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

Categorical4
Numeric4
Text3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/F/1/datasetView.do

Alerts

이용건수 is highly overall correlated with 이동거리(M) and 1 other fieldsHigh correlation
이동거리(M) is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
이용시간(분) is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
대여구분코드 is highly imbalanced (62.2%)Imbalance
이동거리(M) has 571 (5.7%) zerosZeros

Reproduction

Analysis started2024-03-13 16:25:58.616357
Analysis finished2024-03-13 16:26:01.348905
Duration2.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03-02
8717 
2021-03-01
1283 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-02
2nd row2021-03-02
3rd row2021-03-02
4th row2021-03-02
5th row2021-03-02

Common Values

ValueCountFrequency (%)
2021-03-02 8717
87.2%
2021-03-01 1283
 
12.8%

Length

2024-03-14T01:26:01.396681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:01.470825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-02 8717
87.2%
2021-03-01 1283
 
12.8%

대여소번호
Real number (ℝ)

Distinct1605
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean989.0361
Minimum5
Maximum3586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:26:01.557250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile170
Q1473
median933
Q31407
95-th percentile1961
Maximum3586
Range3581
Interquartile range (IQR)934

Descriptive statistics

Standard deviation623.29019
Coefficient of variation (CV)0.63019963
Kurtosis0.96462487
Mean989.0361
Median Absolute Deviation (MAD)469
Skewness0.79630697
Sum9890361
Variance388490.66
MonotonicityNot monotonic
2024-03-14T01:26:01.672336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 28
 
0.3%
583 26
 
0.3%
152 24
 
0.2%
502 24
 
0.2%
565 19
 
0.2%
1906 18
 
0.2%
361 18
 
0.2%
1149 18
 
0.2%
703 18
 
0.2%
274 18
 
0.2%
Other values (1595) 9789
97.9%
ValueCountFrequency (%)
5 1
 
< 0.1%
10 3
 
< 0.1%
101 7
0.1%
102 16
0.2%
103 14
0.1%
104 11
0.1%
105 8
0.1%
106 12
0.1%
107 14
0.1%
108 10
0.1%
ValueCountFrequency (%)
3586 1
 
< 0.1%
3582 1
 
< 0.1%
3581 1
 
< 0.1%
3579 1
 
< 0.1%
3578 1
 
< 0.1%
3575 1
 
< 0.1%
3571 4
< 0.1%
3569 2
< 0.1%
3566 1
 
< 0.1%
3560 1
 
< 0.1%

대여소
Text

Distinct1605
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:26:01.914529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length15.2246
Min length3

Characters and Unicode

Total characters152246
Distinct characters524
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

Unique231 ?
Unique (%)2.3%

Sample

1st row1669. 중계역 3번출구
2nd row1554.번동사거리
3rd row676.FITI시험연구원 앞
4th row1024. 강동구청 앞
5th row195. 모래내고가차도
ValueCountFrequency (%)
앞 2761
 
9.1%
옆 564
 
1.9%
출구 400
 
1.3%
1번출구 373
 
1.2%
사거리 275
 
0.9%
2번출구 264
 
0.9%
교차로 239
 
0.8%
3번출구 234
 
0.8%
4번출구 229
 
0.8%
뒤 218
 
0.7%
Other values (3244) 24637
81.6%
2024-03-14T01:26:02.300382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20390
 
13.4%
. 10019
 
6.6%
1 9451
 
6.2%
2 4861
 
3.2%
3 3860
 
2.5%
구 3608
 
2.4%
4 3498
 
2.3%
5 3464
 
2.3%
앞 3348
 
2.2%
6 3342
 
2.2%
Other values (514) 86405
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78779
51.7%
Decimal Number 39644
26.0%
Space Separator 20390
 
13.4%
Other Punctuation 10098
 
6.6%
Uppercase Letter 1381
 
0.9%
Open Punctuation 880
 
0.6%
Close Punctuation 880
 
0.6%
Lowercase Letter 114
 
0.1%
Dash Punctuation 58
 
< 0.1%
Connector Punctuation 9
 
< 0.1%
Other values (2) 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3608
 
