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
반납대여소번호 is highly overall correlated with 대여 대여소번호High correlation
이용시간 is highly overall correlated with 이용거리High correlation
이용거리 is highly overall correlated with 이용시간High correlation
대여 대여소번호 is highly skewed (γ1 = 43.66253594)Skewed
반납대여소번호 is highly skewed (γ1 = 43.41107431)Skewed
이용거리 has 589 (5.9%) zerosZeros

Reproduction

Analysis started2023-12-11 07:32:39.163851
Analysis finished2023-12-11 07:32:46.960218
Duration7.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct5936
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:32:47.217917image/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

Unique3309 ?
Unique (%)33.1%

Sample

1st rowSPB-09028
2nd rowSPB-12187
3rd rowSPB-18308
4th rowSPB-10301
5th rowSPB-10507
ValueCountFrequency (%)
spb-23528 8
 
0.1%
spb-09929 7
 
0.1%
spb-06456 7
 
0.1%
spb-23619 7
 
0.1%
spb-10796 7
 
0.1%
spb-20536 7
 
0.1%
spb-15407 6
 
0.1%
spb-24454 6
 
0.1%
spb-24554 6
 
0.1%
spb-21836 6
 
0.1%
Other values (5926) 9933
99.3%
2023-12-11T16:32:47.712773image/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%
1 8615
9.6%
2 7008
7.8%
0 6331
7.0%
3 4316
 
4.8%
4 4224
 
4.7%
5 4093
 
4.5%
Other values (4) 15413
17.1%

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 (%)
1 8615
17.2%
2 7008
14.0%
0 6331
12.7%
3 4316
8.6%
4 4224
8.4%
5 4093
8.2%
6 3925
7.8%
8 3915
7.8%
7 3840
7.7%
9 3733
7.5%
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%
1 8615
14.4%
2 7008
11.7%
0 6331
10.6%
3 4316
7.2%
4 4224
7.0%
5 4093
6.8%
6 3925
 
6.5%
8 3915
 
6.5%
7 3840
 
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 (%)
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
1 8615
9.6%
2 7008
7.8%
0 6331
7.0%
3 4316
 
4.8%
4 4224
 
4.7%
5 4093
 
4.5%
Other values (4) 15413
17.1%

대여일시
Date

Distinct9690
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-12-01 00:00:22
Maximum2019-12-03 21:28:00
2023-12-11T16:32:47.878846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:48.088790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여 대여소번호
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1444
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1226.0543
Minimum3
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:48.608464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile156
Q1455.75
median1145.5
Q31804.25
95-th percentile2620
Maximum99999
Range99996
Interquartile range (IQR)1348.5

Descriptive statistics

Standard deviation1642.2943
Coefficient of variation (CV)1.3394956
Kurtosis2616.1329
Mean1226.0543
Median Absolute Deviation (MAD)674
Skewness43.662536
Sum12260543
Variance2697130.5
MonotonicityNot monotonic
2023-12-11T16:32:48.784234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
567 43
 
0.4%
113 43
 
0.4%
2701 42
 
0.4%
1153 32
 
0.3%
1191 29
 
0.3%
1308 29
 
0.3%
592 28
 
0.3%
114 28
 
0.3%
2177 28
 
0.3%
2183 27
 
0.3%
Other values (1434) 9671
96.7%
ValueCountFrequency (%)
3 1
 
< 0.1%
101 5
 
0.1%
102 17
0.2%
103 9
0.1%
104 13
0.1%
105 4
 
< 0.1%
106 13
0.1%
107 19
0.2%
108 16
0.2%
109 8
0.1%
ValueCountFrequency (%)
99999 2
 
< 0.1%
9997 3
 
< 0.1%
3542 8
 
0.1%
3541 16
0.2%
3538 4
 
< 0.1%
3537 6
 
0.1%
3536 10
0.1%
3535 13
0.1%
3534 24
0.2%
3533 14
0.1%

대여 대여소명
Text

Distinct1444
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:32:49.093771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length25
Mean length9.9149
Min length3

Characters and Unicode

Total characters99149
Distinct characters510
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

Unique112 ?
Unique (%)1.1%

Sample

1st row서울시립대 후문
2nd row마곡나루역 5번출구 뒤편
3rd row광진구청 앞
4th row보라매공원 보도육교
5th row청계8가 사거리
ValueCountFrequency (%)
앞 2676
 
