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 8809 (88.1%) zerosZeros
반납거치대 has 8809 (88.1%) zerosZeros
이용거리 has 8993 (89.9%) zerosZeros

Reproduction

Analysis started2023-12-11 07:31:34.906515
Analysis finished2023-12-11 07:31:41.519506
Duration6.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct6246
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:42.122538image/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

Unique3686 ?
Unique (%)36.9%

Sample

1st rowSPB-41025
2nd rowSPB-30194
3rd rowSPB-24651
4th rowSPB-38584
5th rowSPB-39927
ValueCountFrequency (%)
spb-30274 7
 
0.1%
spb-42282 7
 
0.1%
spb-33016 7
 
0.1%
spb-33291 7
 
0.1%
spb-30792 7
 
0.1%
spb-31918 6
 
0.1%
spb-31415 6
 
0.1%
spb-33771 6
 
0.1%
spb-31626 6
 
0.1%
spb-37931 6
 
0.1%
Other values (6236) 9935
99.4%
2023-12-11T16:31:42.637374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 11144
12.4%
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
4 5667
 
6.3%
1 5165
 
5.7%
2 4954
 
5.5%
0 4685
 
5.2%
6 3766
 
4.2%
Other values (4) 14619
16.2%

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 11144
22.3%
4 5667
11.3%
1 5165
10.3%
2 4954
9.9%
0 4685
9.4%
6 3766
 
7.5%
5 3747
 
7.5%
7 3736
 
7.5%
9 3640
 
7.3%
8 3496
 
7.0%
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 11144
18.6%
- 10000
16.7%
4 5667
9.4%
1 5165
8.6%
2 4954
8.3%
0 4685
7.8%
6 3766
 
6.3%
5 3747
 
6.2%
7 3736
 
6.2%
9 3640
 
6.1%
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 11144
12.4%
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
4 5667
 
6.3%
1 5165
 
5.7%
2 4954
 
5.5%
0 4685
 
5.2%
6 3766
 
4.2%
Other values (4) 14619
16.2%

대여일시
Date

Distinct9037
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-09-01 00:00:07
Maximum2020-09-01 21:34:16
2023-12-11T16:31:42.793457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:42.940600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여 대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1843
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1363.964
Minimum10
Maximum4652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:43.112768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile185
Q1565
median1195
Q32058
95-th percentile3115
Maximum4652
Range4642
Interquartile range (IQR)1493

Descriptive statistics

Standard deviation921.11982
Coefficient of variation (CV)0.67532561
Kurtosis-0.58277563
Mean1363.964
Median Absolute Deviation (MAD)721
Skewness0.55418763
Sum13639640
Variance848461.73
MonotonicityNot monotonic
2023-12-11T16:31:43.264663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 56
 
0.6%
207 49
 
0.5%
2102 40
 
0.4%
2701 38
 
0.4%
583 30
 
0.3%
152 29
 
0.3%
1160 28
 
0.3%
2177 26
 
0.3%
247 26
 
0.3%
1153 25
 
0.2%
Other values (1833) 9653
96.5%
ValueCountFrequency (%)
10 1
 
< 0.1%
101 2
 
< 0.1%
102 14
0.1%
103 12
0.1%
104 5
 
0.1%
105 4
 
< 0.1%
106 11
0.1%
107 6
0.1%
108 3
 
< 0.1%
109 13
0.1%
ValueCountFrequency (%)
4652 1
 
< 0.1%
3600 10
0.1%
3588 1
 
< 0.1%
3587 2
 
< 0.1%
3586 5
0.1%
3582 4
 
< 0.1%
3581 2
 
< 0.1%
3579 6
0.1%
3578 7
0.1%
3577 1
 
< 0.1%

대여 대여소명
Text

Distinct1843
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:43.532207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length24
Mean length10.0063
Min length2

Characters and Unicode

Total characters100063
Distinct characters544
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

Unique259 ?
Unique (%)2.6%

Sample

1st row쌍용플레티넘오피스텔
2nd row신트리공원 입구
3rd row서교동 사거리
4th rowLG서비스 역촌점
5th row경의선(노고산동)
ValueCountFrequency (%)
앞 2657
 
