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 9621 (96.2%) zerosZeros
반납거치대 has 9621 (96.2%) zerosZeros
이용거리 has 9589 (95.9%) zerosZeros

Reproduction

Analysis started2023-12-11 07:31:03.240435
Analysis finished2023-12-11 07:31:10.778589
Duration7.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct6351
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:11.043743image/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

Unique3885 ?
Unique (%)38.9%

Sample

1st rowSPB-51106
2nd rowSPB-35555
3rd rowSPB-44979
4th rowSPB-35587
5th rowSPB-43907
ValueCountFrequency (%)
spb-50198 7
 
0.1%
spb-50973 7
 
0.1%
spb-33133 7
 
0.1%
spb-50154 6
 
0.1%
spb-51168 6
 
0.1%
spb-30780 6
 
0.1%
spb-37893 6
 
0.1%
spb-37856 6
 
0.1%
spb-41389 6
 
0.1%
spb-51415 6
 
0.1%
Other values (6341) 9937
99.4%
2023-12-11T16:31:11.562363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
3 9481
10.5%
5 5784
6.4%
4 5777
6.4%
1 5509
 
6.1%
0 5164
 
5.7%
2 4424
 
4.9%
Other values (4) 13861
15.4%

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 9481
19.0%
5 5784
11.6%
4 5777
11.6%
1 5509
11.0%
0 5164
10.3%
2 4424
8.8%
6 3532
 
7.1%
7 3488
 
7.0%
8 3446
 
6.9%
9 3395
 
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 (%)
- 10000
16.7%
3 9481
15.8%
5 5784
9.6%
4 5777
9.6%
1 5509
9.2%
0 5164
8.6%
2 4424
7.4%
6 3532
 
5.9%
7 3488
 
5.8%
8 3446
 
5.7%
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 (%)
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
3 9481
10.5%
5 5784
6.4%
4 5777
6.4%
1 5509
 
6.1%
0 5164
 
5.7%
2 4424
 
4.9%
Other values (4) 13861
15.4%

대여일시
Date

Distinct9472
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-11-01 00:00:01
Maximum2020-11-02 16:15:25
2023-12-11T16:31:11.835880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:12.038201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여 대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1857
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1407.3488
Minimum101
Maximum4711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:12.250138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile203.95
Q1617
median1233
Q32080
95-th percentile3310.05
Maximum4711
Range4610
Interquartile range (IQR)1463

Descriptive statistics

Standard deviation929.71712
Coefficient of variation (CV)0.66061599
Kurtosis-0.47488709
Mean1407.3488
Median Absolute Deviation (MAD)694
Skewness0.59032614
Sum14073488
Variance864373.92
MonotonicityNot monotonic
2023-12-11T16:31:12.434476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153 42
 
0.4%
2102 38
 
0.4%
207 31
 
0.3%
502 30
 
0.3%
3533 30
 
0.3%
646 29
 
0.3%
2715 28
 
0.3%
1210 25
 
0.2%
1308 24
 
0.2%
232 24
 
0.2%
Other values (1847) 9699
97.0%
ValueCountFrequency (%)
101 4
 
< 0.1%
102 12
0.1%
103 8
0.1%
104 8
0.1%
105 7
0.1%
106 12
0.1%
107 8
0.1%
108 5
0.1%
109 4
 
< 0.1%
111 4
 
< 0.1%
ValueCountFrequency (%)
4711 3
 
< 0.1%
4702 1
 
< 0.1%
4652 1
 
< 0.1%
3600 6
0.1%
3588 1
 
< 0.1%
3587 6
0.1%
3586 6
0.1%
3582 4
 
< 0.1%
3581 5
0.1%
3579 11
0.1%

대여 대여소명
Text

Distinct1856
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:12.717366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length25
Mean length10.0303
Min length2

Characters and Unicode

Total characters100303
Distinct characters540
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

Unique251 ?
Unique (%)2.5%

Sample

1st row창신3동주민센터
2nd row휘경2동 주민센터
3rd row신대방삼거리역 3번출구쪽
4th row공항시장역 2번출구 뒤
5th row일동제약 사거리
ValueCountFrequency (%)
앞 2608
 
