Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory976.6 KiB
Average record size in memory100.0 B

Variable types

Text3
DateTime2
Numeric4
Unsupported2

Dataset

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

Alerts

대여거치대 is an unsupported type, check if it needs cleaning or further analysisUnsupported
반납대여소번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
반납거치대 has 7518 (75.2%) zerosZeros
이용거리 has 4897 (49.0%) zerosZeros

Reproduction

Analysis started2023-12-11 07:31:48.626054
Analysis finished2023-12-11 07:31:52.817657
Duration4.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct6360
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:53.071849image/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

Unique3851 ?
Unique (%)38.5%

Sample

1st rowSPB-37945
2nd rowSPB-36729
3rd rowSPB-30044
4th rowSPB-32819
5th rowSPB-34442
ValueCountFrequency (%)
spb-31328 7
 
0.1%
spb-37076 7
 
0.1%
spb-30915 7
 
0.1%
spb-36778 7
 
0.1%
spb-33456 6
 
0.1%
spb-37505 6
 
0.1%
spb-35973 6
 
0.1%
spb-30994 6
 
0.1%
spb-37385 6
 
0.1%
spb-36103 6
 
0.1%
Other values (6350) 9936
99.4%
2023-12-11T16:31:53.531823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 11148
12.4%
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
1 5457
 
6.1%
2 4956
 
5.5%
0 4886
 
5.4%
4 4182
 
4.6%
6 3933
 
4.4%
Other values (4) 15438
17.2%

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 11148
22.3%
1 5457
10.9%
2 4956
9.9%
0 4886
9.8%
4 4182
 
8.4%
6 3933
 
7.9%
9 3918
 
7.8%
8 3847
 
7.7%
7 3844
 
7.7%
5 3829
 
7.7%
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 11148
18.6%
- 10000
16.7%
1 5457
9.1%
2 4956
8.3%
0 4886
8.1%
4 4182
 
7.0%
6 3933
 
6.6%
9 3918
 
6.5%
8 3847
 
6.4%
7 3844
 
6.4%
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 11148
12.4%
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
1 5457
 
6.1%
2 4956
 
5.5%
0 4886
 
5.4%
4 4182
 
4.6%
6 3933
 
4.4%
Other values (4) 15438
17.2%

대여일시
Date

Distinct9031
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-07-01 00:00:23
Maximum2020-07-01 19:59:28
2023-12-11T16:31:53.700379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:53.849916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여 대여소번호
Real number (ℝ)

Distinct1800
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1313.9294
Minimum101
Maximum9997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:54.017010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile183
Q1547.5
median1184
Q31961
95-th percentile3010
Maximum9997
Range9896
Interquartile range (IQR)1413.5

Descriptive statistics

Standard deviation897.58708
Coefficient of variation (CV)0.6831319
Kurtosis0.43867559
Mean1313.9294
Median Absolute Deviation (MAD)681.5
Skewness0.70261574
Sum13139294
Variance805662.57
MonotonicityNot monotonic
2023-12-11T16:31:54.189144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 46
 
0.5%
1210 41
 
0.4%
502 36
 
0.4%
152 34
 
0.3%
2701 30
 
0.3%
2219 29
 
0.3%
113 29
 
0.3%
1906 28
 
0.3%
2102 26
 
0.3%
565 25
 
0.2%
Other values (1790) 9676
96.8%
ValueCountFrequency (%)
101 1
 
< 0.1%
102 18
0.2%
103 12
0.1%
104 6
 
0.1%
105 9
0.1%
106 19
0.2%
107 10
0.1%
108 7
 
0.1%
109 6
 
0.1%
110 3
 
< 0.1%
ValueCountFrequency (%)
9997 1
 
< 0.1%
3588 1
 
< 0.1%
3586 4
< 0.1%
3582 2
 
< 0.1%
3581 3
< 0.1%
3579 6
0.1%
3578 6
0.1%
3577 3
< 0.1%
3571 4
< 0.1%
3569 5
0.1%

대여 대여소명
Text

Distinct1801
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:54.517457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length23
Mean length9.9476
Min length2

Characters and Unicode

Total characters99476
Distinct characters537
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)2.7%

Sample

1st rowDDP 패션몰
2nd row광진청소년수련관
3rd row흑석역 1번출구
4th row구로구청
5th row대치역 사거리
ValueCountFrequency (%)
앞 2593
 
11.9%
출구 489
 
2.2%
옆 480
 
2.2%
1번출구 470
 
2.2%
뒤 274
 
1.3%
3번출구 267
 
1.2%
2번출구 266
 
1.2%
5번출구 252
 
1.2%
사거리 249
 
1.1%
4번출구 218
 
1.0%
Other values (2120) 16191
74.4%
2023-12-11T16:31:55.020053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11759
 