12.2%
옆 611
 
2.8%
1번출구 472
 
2.1%
출구 443
 
2.0%
2번출구 349
 
1.6%
사거리 308
 
1.4%
뒤 295
 
1.3%
5번출구 247
 
1.1%
3번출구 221
 
1.0%
교차로 215
 
1.0%
Other values (1759) 16142
73.4%
2023-12-11T16:32:49.639260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11989
 
12.1%
구 4076
 
4.1%
역 3311
 
3.3%
앞 3239
 
3.3%
번 3051
 
3.1%
출 2964
 
3.0%
동 1808
 
1.8%
교 1598
 
1.6%
1 1514
 
1.5%
리 1399
 
1.4%
Other values (500) 64200
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78609
79.3%
Space Separator 11989
 
12.1%
Decimal Number 5046
 
5.1%
Uppercase Letter 1348
 
1.4%
Close Punctuation 897
 
0.9%
Open Punctuation 897
 
0.9%
Lowercase Letter 132
 
0.1%
Other Punctuation 103
 
0.1%
Dash Punctuation 99
 
0.1%
Math Symbol 17
 
< 0.1%
Other values (2) 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4076
 
5.2%
역 3311
 
4.2%
앞 3239
 
4.1%
번 3051
 
3.9%
출 2964
 
3.8%
동 1808
 
2.3%
교 1598
 
2.0%
리 1399
 
1.8%
사 1187
 
1.5%
대 1169
 
1.5%
Other values (448) 54807
69.7%
Uppercase Letter
ValueCountFrequency (%)
K 167
12.4%
S 151
11.2%
C 126
 
9.3%
L 97
 
7.2%
B 96
 
7.1%
G 94
 
7.0%
I 77
 
5.7%
M 72
 
5.3%
T 66
 
4.9%
A 64
 
4.7%
Other values (14) 338
25.1%
Decimal Number
ValueCountFrequency (%)
1 1514
30.0%
2 996
19.7%
3 598
 
11.9%
4 498
 
9.9%
5 401
 
7.9%
8 259
 
5.1%
7 226
 
4.5%
0 205
 
4.1%
6 192
 
3.8%
9 157
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
e 42
31.8%
n 38
28.8%
l 19
14.4%
y 19
14.4%
k 7
 
5.3%
t 5
 
3.8%
s 2
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 68
66.0%
& 19
 
18.4%
? 16
 
15.5%
Math Symbol
ValueCountFrequency (%)
~ 14
82.4%
+ 3
 
17.6%
Space Separator
ValueCountFrequency (%)
11989
100.0%
Close Punctuation
ValueCountFrequency (%)
) 897
100.0%
Open Punctuation
ValueCountFrequency (%)
( 897
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78618
79.3%
Common 19051
 
19.2%
Latin 1480
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4076
 
5.2%
역 3311
 
4.2%
앞 3239
 
4.1%
번 3051
 
3.9%
출 2964
 
3.8%
동 1808
 
2.3%
교 1598
 
2.0%
리 1399
 
1.8%
사 1187
 
1.5%
대 1169
 
1.5%
Other values (449) 54816
69.7%
Latin
ValueCountFrequency (%)
K 167
 
11.3%
S 151
 
10.2%
C 126
 
8.5%
L 97
 
6.6%
B 96
 
6.5%
G 94
 
6.4%
I 77
 
5.2%
M 72
 
4.9%
T 66
 
4.5%
A 64
 
4.3%
Other values (21) 470
31.8%
Common
ValueCountFrequency (%)
11989
62.9%
1 1514
 
7.9%
2 996
 
5.2%
) 897
 
4.7%
( 897
 
4.7%
3 598
 
3.1%
4 498
 
2.6%
5 401
 
2.1%
8 259
 
1.4%
7 226
 
1.2%
Other values (10) 776
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78609
79.3%
ASCII 20531
 
20.7%
None 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11989
58.4%
1 1514
 