4.6%
앞 3348
 
4.2%
역 2868
 
3.6%
번 2600
 
3.3%
출 2550
 
3.2%
동 2035
 
2.6%
교 1629
 
2.1%
리 1405
 
1.8%
아 1168
 
1.5%
원 1134
 
1.4%
Other values (458) 56434
71.6%
Uppercase Letter
ValueCountFrequency (%)
K 172
12.5%
S 167
12.1%
C 118
 
8.5%
G 103
 
7.5%
T 100
 
7.2%
L 94
 
6.8%
B 87
 
6.3%
A 76
 
5.5%
I 74
 
5.4%
D 65
 
4.7%
Other values (14) 325
23.5%
Lowercase Letter
ValueCountFrequency (%)
e 33
28.9%
n 18
15.8%
l 14
12.3%
t 11
 
9.6%
k 9
 
7.9%
y 9
 
7.9%
c 5
 
4.4%
m 5
 
4.4%
o 5
 
4.4%
s 3
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 9451
23.8%
2 4861
12.3%
3 3860
9.7%
4 3498
 
8.8%
5 3464
 
8.7%
6 3342
 
8.4%
7 3164
 
8.0%
0 2944
 
7.4%
9 2549
 
6.4%
8 2511
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 10019
99.2%
, 62
 
0.6%
& 10
 
0.1%
? 7
 
0.1%
Space Separator
ValueCountFrequency (%)
20390
100.0%
Open Punctuation
ValueCountFrequency (%)
( 880
100.0%
Close Punctuation
ValueCountFrequency (%)
) 880
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78786
51.7%
Common 71965
47.3%
Latin 1495
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3608
 
4.6%
앞 3348
 
4.2%
역 2868
 
3.6%
번 2600
 
3.3%
출 2550
 
3.2%
동 2035
 
2.6%
교 1629
 
2.1%
리 1405
 
1.8%
아 1168
 
1.5%
원 1134
 
1.4%
Other values (459) 56441
71.6%
Latin
ValueCountFrequency (%)
K 172
 
11.5%
S 167
 
11.2%
C 118
 
7.9%
G 103
 
6.9%
T 100
 
6.7%
L 94
 
6.3%
B 87
 
5.8%
A 76
 
5.1%
I 74
 
4.9%
D 65
 
4.3%
Other values (25) 439
29.4%
Common
ValueCountFrequency (%)
20390
28.3%
. 10019
13.9%
1 9451
13.1%
2 4861
 
6.8%
3 3860
 
5.4%
4 3498
 
4.9%
5 3464
 
4.8%
6 3342
 
4.6%
7 3164
 
4.4%
0 2944
 
4.1%
Other values (10) 6972
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78779
51.7%
ASCII 73460
48.3%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20390
27.8%
. 10019
13.6%
1 9451
12.9%
2 4861
 
6.6%
3 3860
 
5.3%
4 3498
 
4.8%
5 3464
 
4.7%
6 3342
 
4.5%
7 3164
 
4.3%
0 2944
 
4.0%
Other values (45) 8467
11.5%
Hangul
ValueCountFrequency (%)
구 3608
 
4.6%
앞 3348
 
4.2%
역 2868
 
3.6%
번 2600
 
3.3%
출 2550
 
3.2%
동 2035
 
2.6%
교 1629
 
2.1%
리 1405
 
1.8%
아 1168
 
1.5%
원 1134
 
1.4%
Other values (458) 56434
71.6%
None
ValueCountFrequency (%)
㈜ 7
100.0%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7555 
일일(회원)
2344 
일일(비회원)
 
46
단체
 
42
BIL_021
 
13

Length

Max length7
Median length2
Mean length2.9671
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기
2nd row정기
3rd row정기
4th row정기
5th row정기

Common Values

ValueCountFrequency (%)
정기 7555
75.5%
일일(회원) 2344
 
23.4%
일일(비회원) 46
 
0.5%
단체 42
 
0.4%
BIL_021 13
 
0.1%

Length

2024-03-14T01:26:02.423129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:02.506210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7555
75.5%
일일(회원 2344
 