12.2%
옆 495
 
2.3%
1번출구 486
 
2.2%
출구 470
 
2.2%
3번출구 265
 
1.2%
2번출구 249
 
1.1%
뒤 241
 
1.1%
사거리 226
 
1.0%
5번출구 223
 
1.0%
입구 191
 
0.9%
Other values (2169) 16356
74.8%
2023-12-11T16:31:44.003628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11874
 
11.9%
구 3868
 
3.9%
앞 3165
 
3.2%
역 3145
 
3.1%
번 2862
 
2.9%
출 2771
 
2.8%
동 1956
 
2.0%
1 1587
 
1.6%
교 1578
 
1.6%
리 1295
 
1.3%
Other values (534) 65962
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79283
79.2%
Space Separator 11874
 
11.9%
Decimal Number 5384
 
5.4%
Uppercase Letter 1299
 
1.3%
Open Punctuation 917
 
0.9%
Close Punctuation 917
 
0.9%
Lowercase Letter 141
 
0.1%
Other Punctuation 110
 
0.1%
Dash Punctuation 107
 
0.1%
Math Symbol 21
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3868
 
4.9%
앞 3165
 
4.0%
역 3145
 
4.0%
번 2862
 
3.6%
출 2771
 
3.5%
동 1956
 
2.5%
교 1578
 
2.0%
리 1295
 
1.6%
원 1125
 
1.4%
사 1106
 
1.4%
Other values (478) 56412
71.2%
Uppercase Letter
ValueCountFrequency (%)
K 155
11.9%
S 154
11.9%
C 119
 
9.2%
G 99
 
7.6%
T 95
 
7.3%
L 92
 
7.1%
A 74
 
5.7%
I 70
 
5.4%
B 68
 
5.2%
M 59
 
4.5%
Other values (14) 314
24.2%
Lowercase Letter
ValueCountFrequency (%)
e 39
27.7%
n 28
19.9%
k 16
11.3%
l 15
 
10.6%
y 14
 
9.9%
v 9
 
6.4%
s 9
 
6.4%
t 8
 
5.7%
m 1
 
0.7%
o 1
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 1587
29.5%
2 935
17.4%
3 712
13.2%
4 497
 
9.2%
5 420
 
7.8%
8 302
 
5.6%
0 260
 
4.8%
6 239
 
4.4%
7 233
 
4.3%
9 199
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 83
75.5%
& 20
 
18.2%
? 7
 
6.4%
Math Symbol
ValueCountFrequency (%)
~ 18
85.7%
+ 3
 
14.3%
Space Separator
ValueCountFrequency (%)
11874
100.0%
Open Punctuation
ValueCountFrequency (%)
( 917
100.0%
Close Punctuation
ValueCountFrequency (%)
) 917
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 107
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79288
79.2%
Common 19335
 
19.3%
Latin 1440
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3868
 
4.9%
앞 3165
 
4.0%
역 3145
 
4.0%
번 2862
 
3.6%
출 2771
 
3.5%
동 1956
 
2.5%
교 1578
 
2.0%
리 1295
 
1.6%
원 1125
 
1.4%
사 1106
 
1.4%
Other values (479) 56417
71.2%
Latin
ValueCountFrequency (%)
K 155
 
10.8%
S 154
 
10.7%
C 119
 
8.3%
G 99
 
6.9%
T 95
 
6.6%
L 92
 
6.4%
A 74
 
5.1%
I 70
 
4.9%
B 68
 
4.7%
M 59
 
4.1%
Other values (25) 455
31.6%
Common
ValueCountFrequency (%)
11874
61.4%
1 1587
 
8.2%
2 935
 
4.8%
( 917
 
4.7%
) 917
 
4.7%
3 712
 
3.7%
4 497
 
2.6%
5 420
 
2.2%
8 302
 
1.6%
0 260
 
1.3%
Other values (10) 914
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79283
79.2%
ASCII 20775
 