12.0%
옆 492
 
2.3%
1번출구 455
 
2.1%
출구 453
 
2.1%
사거리 268
 
1.2%
3번출구 257
 
1.2%
뒤 236
 
1.1%
교차로 230
 
1.1%
2번출구 227
 
1.0%
5번출구 216
 
1.0%
Other values (2188) 16266
74.9%
2023-12-11T16:31:13.225198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11728
 
11.7%
구 3851
 
3.8%
역 3178
 
3.2%
앞 3129
 
3.1%
번 2901
 
2.9%
출 2772
 
2.8%
동 1911
 
1.9%
교 1630
 
1.6%
1 1588
 
1.6%
리 1474
 
1.5%
Other values (530) 66141
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79714
79.5%
Space Separator 11728
 
11.7%
Decimal Number 5335
 
5.3%
Uppercase Letter 1282
 
1.3%
Open Punctuation 965
 
1.0%
Close Punctuation 965
 
1.0%
Other Punctuation 113
 
0.1%
Dash Punctuation 95
 
0.1%
Lowercase Letter 84
 
0.1%
Math Symbol 14
 
< 0.1%
Other values (2) 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3851
 
4.8%
역 3178
 
4.0%
앞 3129
 
3.9%
번 2901
 
3.6%
출 2772
 
3.5%
동 1911
 
2.4%
교 1630
 
2.0%
리 1474
 
1.8%
사 1170
 
1.5%
대 1111
 
1.4%
Other values (475) 56587
71.0%
Uppercase Letter
ValueCountFrequency (%)
K 143
11.2%
C 119
 
9.3%
T 117
 
9.1%
S 116
 
9.0%
G 93
 
7.3%
L 87
 
6.8%
A 86
 
6.7%
M 73
 
5.7%
B 72
 
5.6%
P 69
 
5.4%
Other values (13) 307
23.9%
Lowercase Letter
ValueCountFrequency (%)
e 26
31.0%
k 12
14.3%
t 12
14.3%
l 7
 
8.3%
n 6
 
7.1%
s 4
 
4.8%
o 4
 
4.8%
c 4
 
4.8%
m 4
 
4.8%
y 3
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 1588
29.8%
2 945
17.7%
3 735
13.8%
4 522
 
9.8%
5 368
 
6.9%
0 296
 
5.5%
8 273
 
5.1%
7 250
 
4.7%
6 204
 
3.8%
9 154
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 94
83.2%
? 10
 
8.8%
& 9
 
8.0%
Math Symbol
ValueCountFrequency (%)
~ 10
71.4%
+ 4
 
28.6%
Space Separator
ValueCountFrequency (%)
11728
100.0%
Open Punctuation
ValueCountFrequency (%)
( 965
100.0%
Close Punctuation
ValueCountFrequency (%)
) 965
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79715
79.5%
Common 19222
 
19.2%
Latin 1366
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3851
 
4.8%
역 3178
 
4.0%
앞 3129
 
3.9%
번 2901
 
3.6%
출 2772
 
3.5%
동 1911
 
2.4%
교 1630
 
2.0%
리 1474
 
1.8%
사 1170
 
1.5%
대 1111
 
1.4%
Other values (476) 56588
71.0%
Latin
ValueCountFrequency (%)
K 143
 
10.5%
C 119
 
8.7%
T 117
 
8.6%
S 116
 
8.5%
G 93
 
6.8%
L 87
 
6.4%
A 86
 
6.3%
M 73
 
5.3%
B 72
 
5.3%
P 69
 
5.1%
Other values (24) 391
28.6%
Common
ValueCountFrequency (%)
11728
61.0%
1 1588
 
8.3%
( 965
 
5.0%
) 965
 
5.0%
2 945
 
4.9%
3 735
 
3.8%
4 522
 
2.7%
5 368
 
1.9%
0 296
 
1.5%
8 273
 
1.4%
Other values (10) 837
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79714
79.5%
ASCII 20588
 
20.5%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11728
57.0%
1 1588
 
7.7%
( 965
 
4.7%
) 965
 
4.7%
2 945
 
4.6%
3 735
 
3.6%
4 522
 
2.5%
5 368
 
1.8%
0 296
 
1.4%
8 273
 
1.3%
Other values (44) 2203
 
10.7%
Hangul
ValueCountFrequency (%)
구 3851
 
4.8%
역 3178
 
4.0%
앞 3129
 
3.9%
번 2901
 
3.6%
출 2772
 
3.5%
동 1911
 
2.4%
교 1630
 
2.0%
리 1474
 
1.8%
사 1170
 
1.5%
대 1111
 
1.4%
Other values (475) 56587
71.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