23.4%
일일(비회원 46
 
0.5%
단체 42
 
0.4%
bil_021 13
 
0.1%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3659 
M
3413 
F
2220 
<NA>
702 
m
 
3

Length

Max length4
Median length1
Mean length1.5765
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th row\N

Common Values

ValueCountFrequency (%)
\N 3659
36.6%
M 3413
34.1%
F 2220
22.2%
<NA> 702
 
7.0%
m 3
 
< 0.1%
f 3
 
< 0.1%

Length

2024-03-14T01:26:02.596742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:02.687686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3659
36.6%
m 3416
34.2%
f 2223
22.2%
na 702
 
7.0%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
3424 
AGE_003
2153 
AGE_004
1658 
AGE_005
1228 
AGE_001
726 
Other values (3)
811 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_002
2nd rowAGE_004
3rd rowAGE_002
4th rowAGE_005
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 3424
34.2%
AGE_003 2153
21.5%
AGE_004 1658
16.6%
AGE_005 1228
 
12.3%
AGE_001 726
 
7.3%
AGE_006 485
 
4.9%
AGE_008 244
 
2.4%
AGE_007 82
 
0.8%

Length

2024-03-14T01:26:02.770137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:02.851865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 3424
34.2%
age_003 2153
21.5%
age_004 1658
16.6%
age_005 1228
 
12.3%
age_001 726
 
7.3%
age_006 485
 
4.9%
age_008 244
 
2.4%
age_007 82
 
0.8%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.011
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:26:02.968588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum28
Range27
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8729735
Coefficient of variation (CV)0.93136426
Kurtosis21.140305
Mean2.011
Median Absolute Deviation (MAD)0
Skewness3.6320214
Sum20110
Variance3.5080298
MonotonicityNot monotonic
2024-03-14T01:26:03.070360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 5813
58.1%
2 1956
 
19.6%
3 929
 
9.3%
4 520
 
5.2%
5 280
 
2.8%
6 173
 
1.7%
7 110
 
1.1%
8 68
 
0.7%
9 46
 
0.5%
10 29
 
0.3%
Other values (12) 76
 
0.8%
ValueCountFrequency (%)
1 5813
58.1%
2 1956
 
19.6%
3 929
 
9.3%
4 520
 
5.2%
5 280
 
2.8%
6 173
 
1.7%
7 110
 
1.1%
8 68
 
0.7%
9 46
 
0.5%
10 29
 
0.3%
ValueCountFrequency (%)
28 1
 
< 0.1%
23 2
 
< 0.1%
22 1
 
< 0.1%
19 2
 
< 0.1%
18 3
 
< 0.1%
17 3
 
< 0.1%
16 4
 
< 0.1%
15 5
0.1%
14 12
0.1%
13 12
0.1%

운동량
Text

Distinct7904
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:26:03.348944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.0686
Min length1

Characters and Unicode

Total characters50686
Distinct characters13
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

Unique6648 ?
Unique (%)66.5%

Sample

1st row130.52
2nd row167.78
3rd row65.37
4th row30.16
5th row124.13
ValueCountFrequency (%)
0 547
 
5.5%
n 24
 
0.2%
9.81 7
 
0.1%
30.63 6
 
0.1%
48.3 5
 
< 0.1%
9.07 5
 
< 0.1%
34.63 5
 
< 0.1%
72.28 5
 
< 0.1%
29.31 5
 
< 0.1%
27.56 5
 
< 0.1%
Other values (7894) 9386
93.9%
2024-03-14T01:26:03.766452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9335
18.4%
1 6114
12.1%
2 5179
10.2%
3 4514
8.9%
4 4130
8.1%
5 3924
7.7%
6 3853
7.6%
9 3565
 
7.0%
8 3556
 
7.0%
7 3536
 
7.0%
Other values (3) 2980
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41303
81.5%
Other Punctuation 9359
 