7.4%
2 996
 
4.9%
) 897
 
4.4%
( 897
 
4.4%
3 598
 
2.9%
4 498
 
2.4%
5 401
 
2.0%
8 259
 
1.3%
7 226
 
1.1%
Other values (41) 2256
 
11.0%
Hangul
ValueCountFrequency (%)
구 4076
 
5.2%
역 3311
 
4.2%
앞 3239
 
4.1%
번 3051
 
3.9%
출 2964
 
3.8%
동 1808
 
2.3%
교 1598
 
2.0%
리 1399
 
1.8%
사 1187
 
1.5%
대 1169
 
1.5%
Other values (448) 54807
69.7%
None
ValueCountFrequency (%)
㈜ 9
100.0%

대여거치대
Real number (ℝ)

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9226
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:49.833207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile17
Maximum39
Range38
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.0878481
Coefficient of variation (CV)0.73496202
Kurtosis2.59533
Mean6.9226
Median Absolute Deviation (MAD)3
Skewness1.307129
Sum69226
Variance25.886198
MonotonicityNot monotonic
2023-12-11T16:32:49.993135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 1242
12.4%
3 874
8.7%
5 848
8.5%
2 842
8.4%
4 811
8.1%
7 772
7.7%
8 763
7.6%
6 713
 
7.1%
10 689
 
6.9%
9 645
 
6.5%
Other values (26) 1801
18.0%
ValueCountFrequency (%)
1 1242
12.4%
2 842
8.4%
3 874
8.7%
4 811
8.1%
5 848
8.5%
6 713
7.1%
7 772
7.7%
8 763
7.6%
9 645
6.5%
10 689
6.9%
ValueCountFrequency (%)
39 3
 
< 0.1%
35 2
 
< 0.1%
34 1
 
< 0.1%
33 5
0.1%
32 4
 
< 0.1%
31 4
 
< 0.1%
30 12
0.1%
29 11
0.1%
28 6
0.1%
27 9
0.1%

반납일시
Date

Distinct9687
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-12-01 00:05:31
Maximum2019-12-03 21:33:44
2023-12-11T16:32:50.157527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:50.388327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반납대여소번호
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1434
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1214.31
Minimum3
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:50.590961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile157
Q1500
median1139.5
Q31741
95-th percentile2618
Maximum99999
Range99996
Interquartile range (IQR)1241

Descriptive statistics

Standard deviation1307.5803
Coefficient of variation (CV)1.0768093
Kurtosis3257.9131
Mean1214.31
Median Absolute Deviation (MAD)639.5
Skewness43.411074
Sum12143100
Variance1709766.2
MonotonicityNot monotonic
2023-12-11T16:32:50.807371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 58
 
0.6%
2701 42
 
0.4%
1210 37
 
0.4%
590 35
 
0.4%
1153 35
 
0.4%
1308 35
 
0.4%
1911 32
 
0.3%
502 30
 
0.3%
1906 29
 
0.3%
210 28
 
0.3%
Other values (1424) 9639
96.4%
ValueCountFrequency (%)
3 1
 
< 0.1%
101 8
 
0.1%
102 20
0.2%
103 8
 
0.1%
104 12
0.1%
105 2
 
< 0.1%
106 16
0.2%
107 19
0.2%
108 9
0.1%
109 10
0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
9997 3
 
< 0.1%
3542 8
0.1%
3541 19
0.2%
3539 1
 
< 0.1%
3538 4
 
< 0.1%
3537 3
 
< 0.1%
3536 7
 
0.1%
3535 14
0.1%
3534 10
0.1%

반납대여소명
Text

Distinct1434
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:32:51.174160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length25
Mean length9.925
Min length3

Characters and Unicode

Total characters99250
Distinct characters512
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

Unique147 ?
Unique (%)1.5%

Sample

1st row서울시립대 후문
2nd row마곡센트럴타워 1차
3rd row세종사이버대학교
4th row대림역4번출구
5th row청계7가 사거리
ValueCountFrequency (%)
앞 2725
 
12.4%
옆 581
 
2.6%
출구 473
 
2.2%
1번출구 466
 
2.1%
사거리 307
 
1.4%
2번출구 305
 
1.4%
뒤 285
 
1.3%
3번출구 255
 
1.2%
4번출구 249
 
1.1%
교차로 223
 
1.0%
Other values (1742) 16099
73.3%
2023-12-11T16:32:51.781850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11990
 
