Range70040
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3096.0315
Coefficient of variation (CV)5.3451933
Kurtosis177.42306
Mean579.21788
Median Absolute Deviation (MAD)0
Skewness11.127303
Sum5792178.8
Variance9585411.3
MonotonicityNot monotonic
2023-12-11T16:31:32.079452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9100
91.0%
940.0 6
 
0.1%
790.0 5
 
0.1%
2990.0 5
 
0.1%
1590.0 4
 
< 0.1%
840.0 4
 
< 0.1%
1840.0 4
 
< 0.1%
2910.0 4
 
< 0.1%
1360.0 4
 
< 0.1%
1200.0 4
 
< 0.1%
Other values (687) 860
 
8.6%
ValueCountFrequency (%)
0.0 9100
91.0%
20.0 1
 
< 0.1%
70.0 1
 
< 0.1%
120.0 1
 
< 0.1%
130.0 1
 
< 0.1%
176.28 1
 
< 0.1%
176.4 1
 
< 0.1%
200.0 1
 
< 0.1%
220.0 2
 
< 0.1%
230.0 1
 
< 0.1%
ValueCountFrequency (%)
70040.0 1
< 0.1%
65920.0 1
< 0.1%
65520.0 1
< 0.1%
63350.0 1
< 0.1%
63300.0 1
< 0.1%
59080.0 1
< 0.1%
55690.0 1
< 0.1%
55670.0 1
< 0.1%
54900.0 1
< 0.1%
51010.0 1
< 0.1%

Interactions

2023-12-11T16:31:25.198043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:21.315721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:22.008807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:22.685474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:23.515543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:24.517710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:25.299680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:21.415232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:22.107008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:22.862521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:23.612902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:24.621162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:25.405128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:21.546154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:22.213099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:23.007874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:23.727393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:24.731901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:25.511672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:21.644425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:22.312806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:23.144916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:24.107533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:24.845853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:25.629542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:21.784389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:22.449310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:23.275090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:24.263662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:24.985071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:25.729705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:21.882241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:22.564459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:23.397701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:24.397196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:25.090139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:31:32.237168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.0000.0180.6870.0360.0070.000
대여거치대0.0181.0000.0440.7170.0000.517
반납대여소번호0.6870.0441.0000.0730.0800.037
반납거치대0.0360.7170.0731.0000.0000.442
이용시간0.0070.0000.0800.0001.0000.191
이용거리0.0000.5170.0370.4420.1911.000
2023-12-11T16:31:32.373785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.000-0.0480.542-0.049-0.021-0.040
대여거치대-0.0481.000-0.0440.9970.0250.828
반납대여소번호0.542-0.0441.000-0.045-0.036-0.039
반납거치대-0.0490.997-0.0451.0000.0260.829
이용시간-0.0210.025-0.0360.0261.0000.048
이용거리-0.0400.828-0.0390.8290.0481.000

Missing values

2023-12-11T16:31:25.909354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:31:26.104427image/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

자전거번호대여일시대여 대여소번호대여 대여소명대여거치대반납일시반납대여소번호반납대여소명반납거치대이용시간이용거리
62909SPB-362162020-10-01 20:01:122658가락몰 업무동02020-10-01 20:47:532613잠실나들목0470.0
48927SPB-323172020-10-01 17:42:511716하나로마트 창동점02020-10-01 18:32:411452겸재교 진입부0500.0
15695SPB-307022020-10-01 12:18:011060천일초교 사거리02020-10-01 12:24:141047건강보험 강동지사kt060.0
7150SPB-423962020-10-01 07:02:43551구의삼성쉐르빌 앞02020-10-01 07:15:293520광진경찰서0130.0
39963SPB-340192020-10-01 16:44:41673안암골벽산아파트(후문)02020-10-01 17:18:35586마장동 주민센터0340.0
15170SPB-187142020-10-01 12:01:391365선잠단지 앞52020-10-01 12:13:51340혜화동 로터리4111910.0
5772SPB-325942020-10-01 03:08:13153성산2교 사거리02020-10-01 04:21:342525반포쇼핑타운 2동 앞0730.0
46764SPB-313162020-10-01 17:19:001157강서구청02020-10-01 18:15:062709강변아파트 310동 앞0560.0
49252SPB-354292020-10-01 18:28:56207여의나루역 1번출구 앞02020-10-01 18:35:26207여의나루역 1번출구 앞070.0
38335SPB-439152020-10-01 16:34:31502뚝섬유원지역 1번출구 앞02020-10-01 17:04:542613잠실나들목0300.0
자전거번호대여일시대여 대여소번호대여 대여소명대여거치대반납일시반납대여소번호반납대여소명반납거치대이용시간이용거리
27446SPB-384712020-10-01 13:49:56182망원2빗물펌프장 앞02020-10-01 15:16:05106합정역 7번출구 앞0860.0
28787SPB-428072020-10-01 15:18:13324신세계백화점 본점 앞02020-10-01 15:31:28321KEB 하나금융그룹 명동사옥 옆0130.0
20793SPB-312422020-10-01 13:33:532706강서공업고등학교 앞02020-10-01 13:48:561108공항시장역 2번출구 뒤0150.0
43997SPB-404262020-10-01 17:40:202647잠실 자전거 수리센터 앞02020-10-01 17:52:372610여흥레이크빌 앞 (석촌호수 까페거리)0120.0
60556SPB-205352020-10-01 18:42:242105미성동 신림체육센터32020-10-01 20:23:521906신도림역 1번 출구 앞69316310.0
53251SPB-362502020-10-01 17:41:292266서초역 3번출구02020-10-01 19:08:452387래미안강남힐즈 사거리0870.0
62042SPB-397212020-10-01 20:30:181984구로구청02020-10-01 20:39:121985구로도서관090.0
56133SPB-327482020-10-01 18:21:561132등촌역 7번출구02020-10-01 19:37:501132등촌역 7번출구0760.0
15224SPB-371122020-10-01 11:56:582732마곡수명산 1-2단지02020-10-01 12:14:522710라이품 공영주차장 앞0180.0
50978SPB-406692020-10-01 17:35:462414도곡역 아카데미스위트 앞02020-10-01 18:49:512372대치역 사거리0740.0