20.8%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11874
57.2%
1 1587
 
7.6%
2 935
 
4.5%
( 917
 
4.4%
) 917
 
4.4%
3 712
 
3.4%
4 497
 
2.4%
5 420
 
2.0%
8 302
 
1.5%
0 260
 
1.3%
Other values (45) 2354
 
11.3%
Hangul
ValueCountFrequency (%)
구 3868
 
4.9%
앞 3165
 
4.0%
역 3145
 
4.0%
번 2862
 
3.6%
출 2771
 
3.5%
동 1956
 
2.5%
교 1578
 
2.0%
리 1295
 
1.6%
원 1125
 
1.4%
사 1106
 
1.4%
Other values (478) 56412
71.2%
None
ValueCountFrequency (%)
㈜ 5
100.0%

대여거치대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8276
Minimum0
Maximum31
Zeros8809
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:44.147727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8182393
Coefficient of variation (CV)3.4053157
Kurtosis21.911018
Mean0.8276
Median Absolute Deviation (MAD)0
Skewness4.3057892
Sum8276
Variance7.9424725
MonotonicityNot monotonic
2023-12-11T16:31:44.262932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 8809
88.1%
1 136
 
1.4%
3 114
 
1.1%
7 108
 
1.1%
2 100
 
1.0%
10 95
 
0.9%
6 93
 
0.9%
8 92
 
0.9%
5 91
 
0.9%
4 81
 
0.8%
Other values (21) 281
 
2.8%
ValueCountFrequency (%)
0 8809
88.1%
1 136
 
1.4%
2 100
 
1.0%
3 114
 
1.1%
4 81
 
0.8%
5 91
 
0.9%
6 93
 
0.9%
7 108
 
1.1%
8 92
 
0.9%
9 71
 
0.7%
ValueCountFrequency (%)
31 1
< 0.1%
30 1
< 0.1%
29 1
< 0.1%
28 2
< 0.1%
27 2
< 0.1%
25 1
< 0.1%
24 1
< 0.1%
23 2
< 0.1%
22 2
< 0.1%
21 2
< 0.1%

반납일시
Date

Distinct9081
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-09-01 00:02:23
Maximum2020-09-01 21:37:49
2023-12-11T16:31:44.402154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:44.598314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반납대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1827
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1342.8152
Minimum10
Maximum4652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:44.766594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile179
Q1567
median1190
Q32008.25
95-th percentile3010.05
Maximum4652
Range4642
Interquartile range (IQR)1441.25

Descriptive statistics

Standard deviation904.94987
Coefficient of variation (CV)0.67391989
Kurtosis-0.49699828
Mean1342.8152
Median Absolute Deviation (MAD)690
Skewness0.5753501
Sum13428152
Variance818934.26
MonotonicityNot monotonic
2023-12-11T16:31:44.901908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 72
 
0.7%
207 52
 
0.5%
583 42
 
0.4%
152 37
 
0.4%
2177 32
 
0.3%
210 30
 
0.3%
2102 30
 
0.3%
2183 29
 
0.3%
565 29
 
0.3%
1160 29
 
0.3%
Other values (1817) 9618
96.2%
ValueCountFrequency (%)
10 1
 
< 0.1%
101 6
 
0.1%
102 20
0.2%
103 16
0.2%
104 7
 
0.1%
105 5
 
0.1%
106 12
0.1%
107 9
0.1%
108 6
 
0.1%
109 9
0.1%
ValueCountFrequency (%)
4652 1
 
< 0.1%
3600 9
0.1%
3588 3
 
< 0.1%
3587 1
 
< 0.1%
3586 3
 
< 0.1%
3582 1
 
< 0.1%
3581 2
 
< 0.1%
3579 3
 
< 0.1%
3578 2
 
< 0.1%
3577 4
< 0.1%

반납대여소명
Text

Distinct1826
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:45.120188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length24
Mean length9.9815
Min length2

Characters and Unicode

Total characters99815
Distinct characters545
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

Unique265 ?
Unique (%)2.6%

Sample

1st row고척근린공원 고척도서관 앞
2nd row염창역 1번 출구
3rd row마스타 빌딩 앞
4th row불광역 8번출구
5th row공덕역 2번출구
ValueCountFrequency (%)
앞 2694
 
12.4%
1번출구 513
 
2.4%
출구 479
 
2.2%
옆 477
 
2.2%
3번출구 258
 
1.2%
뒤 244
 
1.1%
2번출구 227
 
1.0%
사거리 224
 
1.0%
5번출구 216
 
1.0%
입구 193
 
0.9%
Other values (2164) 16231
74.6%
2023-12-11T16:31:45.536872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11774
 