11.8%
구 4019
 
4.0%
역 3256
 
3.3%
앞 3086
 
3.1%
번 3015
 
3.0%
출 2929
 
2.9%
동 1775
 
1.8%
교 1592
 
1.6%
1 1566
 
1.6%
리 1300
 
1.3%
Other values (527) 65179
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78867
79.3%
Space Separator 11759
 
11.8%
Decimal Number 5364
 
5.4%
Uppercase Letter 1342
 
1.3%
Close Punctuation 891
 
0.9%
Open Punctuation 891
 
0.9%
Dash Punctuation 114
 
0.1%
Lowercase Letter 110
 
0.1%
Other Punctuation 105
 
0.1%
Math Symbol 21
 
< 0.1%
Other values (2) 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4019
 
5.1%
역 3256
 
4.1%
앞 3086
 
3.9%
번 3015
 
3.8%
출 2929
 
3.7%
동 1775
 
2.3%
교 1592
 
2.0%
리 1300
 
1.6%
원 1140
 
1.4%
사 1128
 
1.4%
Other values (471) 55627
70.5%
Uppercase Letter
ValueCountFrequency (%)
S 166
12.4%
K 160
11.9%
C 127
9.5%
L 103
 
7.7%
G 100
 
7.5%
A 83
 
6.2%
B 81
 
6.0%
I 77
 
5.7%
T 74
 
5.5%
M 70
 
5.2%
Other values (14) 301
22.4%
Lowercase Letter
ValueCountFrequency (%)
e 36
32.7%
k 13
 
11.8%
n 12
 
10.9%
t 12
 
10.9%
l 10
 
9.1%
y 6
 
5.5%
s 5
 
4.5%
v 4
 
3.6%
c 4
 
3.6%
m 4
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 1566
29.2%
2 995
18.5%
3 689
12.8%
4 538
 
10.0%
5 423
 
7.9%
8 270
 
5.0%
7 252
 
4.7%
0 249
 
4.6%
6 204
 
3.8%
9 178
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 82
78.1%
? 12
 
11.4%
& 11
 
10.5%
Math Symbol
ValueCountFrequency (%)
~ 15
71.4%
+ 6
 
28.6%
Space Separator
ValueCountFrequency (%)
11759
100.0%
Close Punctuation
ValueCountFrequency (%)
) 891
100.0%
Open Punctuation
ValueCountFrequency (%)
( 891
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78874
79.3%
Common 19149
 
19.2%
Latin 1452
 
1.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4019
 
5.1%
역 3256
 
4.1%
앞 3086
 
3.9%
번 3015
 
3.8%
출 2929
 
3.7%
동 1775
 
2.3%
교 1592
 
2.0%
리 1300
 
1.6%
원 1140
 
1.4%
사 1128
 
1.4%
Other values (471) 55634
70.5%
Latin
ValueCountFrequency (%)
S 166
11.4%
K 160
 
11.0%
C 127
 
8.7%
L 103
 
7.1%
G 100
 
6.9%
A 83
 
5.7%
B 81
 
5.6%
I 77
 
5.3%
T 74
 
5.1%
M 70
 
4.8%
Other values (25) 411
28.3%
Common
ValueCountFrequency (%)
11759
61.4%
1 1566
 
8.2%
2 995
 
5.2%
) 891
 
4.7%
( 891
 
4.7%
3 689
 
3.6%
4 538
 
2.8%
5 423
 
2.2%
8 270
 
1.4%
7 252
 
1.3%
Other values (10) 875
 
4.6%
Han
ValueCountFrequency (%)
怜 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78866
79.3%
ASCII 20601
 
20.7%
None 8
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11759
57.1%
1 1566
 
7.6%
2 995
 
4.8%
) 891
 
4.3%
( 891
 
4.3%
3 689
 
3.3%
4 538
 
2.6%
5 423
 
2.1%
8 270
 
1.3%
7 252
 
1.2%
Other values (45) 2327
 
11.3%
Hangul
ValueCountFrequency (%)
구 4019
 
5.1%
역 3256
 
4.1%
앞 3086
 
3.9%
번 3015
 
3.8%
출 2929
 
3.7%
동 1775
 
2.3%
교 1592
 
2.0%
리 1300
 
1.6%
원 1140
 
1.4%
사 1128
 
1.4%
Other values (470) 55626
70.5%
None
ValueCountFrequency (%)
㈜ 8
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
怜 1
100.0%