12.1%
구 4212
 
4.2%
역 3350
 
3.4%
앞 3255
 
3.3%
번 3112
 
3.1%
출 3011
 
3.0%
동 1673
 
1.7%
교 1552
 
1.6%
1 1464
 
1.5%
리 1453
 
1.5%
Other values (502) 64178
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78564
79.2%
Space Separator 11990
 
12.1%
Decimal Number 5044
 
5.1%
Uppercase Letter 1429
 
1.4%
Close Punctuation 928
 
0.9%
Open Punctuation 928
 
0.9%
Lowercase Letter 140
 
0.1%
Other Punctuation 110
 
0.1%
Dash Punctuation 80
 
0.1%
Math Symbol 19
 
< 0.1%
Other values (2) 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4212
 
5.4%
역 3350
 
4.3%
앞 3255
 
4.1%
번 3112
 
4.0%
출 3011
 
3.8%
동 1673
 
2.1%
교 1552
 
2.0%
리 1453
 
1.8%
사 1194
 
1.5%
대 1194
 
1.5%
Other values (448) 54558
69.4%
Uppercase Letter
ValueCountFrequency (%)
S 167
11.7%
K 161
11.3%
C 157
11.0%
G 96
 
6.7%
L 94
 
6.6%
B 85
 
5.9%
M 84
 
5.9%
I 84
 
5.9%
A 77
 
5.4%
T 73
 
5.1%
Other values (14) 351
24.6%
Decimal Number
ValueCountFrequency (%)
1 1464
29.0%
2 1000
19.8%
3 650
12.9%
4 531
 
10.5%
5 366
 
7.3%
8 252
 
5.0%
7 243
 
4.8%
6 205
 
4.1%
0 198
 
3.9%
9 135
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
n 38
27.1%
e 37
26.4%
l 20
14.3%
y 19
13.6%
t 12
 
8.6%
k 11
 
7.9%
o 1
 
0.7%
c 1
 
0.7%
m 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 69
62.7%
& 22
 
20.0%
? 19
 
17.3%
Math Symbol
ValueCountFrequency (%)
~ 15
78.9%
+ 4
 
21.1%
Space Separator
ValueCountFrequency (%)
11990
100.0%
Close Punctuation
ValueCountFrequency (%)
) 928
100.0%
Open Punctuation
ValueCountFrequency (%)
( 928
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78575
79.2%
Common 19106
 
19.3%
Latin 1569
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4212
 
5.4%
역 3350
 
4.3%
앞 3255
 
4.1%
번 3112
 
4.0%
출 3011
 
3.8%
동 1673
 
2.1%
교 1552
 
2.0%
리 1453
 
1.8%
사 1194
 
1.5%
대 1194
 
1.5%
Other values (449) 54569
69.4%
Latin
ValueCountFrequency (%)
S 167
 
10.6%
K 161
 
10.3%
C 157
 
10.0%
G 96
 
6.1%
L 94
 
6.0%
B 85
 
5.4%
M 84
 
5.4%
I 84
 
5.4%
A 77
 
4.9%
T 73
 
4.7%
Other values (23) 491
31.3%
Common
ValueCountFrequency (%)
11990
62.8%
1 1464
 
7.7%
2 1000
 
5.2%
) 928
 
4.9%
( 928
 
4.9%
3 650
 
3.4%
4 531
 
2.8%
5 366
 
1.9%
8 252
 
1.3%
7 243
 
1.3%
Other values (10) 754
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78564
79.2%
ASCII 20675
 
20.8%
None 11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11990
58.0%
1 1464
 
7.1%
2 1000
 
4.8%
) 928
 
4.5%
( 928
 
4.5%
3 650
 
3.1%
4 531
 
2.6%
5 366
 
1.8%
8 252
 
1.2%
7 243
 
1.2%
Other values (43) 2323
 
11.2%
Hangul
ValueCountFrequency (%)
구 4212
 
5.4%
역 3350
 
4.3%
앞 3255
 
4.1%
번 3112
 
4.0%
출 3011
 
3.8%
동 1673
 
2.1%
교 1552
 
2.0%
리 1453
 
1.8%
사 1194
 
1.5%
대 1194
 
1.5%
Other values (448) 54558
69.4%
None
ValueCountFrequency (%)
㈜ 11
100.0%

반납거치대
Real number (ℝ)