대여거치대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2511
Minimum0
Maximum34
Zeros9621
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:13.420243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5754038
Coefficient of variation (CV)6.2740094
Kurtosis86.70897
Mean0.2511
Median Absolute Deviation (MAD)0
Skewness8.2903965
Sum2511
Variance2.481897
MonotonicityNot monotonic
2023-12-11T16:31:13.567904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 9621
96.2%
1 58
 
0.6%
3 36
 
0.4%
5 33
 
0.3%
9 32
 
0.3%
7 30
 
0.3%
8 30
 
0.3%
4 29
 
0.3%
6 28
 
0.3%
10 27
 
0.3%
Other values (14) 76
 
0.8%
ValueCountFrequency (%)
0 9621
96.2%
1 58
 
0.6%
2 21
 
0.2%
3 36
 
0.4%
4 29
 
0.3%
5 33
 
0.3%
6 28
 
0.3%
7 30
 
0.3%
8 30
 
0.3%
9 32
 
0.3%
ValueCountFrequency (%)
34 1
 
< 0.1%
25 2
 
< 0.1%
22 1
 
< 0.1%
20 3
< 0.1%
19 2
 
< 0.1%
18 3
< 0.1%
17 5
0.1%
16 5
0.1%
15 6
0.1%
14 6
0.1%

반납일시
Date

Distinct9436
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-11-01 00:05:32
Maximum2020-11-02 16:18:40
2023-12-11T16:31:13.719918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:13.894474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반납대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1810
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1399.3525
Minimum3
Maximum4711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:14.373315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile205
Q1607.75
median1220
Q32067
95-th percentile3406
Maximum4711
Range4708
Interquartile range (IQR)1459.25

Descriptive statistics

Standard deviation932.39095
Coefficient of variation (CV)0.6663017
Kurtosis-0.47783845
Mean1399.3525
Median Absolute Deviation (MAD)700
Skewness0.60158062
Sum13993525
Variance869352.88
MonotonicityNot monotonic
2023-12-11T16:31:14.536345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153 36
 
0.4%
502 34
 
0.3%
114 34
 
0.3%
210 32
 
0.3%
1308 32
 
0.3%
2177 30
 
0.3%
284 29
 
0.3%
2173 28
 
0.3%
1149 27
 
0.3%
1211 27
 
0.3%
Other values (1800) 9691
96.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
101 3
 
< 0.1%
102 9
0.1%
103 5
0.1%
104 8
0.1%
105 4
 
< 0.1%
106 11
0.1%
107 10
0.1%
108 5
0.1%
109 3
 
< 0.1%
ValueCountFrequency (%)
4711 3
 
< 0.1%
4702 1
 
< 0.1%
3600 7
0.1%
3588 2
 
< 0.1%
3587 7
0.1%
3586 6
0.1%
3582 2
 
< 0.1%
3581 3
 
< 0.1%
3579 11
0.1%
3578 7
0.1%

반납대여소명
Text

Distinct1809
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:14.772644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length9.9512
Min length2

Characters and Unicode

Total characters99512
Distinct characters539
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

Unique251 ?
Unique (%)2.5%

Sample

1st row신당역 12번 출구 뒤
2nd row외국어대 정문 앞
3rd row도림4거리
4th row마곡나루역 2번 출구
5th row삼호물산버스정류장(23370) 옆
ValueCountFrequency (%)
앞 2580
 
11.9%
출구 484
 
2.2%
옆 463
 
2.1%
1번출구 455
 
2.1%
사거리 252
 
1.2%
뒤 242
 
1.1%
3번출구 237
 
1.1%
5번출구 237
 
1.1%
2번출구 233
 
1.1%
교차로 221
 
1.0%
Other values (2131) 16188
75.0%
2023-12-11T16:31:15.219424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11604
 
11.7%
구 3802
 
3.8%
역 3197
 
3.2%
앞 3032
 
3.0%
번 2899
 
2.9%
출 2777
 
2.8%
동 1897
 
1.9%
교 1555
 
1.6%
1 1528
 
1.5%
리 1467
 
1.5%
Other values (529) 65754
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78828
79.2%
Space Separator 11604
 