대여거치대
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

반납일시
Date

Distinct8972
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-07-01 00:05:50
Maximum2020-07-01 20:03:34
2023-12-11T16:31:55.154847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:55.292187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반납대여소번호
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

반납대여소명
Text

Distinct1781
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:31:55.488296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length10.0307
Min length2

Characters and Unicode

Total characters100307
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

Unique276 ?
Unique (%)2.8%

Sample

1st rowDDP 패션몰
2nd row광남중학교
3rd row흑석 아크로리버하임(103동)
4th row태평양물산빌딩
5th row하이브랜드 앞
ValueCountFrequency (%)
앞 2746
 
12.5%
출구 520
 
2.4%
옆 484
 
2.2%
1번출구 475
 
2.2%
3번출구 276
 
1.3%
2번출구 273
 
1.2%
사거리 270
 
1.2%
뒤 264
 
1.2%
5번출구 229
 
1.0%
교차로 215
 
1.0%
Other values (2111) 16168
73.8%
2023-12-11T16:31:55.822826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11934
 
11.9%
구 4040
 
4.0%
역 3336
 
3.3%
앞 3264
 
3.3%
번 3053
 
3.0%
출 2977
 
3.0%
동 1810
 
1.8%
1 1564
 
1.6%
교 1557
 
1.6%
리 1443
 
1.4%
Other values (526) 65329
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79232
79.0%
Space Separator 11934
 
11.9%
Decimal Number 5323
 
5.3%
Uppercase Letter 1430
 
1.4%
Close Punctuation 987
 
1.0%
Open Punctuation 987
 
1.0%
Lowercase Letter 163
 
0.2%
Other Punctuation 112
 
0.1%
Dash Punctuation 109
 
0.1%
Connector Punctuation 11
 
< 0.1%
Other values (2) 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4040
 
5.1%
역 3336
 
4.2%
앞 3264
 
4.1%
번 3053
 
3.9%
출 2977
 
3.8%
동 1810
 
2.3%
교 1557
 
2.0%
리 1443
 
1.8%
사 1189
 
1.5%
원 1084
 
1.4%
Other values (470) 55479
70.0%
Uppercase Letter
ValueCountFrequency (%)
S 178
12.4%
K 166
11.6%
C 140
 
9.8%
T 109
 
7.6%
G 97
 
6.8%
L 92
 
6.4%
M 87
 
6.1%
B 79
 
5.5%
A 64
 
4.5%
I 63
 
4.4%
Other values (14) 355
24.8%
Lowercase Letter
ValueCountFrequency (%)
e 45
27.6%
n 38
23.3%
l 23
14.1%
y 19
11.7%
k 9
 
5.5%
t 8
 
4.9%
s 5
 
3.1%
o 4
 
2.5%
m 4
 
2.5%
c 4
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 1564
29.4%
2 960
18.0%
3 686
12.9%
4 497
 
9.3%
5 388
 
7.3%
8 278
 
5.2%
7 266
 
5.0%
0 265
 
5.0%
6 237
 
4.5%
9 182
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 79
70.5%
& 26
 
23.2%
? 7
 
6.2%
Math Symbol
ValueCountFrequency (%)
~ 9
90.0%
+ 1
 
10.0%
Space Separator
ValueCountFrequency (%)
11934
100.0%
Close Punctuation
ValueCountFrequency (%)
) 987
100.0%
Open Punctuation
ValueCountFrequency (%)
( 987
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 11
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79241
79.0%
Common 19473
 
19.4%
Latin 1593
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4040
 
5.1%
역 3336
 
4.2%
앞 3264
 
4.1%
번 3053
 
3.9%
출 2977
 
3.8%
동 1810
 
2.3%
교 1557
 
2.0%
리 1443
 
1.8%
사 1189
 
1.5%
원 1084
 
1.4%
Other values (471) 55488
70.0%
Latin
ValueCountFrequency (%)
S 178
 
11.2%
K 166
 
10.4%
C 140
 
8.8%
T 109
 
6.8%
G 97
 
6.1%
L 92
 
5.8%
M 87
 
5.5%
B 79
 
5.0%
A 64
 
4.0%
I 63
 
4.0%
Other values (25) 518
32.5%
Common
ValueCountFrequency (%)
11934
61.3%
1 1564
 
8.0%
) 987
 
5.1%
( 987
 
5.1%
2 960
 
4.9%
3 686
 
3.5%
4 497
 
2.6%
5 388
 
2.0%
8 278
 
1.4%
7 266
 
1.4%
Other values (10) 926
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79232
79.0%
ASCII 21066
 
21.0%
None 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11934
56.7%
1 1564
 