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8864
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:51.983933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile17
Maximum40
Range39
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.2197912
Coefficient of variation (CV)0.75798547
Kurtosis2.4307553
Mean6.8864
Median Absolute Deviation (MAD)3
Skewness1.2990186
Sum68864
Variance27.24622
MonotonicityNot monotonic
2023-12-11T16:32:52.197357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 1409
14.1%
3 898
9.0%
2 848
8.5%
5 794
7.9%
4 778
7.8%
8 720
 
7.2%
10 720
 
7.2%
7 692
 
6.9%
9 661
 
6.6%
6 656
 
6.6%
Other values (28) 1824
18.2%
ValueCountFrequency (%)
1 1409
14.1%
2 848
8.5%
3 898
9.0%
4 778
7.8%
5 794
7.9%
6 656
6.6%
7 692
6.9%
8 720
7.2%
9 661
6.6%
10 720
7.2%
ValueCountFrequency (%)
40 1
 
< 0.1%
38 2
 
< 0.1%
36 1
 
< 0.1%
35 2
 
< 0.1%
34 2
 
< 0.1%
33 3
 
< 0.1%
32 5
 
0.1%
31 2
 
< 0.1%
30 14
0.1%
29 8
0.1%

이용시간
Real number (ℝ)

HIGH CORRELATION 

Distinct149
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.4333
Minimum1
Maximum645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:52.434820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median11
Q322
95-th percentile61
Maximum645
Range644
Interquartile range (IQR)16

Descriptive statistics

Standard deviation22.139638
Coefficient of variation (CV)1.2010675
Kurtosis73.624948
Mean18.4333
Median Absolute Deviation (MAD)6
Skewness4.8250311
Sum184333
Variance490.16357
MonotonicityNot monotonic
2023-12-11T16:32:52.650265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 692
 
6.9%
4 636
 
6.4%
6 632
 
6.3%
7 555
 
5.5%
3 536
 
5.4%
8 515
 
5.1%
9 452
 
4.5%
10 432
 
4.3%
11 379
 
3.8%
12 372
 
3.7%
Other values (139) 4799
48.0%
ValueCountFrequency (%)
1 81
 
0.8%
2 310
3.1%
3 536
5.4%
4 636
6.4%
5 692
6.9%
6 632
6.3%
7 555
5.5%
8 515
5.1%
9 452
4.5%
10 432
4.3%
ValueCountFrequency (%)
645 1
< 0.1%
235 1
< 0.1%
223 1
< 0.1%
220 1
< 0.1%
211 1
< 0.1%
177 1
< 0.1%
171 1
< 0.1%
168 2
< 0.1%
165 1
< 0.1%
164 1
< 0.1%

이용거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1083
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2457.147
Minimum0
Maximum74150
Zeros589
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:52.832098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1900
median1560
Q32830
95-th percentile7480
Maximum74150
Range74150
Interquartile range (IQR)1930

Descriptive statistics

Standard deviation3385.0361
Coefficient of variation (CV)1.3776286
Kurtosis96.119363
Mean2457.147
Median Absolute Deviation (MAD)810
Skewness7.2821714
Sum24571470
Variance11458469
MonotonicityNot monotonic
2023-12-11T16:32:53.020374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 589
 
5.9%
1140 56
 
0.6%
790 54
 
0.5%
1020 53
 
0.5%
890 52
 
0.5%
1100 51
 
0.5%
870 51
 
0.5%
1190 51
 
0.5%
920 50
 
0.5%
1180 49
 
0.5%
Other values (1073) 8944
89.4%
ValueCountFrequency (%)
0 589
5.9%
10 8
 
0.1%
20 3
 
< 0.1%
30 3
 
< 0.1%
40 1
 
< 0.1%
60 1
 
< 0.1%
70 1
 
< 0.1%
80 1
 
< 0.1%
90 1
 
< 0.1%
100 2
 
< 0.1%
ValueCountFrequency (%)
74150 1
< 0.1%
70540 1
< 0.1%
62260 1
< 0.1%
57690 1
< 0.1%
56460 1
< 0.1%
54520 1
< 0.1%
51030 1
< 0.1%
50640 1
< 0.1%
48940 1
< 0.1%
46740 1
< 0.1%