11.7%
Decimal Number 5144
 
5.2%
Uppercase Letter 1573
 
1.6%
Close Punctuation 981
 
1.0%
Open Punctuation 981
 
1.0%
Lowercase Letter 154
 
0.2%
Other Punctuation 129
 
0.1%
Dash Punctuation 82
 
0.1%
Math Symbol 18
 
< 0.1%
Other values (2) 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3802
 
4.8%
역 3197
 
4.1%
앞 3032
 
3.8%
번 2899
 
3.7%
출 2777
 
3.5%
동 1897
 
2.4%
교 1555
 
2.0%
리 1467
 
1.9%
사 1199
 
1.5%
대 1103
 
1.4%
Other values (473) 55900
70.9%
Uppercase Letter
ValueCountFrequency (%)
K 175
11.1%
C 175
11.1%
S 165
10.5%
G 129
 
8.2%
L 121
 
7.7%
T 104
 
6.6%
B 89
 
5.7%
M 89
 
5.7%
I 81
 
5.1%
D 71
 
4.5%
Other values (13) 374
23.8%
Lowercase Letter
ValueCountFrequency (%)
e 43
27.9%
n 32
20.8%
l 18
11.7%
y 16
 
10.4%
k 16
 
10.4%
t 9
 
5.8%
s 9
 
5.8%
v 5
 
3.2%
c 2
 
1.3%
m 2
 
1.3%
Decimal Number
ValueCountFrequency (%)
1 1528
29.7%
2 930
18.1%
3 675
13.1%
4 476
 
9.3%
5 409
 
8.0%
0 279
 
5.4%
8 278
 
5.4%
7 240
 
4.7%
6 189
 
3.7%
9 140
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 100
77.5%
& 19
 
14.7%
? 9
 
7.0%
· 1
 
0.8%
Math Symbol
ValueCountFrequency (%)
~ 10
55.6%
+ 8
44.4%
Space Separator
ValueCountFrequency (%)
11604
100.0%
Close Punctuation
ValueCountFrequency (%)
) 981
100.0%
Open Punctuation
ValueCountFrequency (%)
( 981
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78838
79.2%
Common 18947
 
19.0%
Latin 1727
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3802
 
4.8%
역 3197
 
4.1%
앞 3032
 
3.8%
번 2899
 
3.7%
출 2777
 
3.5%
동 1897
 
2.4%
교 1555
 
2.0%
리 1467
 
1.9%
사 1199
 
1.5%
대 1103
 
1.4%
Other values (474) 55910
70.9%
Latin
ValueCountFrequency (%)
K 175
 
10.1%
C 175
 
10.1%
S 165
 
9.6%
G 129
 
7.5%
L 121
 
7.0%
T 104
 
6.0%
B 89
 
5.2%
M 89
 
5.2%
I 81
 
4.7%
D 71
 
4.1%
Other values (24) 528
30.6%
Common
ValueCountFrequency (%)
11604
61.2%
1 1528
 
8.1%
) 981
 
5.2%
( 981
 
5.2%
2 930
 
4.9%
3 675
 
3.6%
4 476
 
2.5%
5 409
 
2.2%
0 279
 
1.5%
8 278
 
1.5%
Other values (11) 806
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78828
79.2%
ASCII 20673
 
20.8%
None 11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11604
56.1%
1 1528
 
7.4%
) 981
 
4.7%
( 981
 
4.7%
2 930
 
4.5%
3 675
 
3.3%
4 476
 
2.3%
5 409
 
2.0%
0 279
 
1.3%
8 278
 
1.3%
Other values (44) 2532
 
12.2%
Hangul
ValueCountFrequency (%)
구 3802
 
4.8%
역 3197
 
4.1%
앞 3032
 
3.8%
번 2899
 
3.7%
출 2777
 
3.5%
동 1897
 
2.4%
교 1555
 
2.0%
리 1467
 
1.9%
사 1199
 
1.5%
대 1103
 
1.4%
Other values (473) 55900
70.9%
None
ValueCountFrequency (%)
㈜ 10
90.9%
· 1
 
9.1%

반납거치대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2621
Minimum0
Maximum29
Zeros9621
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:15.380301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6613487
Coefficient of variation (CV)6.3386064
Kurtosis81.505009
Mean0.2621
Median Absolute Deviation (MAD)0
Skewness8.2812839
Sum2621
Variance2.7600796
MonotonicityNot monotonic
2023-12-11T16:31:15.529345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 9621
96.2%
1 45
 