7.4%
) 987
 
4.7%
( 987
 
4.7%
2 960
 
4.6%
3 686
 
3.3%
4 497
 
2.4%
5 388
 
1.8%
8 278
 
1.3%
7 266
 
1.3%
Other values (45) 2519
 
12.0%
Hangul
ValueCountFrequency (%)
구 4040
 
5.1%
역 3336
 
4.2%
앞 3264
 
4.1%
번 3053
 
3.9%
출 2977
 
3.8%
동 1810
 
2.3%
교 1557
 
2.0%
리 1443
 
1.8%
사 1189
 
1.5%
원 1084
 
1.4%
Other values (470) 55479
70.0%
None
ValueCountFrequency (%)
㈜ 9
100.0%

반납거치대
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8299
Minimum0
Maximum40
Zeros7518
Zeros (%)75.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:55.959183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.05
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.2129492
Coefficient of variation (CV)2.3022838
Kurtosis12.189193
Mean1.8299
Median Absolute Deviation (MAD)0
Skewness3.081917
Sum18299
Variance17.748941
MonotonicityNot monotonic
2023-12-11T16:31:56.117310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 7518
75.2%
1 280
 
2.8%
7 236
 
2.4%
3 210
 
2.1%
8 196
 
2.0%
2 193
 
1.9%
6 189
 
1.9%
4 179
 
1.8%
5 179
 
1.8%
10 176
 
1.8%
Other values (29) 644
 
6.4%
ValueCountFrequency (%)
0 7518
75.2%
1 280
 
2.8%
2 193
 
1.9%
3 210
 
2.1%
4 179
 
1.8%
5 179
 
1.8%
6 189
 
1.9%
7 236
 
2.4%
8 196
 
2.0%
9 144
 
1.4%
ValueCountFrequency (%)
40 2
 
< 0.1%
39 1
 
< 0.1%
38 3
< 0.1%
35 2
 
< 0.1%
34 1
 
< 0.1%
33 2
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
30 5
0.1%
29 1
 
< 0.1%

이용시간
Real number (ℝ)

Distinct161
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.1436
Minimum1
Maximum665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:56.493793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median16
Q335
95-th percentile84
Maximum665
Range664
Interquartile range (IQR)27

Descriptive statistics

Standard deviation27.208488
Coefficient of variation (CV)1.0407323
Kurtosis36.165211
Mean26.1436
Median Absolute Deviation (MAD)10
Skewness3.2913749
Sum261436
Variance740.30181
MonotonicityNot monotonic
2023-12-11T16:31:56.621755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 460
 
4.6%
8 439
 
4.4%
7 432
 
4.3%
5 428
 
4.3%
10 397
 
4.0%
4 365
 
3.6%
9 354
 
3.5%
14 305
 
3.0%
11 305
 
3.0%
13 297
 
3.0%
Other values (151) 6218
62.2%
ValueCountFrequency (%)
1 25
 
0.2%
2 154
 
1.5%
3 289
2.9%
4 365
3.6%
5 428
4.3%
6 460
4.6%
7 432
4.3%
8 439
4.4%
9 354
3.5%
10 397
4.0%
ValueCountFrequency (%)
665 1
< 0.1%
274 1
< 0.1%
269 1
< 0.1%
253 1
< 0.1%
251 1
< 0.1%
209 2
< 0.1%
206 1
< 0.1%
205 1
< 0.1%
188 2
< 0.1%
185 1
< 0.1%

이용거리
Real number (ℝ)

ZEROS 

Distinct3760
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1921.1827
Minimum0
Maximum81240
Zeros4897
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:31:56.786808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median230.11
Q32303.13
95-th percentile8720.542
Maximum81240
Range81240
Interquartile range (IQR)2303.13

Descriptive statistics

Standard deviation3852.0776
Coefficient of variation (CV)2.0050554
Kurtosis59.989523
Mean1921.1827
Median Absolute Deviation (MAD)230.11
Skewness5.5065264
Sum19211827
Variance14838502
MonotonicityNot monotonic
2023-12-11T16:31:56.936508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4897
49.0%
1140.0 13
 