Interactions

2023-12-11T16:32:45.796158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:41.314236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:42.248750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:43.164673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:44.027540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:44.903472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:45.929326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:41.432698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:42.398055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:43.305418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:44.164429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:45.056409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:46.073594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:41.614847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:42.546588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:43.447957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:44.309583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:45.227008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:46.215000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:41.771472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:42.686280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:43.584338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:44.436952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:45.357837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:46.346708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:41.910967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:42.841592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:43.738770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:44.582083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:45.501434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:46.476048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:42.054131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:42.995535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:43.868456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:44.736875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:45.630777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:32:53.176344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.0000.0000.0000.0000.0000.643
대여거치대0.0001.0000.0000.1270.0000.000
반납대여소번호0.0000.0001.0000.0000.0000.000
반납거치대0.0000.1270.0001.0000.0000.000
이용시간0.0000.0000.0000.0001.0000.471
이용거리0.6430.0000.0000.0000.4711.000
2023-12-11T16:32:53.309619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.000-0.0210.662-0.0150.0130.034
대여거치대-0.0211.000-0.0070.0430.0020.010
반납대여소번호0.662-0.0071.000-0.0240.0210.042
반납거치대-0.0150.043-0.0241.000-0.005-0.001
이용시간0.0130.0020.021-0.0051.0000.754
이용거리0.0340.0100.042-0.0010.7541.000

Missing values

2023-12-11T16:32:46.671025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:32:46.861260image/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
31578SPB-090282019-12-02 16:11:03639서울시립대 후문12019-12-02 18:20:42639서울시립대 후문14118920
20292SPB-121872019-12-02 11:12:112701마곡나루역 5번출구 뒤편142019-12-02 11:17:091193마곡센트럴타워 1차24720
39881SPB-183082019-12-02 21:55:513501광진구청 앞22019-12-02 22:12:343509세종사이버대학교5162860
6408SPB-103012019-12-01 11:38:152176보라매공원 보도육교132019-12-01 12:45:201914대림역4번출구3668620
42860SPB-105072019-12-03 00:19:46362청계8가 사거리32019-12-03 00:29:26378청계7가 사거리281060
36204SPB-199142019-12-02 19:58:09152마포구민체육센터 앞222019-12-02 20:03:24154마포구청역74490
30810SPB-082302019-12-02 17:44:27314국립현대미술관82019-12-02 18:06:25334종로3가역 2번출구 뒤3212300
48368SPB-208522019-12-03 07:36:111964원메디타운 앞12019-12-03 08:10:53290당산동 SK V1 빌딩2329630
53822SPB-193642019-12-03 10:22:29240문래역 4번출구 앞92019-12-03 10:31:16284센트럴 푸르지오 시티 앞98610
2060SPB-120222019-12-01 01:22:233106홍남교 두바퀴쉼터32019-12-01 01:37:03421마포구청 앞1142640
자전거번호대여일시대여 대여소번호대여 대여소명대여거치대반납일시반납대여소번호반납대여소명반납거치대이용시간이용거리
8518SPB-074882019-12-01 22:41:33179가좌역 4번출구 앞52019-12-01 22:50:42170가재울 뉴타운 주유소 옆98910
32820SPB-123292019-12-02 18:30:19307서울역사박물관 앞22019-12-02 18:41:18158독립문 어린이 공원13101480
24835SPB-254022019-12-02 13:06:19429송도병원72019-12-02 14:46:24383신당역 12번 출구 뒤19556460
47008SPB-126752019-12-03 07:24:261841가산동 주민센터102019-12-03 07:29:281848벽산 디지털밸리 5차54670
24114SPB-143212019-12-02 13:53:32971역촌 센트레빌 아파트12019-12-02 14:13:35912응암오거리2192570
36849SPB-030912019-12-02 20:19:011362보문역6번출구 앞52019-12-02 20:23:201355보문2교23560
20590SPB-092562019-12-02 11:13:37362청계8가 사거리32019-12-02 11:34:57361동묘앞역 1번출구 뒤9212090
71579SPB-065242019-12-03 20:24:19567성수역 2번출구 앞152019-12-03 20:29:20563성동세무서 건너편14890
71932SPB-005352019-12-03 20:21:421663동해문화예술관앞122019-12-03 20:39:211663동해문화예술관앞18172050
18168SPB-223242019-12-02 09:29:541364성북동 치안센터 앞62019-12-02 09:36:40342대학로 마로니에공원961210