0.4%
5 41
 
0.4%
2 39
 
0.4%
4 34
 
0.3%
7 33
 
0.3%
8 27
 
0.3%
10 26
 
0.3%
6 25
 
0.2%
3 24
 
0.2%
Other values (15) 85
 
0.9%
ValueCountFrequency (%)
0 9621
96.2%
1 45
 
0.4%
2 39
 
0.4%
3 24
 
0.2%
4 34
 
0.3%
5 41
 
0.4%
6 25
 
0.2%
7 33
 
0.3%
8 27
 
0.3%
9 18
 
0.2%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
25 1
 
< 0.1%
22 1
 
< 0.1%
20 8
0.1%
19 7
0.1%
18 3
 
< 0.1%
17 5
 
0.1%
16 1
 
< 0.1%
15 13
0.1%

이용시간
Real number (ℝ)

Distinct162
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.4416
Minimum1
Maximum676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:15.792313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median14
Q330
95-th percentile75
Maximum676
Range675
Interquartile range (IQR)22

Descriptive statistics

Standard deviation26.088136
Coefficient of variation (CV)1.1128991
Kurtosis79.299055
Mean23.4416
Median Absolute Deviation (MAD)8
Skewness5.0047591
Sum234416
Variance680.59085
MonotonicityNot monotonic
2023-12-11T16:31:16.015723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 535
 
5.3%
6 508
 
5.1%
8 500
 
5.0%
10 474
 
4.7%
7 465
 
4.7%
9 443
 
4.4%
4 393
 
3.9%
12 344
 
3.4%
11 335
 
3.4%
13 308
 
3.1%
Other values (152) 5695
57.0%
ValueCountFrequency (%)
1 5
 
0.1%
2 161
 
1.6%
3 303
3.0%
4 393
3.9%
5 535
5.3%
6 508
5.1%
7 465
4.7%
8 500
5.0%
9 443
4.4%
10 474
4.7%
ValueCountFrequency (%)
676 1
< 0.1%
652 1
< 0.1%
260 2
< 0.1%
255 1
< 0.1%
244 1
< 0.1%
227 1
< 0.1%
215 1
< 0.1%
203 1
< 0.1%
185 1
< 0.1%
182 1
< 0.1%

이용거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct335
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.53301
Minimum0
Maximum29500
Zeros9589
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:16.233123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum29500
Range29500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation932.13086
Coefficient of variation (CV)7.4850104
Kurtosis275.02332
Mean124.53301
Median Absolute Deviation (MAD)0
Skewness13.94895
Sum1245330.1
Variance868867.94
MonotonicityNot monotonic
2023-12-11T16:31:16.435843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9589
95.9%
1230.0 4
 
< 0.1%
1860.0 4
 
< 0.1%
950.0 4
 
< 0.1%
890.0 3
 
< 0.1%
1180.0 3
 
< 0.1%
960.0 3
 
< 0.1%
610.0 3
 
< 0.1%
1020.0 3
 
< 0.1%
810.0 3
 
< 0.1%
Other values (325) 381
 
3.8%
ValueCountFrequency (%)
0.0 9589
95.9%
111.2 1
 
< 0.1%
142.02 1
 
< 0.1%
170.0 2
 
< 0.1%
200.0 2
 
< 0.1%
250.0 1
 
< 0.1%
253.11 1
 
< 0.1%
270.0 1
 
< 0.1%
330.0 1
 
< 0.1%
340.0 1
 
< 0.1%
ValueCountFrequency (%)
29500.0 1
< 0.1%
22890.0 1
< 0.1%
22480.0 1
< 0.1%
22050.0 1
< 0.1%
19209.97 1
< 0.1%
15370.0 1
< 0.1%
14100.0 1
< 0.1%
13640.0 1
< 0.1%
13320.0 1
< 0.1%
12740.0 1
< 0.1%