11.8%
구 3867
 
3.9%
앞 3191
 
3.2%
역 3130
 
3.1%
번 2838
 
2.8%
출 2753
 
2.8%
동 1874
 
1.9%
교 1605
 
1.6%
1 1546
 
1.5%
리 1319
 
1.3%
Other values (535) 65918
66.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79170
79.3%
Space Separator 11774
 
11.8%
Decimal Number 5177
 
5.2%
Uppercase Letter 1441
 
1.4%
Close Punctuation 943
 
0.9%
Open Punctuation 943
 
0.9%
Lowercase Letter 144
 
0.1%
Other Punctuation 102
 
0.1%
Dash Punctuation 92
 
0.1%
Math Symbol 18
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3867
 
4.9%
앞 3191
 
4.0%
역 3130
 
4.0%
번 2838
 
3.6%
출 2753
 
3.5%
동 1874
 
2.4%
교 1605
 
2.0%
리 1319
 
1.7%
사 1149
 
1.5%
원 1129
 
1.4%
Other values (479) 56315
71.1%
Uppercase Letter
ValueCountFrequency (%)
K 155
10.8%
S 148
10.3%
C 132
 
9.2%
G 120
 
8.3%
L 114
 
7.9%
T 108
 
7.5%
I 98
 
6.8%
B 85
 
5.9%
A 76
 
5.3%
M 65
 
4.5%
Other values (14) 340
23.6%
Lowercase Letter
ValueCountFrequency (%)
e 43
29.9%
n 26
18.1%
k 15
 
10.4%
l 15
 
10.4%
y 13
 
9.0%
s 11
 
7.6%
v 9
 
6.2%
t 6
 
4.2%
m 2
 
1.4%
o 2
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 1546
29.9%
2 924
17.8%
3 680
13.1%
4 467
 
9.0%
5 430
 
8.3%
8 304
 
5.9%
0 258
 
5.0%
7 213
 
4.1%
6 206
 
4.0%
9 149
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 80
78.4%
& 18
 
17.6%
? 4
 
3.9%
Math Symbol
ValueCountFrequency (%)
~ 16
88.9%
+ 2
 
11.1%
Space Separator
ValueCountFrequency (%)
11774
100.0%
Close Punctuation
ValueCountFrequency (%)
) 943
100.0%
Open Punctuation
ValueCountFrequency (%)
( 943
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79177
79.3%
Common 19053
 
19.1%
Latin 1585
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3867
 
4.9%
앞 3191
 
4.0%
역 3130
 
4.0%
번 2838
 
3.6%
출 2753
 
3.5%
동 1874
 
2.4%
교 1605
 
2.0%
리 1319
 
1.7%
사 1149
 
1.5%
원 1129
 
1.4%
Other values (480) 56322
71.1%
Latin
ValueCountFrequency (%)
K 155
 
9.8%
S 148
 
9.3%
C 132
 
8.3%
G 120
 
7.6%
L 114
 
7.2%
T 108
 
6.8%
I 98
 
6.2%
B 85
 
5.4%
A 76
 
4.8%
M 65
 
4.1%
Other values (25) 484
30.5%
Common
ValueCountFrequency (%)
11774
61.8%
1 1546
 
8.1%
) 943
 
4.9%
( 943
 
4.9%
2 924
 
4.8%
3 680
 
3.6%
4 467
 
2.5%
5 430
 
2.3%
8 304
 
1.6%
0 258
 
1.4%
Other values (10) 784
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79170
79.3%
ASCII 20638
 
20.7%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11774
57.1%
1 1546
 
7.5%
) 943
 
4.6%
( 943
 
4.6%
2 924
 
4.5%
3 680
 
3.3%
4 467
 
2.3%
5 430
 
2.1%
8 304
 
1.5%
0 258
 
1.3%
Other values (45) 2369
 
11.5%
Hangul
ValueCountFrequency (%)
구 3867
 
4.9%
앞 3191
 
4.0%
역 3130
 
4.0%
번 2838
 
3.6%
출 2753
 
3.5%
동 1874
 
2.4%
교 1605
 
2.0%
리 1319
 
1.7%
사 1149
 
1.5%
원 1129
 
1.4%
Other values (479) 56315
71.1%
None
ValueCountFrequency (%)
㈜ 7
100.0%

반납거치대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.833
Minimum0
Maximum40
Zeros8809
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:45.670745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9047125
Coefficient of variation (CV)3.4870498
Kurtosis26.518
Mean0.833
Median Absolute Deviation (MAD)0
Skewness4.6145692
Sum8330
Variance8.4373547
MonotonicityNot monotonic
2023-12-11T16:31:45.790472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 8809
88.1%
1 149
 