0.1%
980.0 12
 
0.1%
1190.0 12
 
0.1%
1580.0 11
 
0.1%
1010.0 11
 
0.1%
1710.0 10
 
0.1%
1520.0 10
 
0.1%
1100.0 10
 
0.1%
890.0 10
 
0.1%
Other values (3750) 5004
50.0%
ValueCountFrequency (%)
0.0 4897
49.0%
0.1 3
 
< 0.1%
0.13 2
 
< 0.1%
0.2 6
 
0.1%
0.26 1
 
< 0.1%
0.29 1
 
< 0.1%
0.4 1
 
< 0.1%
0.58 3
 
< 0.1%
1.71 1
 
< 0.1%
10.0 1
 
< 0.1%
ValueCountFrequency (%)
81240.0 1
< 0.1%
70540.0 1
< 0.1%
66200.0 1
< 0.1%
51730.0 1
< 0.1%
51540.0 1
< 0.1%
49040.0 1
< 0.1%
48350.0 1
< 0.1%
47980.0 1
< 0.1%
46020.0 1
< 0.1%
43290.0 1
< 0.1%

Interactions

2023-12-11T16:31:51.899363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:50.365582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:50.898429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:51.391750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:52.051025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:50.504942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:51.022501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:51.491423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:52.208682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:50.633322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:51.162078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:51.663584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:52.349343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:50.764186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:51.274432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:31:51.766825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:31:57.029842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호반납거치대이용시간이용거리
대여 대여소번호1.0000.0820.0300.029
반납거치대0.0821.0000.0000.152
이용시간0.0300.0001.0000.349
이용거리0.0290.1520.3491.000
2023-12-11T16:31:57.113729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호반납거치대이용시간이용거리
대여 대여소번호1.000-0.078-0.014-0.032
반납거치대-0.0781.000-0.0070.382
이용시간-0.014-0.0071.0000.284
이용거리-0.0320.3820.2841.000

Missing values

2023-12-11T16:31:52.535368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:31:52.719073image/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
2046SPB-379452020-07-01 00:17:17475DDP 패션몰02020-07-01 01:29:3000475DDP 패션몰0720.0
1038SPB-367292020-07-01 00:44:28577광진청소년수련관02020-07-01 00:50:3900544광남중학교060.0
72863SPB-300442020-07-01 19:07:572025흑석역 1번출구02020-07-01 19:59:182078흑석 아크로리버하임(103동)0510.0
54668SPB-328192020-07-01 17:41:051984구로구청02020-07-01 18:11:0801986태평양물산빌딩0302470.75
20965SPB-344422020-07-01 09:27:002372대치역 사거리02020-07-01 09:59:1802252하이브랜드 앞0320.0
45793SPB-390262020-07-01 16:51:223212신풍역 6번출구02020-07-01 16:59:3200242신길선원가와인아파트 앞080.0
45583SPB-047602020-07-01 15:29:042032이수역 11번출구쪽102020-07-01 16:57:2602219고속터미널역 8-1번, 8-2번 출구 사이148710750.0
45902SPB-399452020-07-01 16:52:38512뚝섬역 1번 출구 옆02020-07-01 17:00:4603545한양대역 1번출구080.0
16874SPB-356272020-07-01 08:48:28207여의나루역 1번출구 앞02020-07-01 08:54:1300214금융감독원 앞060.0
22414SPB-301712020-07-01 10:22:541425용마문화복지센터02020-07-01 10:35:4701446중랑전화국 교차로0131759.13
자전거번호대여일시대여 대여소번호대여 대여소명대여거치대반납일시반납대여소번호반납대여소명반납거치대이용시간이용거리
66693SPB-334992020-07-01 19:13:25247당산역 10번출구 앞02020-07-01 19:18:093202당산롯데캐슬프레스티지05542.08
14699SPB-147272020-07-01 08:32:26303광화문역 1번출구 앞32020-07-01 08:38:3600302경복궁역 4번출구 뒤25580.0
2631SPB-359602020-07-01 01:31:40810이태원지하보도02020-07-01 01:56:3000800목월공원 앞0250.0
12708SPB-335872020-07-01 08:01:45247당산역 10번출구 앞02020-07-01 08:21:0300202국민일보 앞0193293.39
18607SPB-302342020-07-01 08:48:18131증산2교02020-07-01 09:18:0701171염창동 새마을금고 건너편 (모닝글로리)0306149.77
3646SPB-321092020-07-01 02:39:051401극동늘푸른아파트02020-07-01 02:57:4301446중랑전화국 교차로0192855.57
20112SPB-380132020-07-01 09:32:321320LG베스트샵 종암점02020-07-01 09:44:1901308안암로터리 버스정류장 앞0120.0
59267SPB-305812020-07-01 18:08:501991오류동역 맞은편02020-07-01 18:36:1501991오류동역 맞은편0273583.04
49716SPB-306122020-07-01 17:11:13641용두역 4번출구02020-07-01 17:35:2900678장안힐스테이트(아) 앞0244018.26
71805SPB-380432020-07-01 18:50:25338세운스퀘어 앞02020-07-01 19:51:45568청계8가사거리 부근0610.0