Interactions

2023-12-11T16:31:09.598875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:05.678310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:06.403440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:07.238824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:07.991650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:08.793154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:09.728480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:05.815697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:06.542694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:07.348699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:08.089393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:08.895515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:09.859189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:05.950799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:06.673989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:07.468194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:08.220447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:09.043241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:10.010745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:06.061404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:06.783620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:07.575464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:08.375009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:09.179372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:10.153862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:06.185205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:06.916777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:07.715367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:08.528224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:09.316891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:10.307681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:06.291127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:07.082098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:07.861798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:08.657505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:09.447528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:31:16.583450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.0000.0000.8950.0000.0240.000
대여거치대0.0001.0000.0000.7400.0000.813
반납대여소번호0.8950.0001.0000.0000.0000.000
반납거치대0.0000.7400.0001.0000.0000.669
이용시간0.0240.0000.0000.0001.0000.127
이용거리0.0000.8130.0000.6690.1271.000
2023-12-11T16:31:16.713998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.000-0.0300.509-0.029-0.002-0.029
대여거치대-0.0301.000-0.0391.000-0.0210.768
반납대여소번호0.509-0.0391.000-0.038-0.014-0.046
반납거치대-0.0291.000-0.0381.000-0.0210.769
이용시간-0.002-0.021-0.014-0.0211.000-0.006
이용거리-0.0290.768-0.0460.769-0.0061.000

Missing values

2023-12-11T16:31:10.460081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:31:10.679777image/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
29220SPB-511062020-11-01 22:26:303417창신3동주민센터02020-11-01 22:36:33383신당역 12번 출구 뒤0100.0
72928SPB-355552020-11-02 15:48:02600휘경2동 주민센터02020-11-02 15:55:44634외국어대 정문 앞080.0
21755SPB-449792020-11-01 19:12:312016신대방삼거리역 3번출구쪽02020-11-01 19:32:53255도림4거리0200.0
29779SPB-355872020-11-01 22:36:151108공항시장역 2번출구 뒤02020-11-01 22:49:262715마곡나루역 2번 출구0130.0
18252SPB-439072020-11-01 17:44:382273일동제약 사거리02020-11-01 17:54:552340삼호물산버스정류장(23370) 옆0100.0
28198SPB-309082020-11-01 20:51:071429장안중학교02020-11-01 22:12:121433중화역 2번출구0810.0
44263SPB-446272020-11-02 08:28:021979구로1동우체국 앞02020-11-02 08:30:571994삼성전자 물류센터 앞030.0
25007SPB-512672020-11-01 20:00:351462동부시장 북문 앞02020-11-01 20:54:011462동부시장 북문 앞0530.0
17954SPB-314552020-11-01 17:21:331428원묵고등학교02020-11-01 17:45:181643태릉입구역 8번출구0240.0
58697SPB-306362020-11-02 12:03:43265영등포유통상가 사거리02020-11-02 12:25:572823중앙유통단지 앞0220.0
자전거번호대여일시대여 대여소번호대여 대여소명대여거치대반납일시반납대여소번호반납대여소명반납거치대이용시간이용거리
64169SPB-318932020-11-02 13:43:25284센트럴 푸르지오 시티 앞02020-11-02 13:49:48265영등포유통상가 사거리060.0
36366SPB-508822020-11-02 03:55:04549아차산역 3번출구02020-11-02 04:03:013587우성식품 앞080.0
44434SPB-313102020-11-02 08:01:102713우장초등학교 앞02020-11-02 08:32:26700KB국민은행 염창역 지점 앞0310.0
46112SPB-422882020-11-02 08:31:29646장한평역 1번출구 (국민은행앞)02020-11-02 08:45:29578성동세무서 부근0140.0
29061SPB-519302020-11-01 20:42:28947연신내 선일하이츠빌 정류소02020-11-01 22:32:23947연신내 선일하이츠빌 정류소01100.0
47024SPB-513322020-11-02 08:48:07537한양대후문역 부근02020-11-02 08:51:083545한양대역 1번출구030.0
19217SPB-506122020-11-01 18:03:36631답십리역 1번출구02020-11-01 18:23:263553마장역3번출구0200.0
22678SPB-138582020-11-01 18:45:47249여의도중학교 옆62020-11-01 19:56:15226샛강역 1번출구 앞10700.0
46691SPB-517982020-11-02 08:41:06703오목교역 7번출구 앞02020-11-02 08:49:05770목동역5번출구 교통정보센터 앞080.0
62649SPB-356592020-11-02 13:13:451102방화사거리 마을버스 버스정류장02020-11-02 13:25:071110공항중학교앞0110.0