1.5%
3 111
 
1.1%
7 100
 
1.0%
5 99
 
1.0%
2 98
 
1.0%
4 97
 
1.0%
10 82
 
0.8%
6 80
 
0.8%
8 79
 
0.8%
Other values (21) 296
 
3.0%
ValueCountFrequency (%)
0 8809
88.1%
1 149
 
1.5%
2 98
 
1.0%
3 111
 
1.1%
4 97
 
1.0%
5 99
 
1.0%
6 80
 
0.8%
7 100
 
1.0%
8 79
 
0.8%
9 73
 
0.7%
ValueCountFrequency (%)
40 1
 
< 0.1%
33 1
 
< 0.1%
31 2
< 0.1%
30 1
 
< 0.1%
29 2
< 0.1%
28 3
< 0.1%
24 1
 
< 0.1%
23 2
< 0.1%
22 1
 
< 0.1%
21 3
< 0.1%

이용시간
Real number (ℝ)

Distinct183
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.0629
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:45.918223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median20
Q344
95-th percentile97
Maximum400
Range399
Interquartile range (IQR)35

Descriptive statistics

Standard deviation30.49555
Coefficient of variation (CV)0.98173543
Kurtosis7.8676444
Mean31.0629
Median Absolute Deviation (MAD)13
Skewness2.0934409
Sum310629
Variance929.97854
MonotonicityNot monotonic
2023-12-11T16:31:46.057797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 379
 
3.8%
10 374
 
3.7%
7 373
 
3.7%
5 366
 
3.7%
8 365
 
3.6%
4 331
 
3.3%
9 319
 
3.2%
11 274
 
2.7%
14 253
 
2.5%
12 243
 
2.4%
Other values (173) 6723
67.2%
ValueCountFrequency (%)
1 9
 
0.1%
2 155
1.6%
3 215
2.1%
4 331
3.3%
5 366
3.7%
6 379
3.8%
7 373
3.7%
8 365
3.6%
9 319
3.2%
10 374
3.7%
ValueCountFrequency (%)
400 1
< 0.1%
337 1
< 0.1%
305 1
< 0.1%
284 1
< 0.1%
258 1
< 0.1%
242 1
< 0.1%
237 1
< 0.1%
232 1
< 0.1%
217 2
< 0.1%
216 1
< 0.1%

이용거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct639
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean517.92618
Minimum0
Maximum79090
Zeros8993
Zeros (%)89.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:46.185456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2910.5
Maximum79090
Range79090
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2708.5617
Coefficient of variation (CV)5.2296289
Kurtosis247.7108
Mean517.92618
Median Absolute Deviation (MAD)0
Skewness12.341811
Sum5179261.8
Variance7336306.5
MonotonicityNot monotonic
2023-12-11T16:31:46.314899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8993
89.9%
1000.0 7
 
0.1%
1330.0 6
 
0.1%
1510.0 6
 
0.1%
760.0 6
 
0.1%
2070.0 5
 
0.1%
1360.0 5
 
0.1%
1380.0 5
 
0.1%
640.0 5
 
0.1%
940.0 5
 
0.1%
Other values (629) 957
 
9.6%
ValueCountFrequency (%)
0.0 8993
89.9%
20.0 1
 
< 0.1%
30.0 2
 
< 0.1%
80.0 1
 
< 0.1%
130.0 2
 
< 0.1%
170.0 1
 
< 0.1%
180.0 2
 
< 0.1%
220.0 1
 
< 0.1%
250.0 1
 
< 0.1%
260.0 2
 
< 0.1%
ValueCountFrequency (%)
79090.0 1
< 0.1%
76380.0 1
< 0.1%
74160.0 1
< 0.1%
51510.0 1
< 0.1%
49820.0 1
< 0.1%
49070.0 1
< 0.1%
34200.0 1
< 0.1%
33640.0 1
< 0.1%
32270.0 1
< 0.1%
31620.0 1
< 0.1%

Interactions

2023-12-11T16:31:40.554243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.027097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.738459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:38.504794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.161395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.823071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:40.663187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.143250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.833104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:38.606920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.263304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.953763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:40.771820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.239944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.971571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:38.701647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.364721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:40.096813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:40.870712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.323470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:38.096374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:38.781170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.470625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:40.207224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:40.979807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.464237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:38.221263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:38.914201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.582787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:40.313789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:41.107913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:37.606069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:38.358712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.032035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:39.705635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:40.431400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:31:46.404820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.0000.0570.9500.0360.0350.017
대여거치대0.0571.0000.0470.6510.0540.289
반납대여소번호0.9500.0471.0000.0400.0170.000
반납거치대0.0360.6510.0401.0000.0000.282
이용시간0.0350.0540.0170.0001.0000.271
이용거리0.0170.2890.0000.2820.2711.000
2023-12-11T16:31:46.500204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.000-0.0520.531-0.051-0.009-0.052
대여거치대-0.0521.000-0.0550.9950.0150.889
반납대여소번호0.531-0.0551.000-0.054-0.004-0.055
반납거치대-0.0510.995-0.0541.0000.0120.887
이용시간-0.0090.015-0.0040.0121.0000.039
이용거리-0.0520.889-0.0550.8870.0391.000

Missing values

2023-12-11T16:31:41.260398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:31:41.433209image/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
51481SPB-410252020-09-01 18:16:35278쌍용플레티넘오피스텔02020-09-01 18:40:441929고척근린공원 고척도서관 앞0240.0
15303SPB-301942020-09-01 08:07:44734신트리공원 입구02020-09-01 08:53:591169염창역 1번 출구0460.0
55637SPB-246512020-09-01 18:58:24108서교동 사거리102020-09-01 19:07:15115마스타 빌딩 앞881130.0
9831SPB-385842020-09-01 07:43:38933LG서비스 역촌점02020-09-01 07:58:11926불광역 8번출구0150.0
5987SPB-399272020-09-01 06:12:223011경의선(노고산동)02020-09-01 06:23:37143공덕역 2번출구0110.0
56057SPB-213032020-09-01 18:57:331112마곡엠밸리4단지 정문12020-09-01 19:10:011178개화산역 2번 출구10111850.0
63363SPB-201692020-09-01 19:40:29310청계광장 옆22020-09-01 20:04:26568청계8가사거리 부근4234250.0
43839SPB-317992020-09-01 17:01:142637잠실월드 메르디앙02020-09-01 17:46:041231잠실역 6번출구0450.0
52658SPB-344422020-09-01 18:17:06600휘경2동 주민센터02020-09-01 18:48:242904중계3차청구아파트0310.0
50693SPB-080452020-09-01 18:11:28272당산육갑문72020-09-01 18:35:40108서교동 사거리9233330.0
자전거번호대여일시대여 대여소번호대여 대여소명대여거치대반납일시반납대여소번호반납대여소명반납거치대이용시간이용거리
66758SPB-430652020-09-01 19:30:19249여의도중학교 옆02020-09-01 20:34:28207여의나루역 1번출구 앞0640.0
21530SPB-309742020-09-01 10:28:19212여의도역 1번출구 옆02020-09-01 11:23:34202국민일보 앞0550.0
16932SPB-355912020-09-01 09:12:451469먹골역 4번출구02020-09-01 09:23:092910도깨비시장0100.0
30973SPB-326962020-09-01 14:22:211634당고개공원 대여소02020-09-01 14:48:031605헬스케어0260.0
55413SPB-331492020-09-01 17:26:572077노들역 3번출구02020-09-01 19:05:492076노들나루공원 입구0990.0
51238SPB-382072020-09-01 18:16:371124발산역 6번 출구 뒤02020-09-01 18:39:051115등촌역 1번출구옆0220.0
69003SPB-337712020-09-01 19:41:462908광운대학교 중앙도서관02020-09-01 20:53:431616하계2동 공항버스정류장 옆0720.0
5286SPB-305322020-09-01 05:21:50680꿈마루어린이도서관 앞02020-09-01 05:29:22650중랑교사거리080.0
12050SPB-334342020-09-01 08:00:102630서울방이동 고분군02020-09-01 08:24:561243문정 법조단지70250.0
71002SPB-310352020-09-01 20:39:40281신동아아파트 앞02020-09-01 21:10:112183동방1교0310.0