18.5%
Uppercase Letter 24
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6114
14.8%
2 5179
12.5%
3 4514
10.9%
4 4130
10.0%
5 3924
9.5%
6 3853
9.3%
9 3565
8.6%
8 3556
8.6%
7 3536
8.6%
0 2932
7.1%
Other Punctuation
ValueCountFrequency (%)
. 9335
99.7%
\ 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50662
> 99.9%
Latin 24
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9335
18.4%
1 6114
12.1%
2 5179
10.2%
3 4514
8.9%
4 4130
8.2%
5 3924
7.7%
6 3853
7.6%
9 3565
 
7.0%
8 3556
 
7.0%
7 3536
 
7.0%
Other values (2) 2956
 
5.8%
Latin
ValueCountFrequency (%)
N 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9335
18.4%
1 6114
12.1%
2 5179
10.2%
3 4514
8.9%
4 4130
8.1%
5 3924
7.7%
6 3853
7.6%
9 3565
 
7.0%
8 3556
 
7.0%
7 3536
 
7.0%
Other values (3) 2980
 
5.9%

탄소량
Text

Distinct752
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:26:04.089698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7328
Min length1

Characters and Unicode

Total characters37328
Distinct characters13
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

Unique226 ?
Unique (%)2.3%

Sample

1st row1.15
2nd row1.53
3rd row0.58
4th row0.3
5th row0.97
ValueCountFrequency (%)
0 551
 
5.5%
0.35 105
 
1.1%
0.14 95
 
0.9%
0.21 93
 
0.9%
0.24 92
 
0.9%
0.31 92
 
0.9%
0.19 91
 
0.9%
0.29 91
 
0.9%
0.16 90
 
0.9%
0.22 89
 
0.9%
Other values (742) 8611
86.1%
2024-03-14T01:26:04.577960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9367
25.1%
0 6845
18.3%
1 4218
11.3%
2 3158
 
8.5%
3 2603
 
7.0%
4 2287
 
6.1%
5 2102
 
5.6%
6 1845
 
4.9%
7 1702
 
4.6%
8 1607
 
4.3%
Other values (3) 1594
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27913
74.8%
Other Punctuation 9391
 
25.2%
Uppercase Letter 24
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6845
24.5%
1 4218
15.1%
2 3158
11.3%
3 2603
 
9.3%
4 2287
 
8.2%
5 2102
 
7.5%
6 1845
 
6.6%
7 1702
 
6.1%
8 1607
 
5.8%
9 1546
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 9367
99.7%
\ 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37304
99.9%
Latin 24
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9367
25.1%
0 6845
18.3%
1 4218
11.3%
2 3158
 
8.5%
3 2603
 
7.0%
4 2287
 
6.1%
5 2102
 
5.6%
6 1845
 
4.9%
7 1702
 
4.6%
8 1607
 
4.3%
Other values (2) 1570
 
4.2%
Latin
ValueCountFrequency (%)
N 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9367
25.1%
0 6845
18.3%
1 4218
11.3%
2 3158
 
8.5%
3 2603
 
7.0%
4 2287
 
6.1%
5 2102
 
5.6%
6 1845
 
4.9%
7 1702
 
4.6%
8 1607
 
4.3%
Other values (3) 1594
 
4.3%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9217
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5509.1779
Minimum0
Maximum167189.48
Zeros571
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:26:04.701313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11322.795
median2996.07
Q36868.8825
95-th percentile19049.275
Maximum167189.48
Range167189.48
Interquartile range (IQR)5546.0875

Descriptive statistics

Standard deviation7538.2152
Coefficient of variation (CV)1.3683013
Kurtosis56.137494
Mean5509.1779
Median Absolute Deviation (MAD)2104.805
Skewness4.9959243
Sum55091779
Variance56824689
MonotonicityNot monotonic
2024-03-14T01:26:04.810015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 571
 
5.7%
222.39 10
 
0.1%
111.2 5
 
0.1%
333.59 5
 
0.1%
1350.0 5
 
0.1%
480.0 5
 
0.1%
461.62 4
 
< 0.1%
1990.0 4
 
< 0.1%
2080.0 4
 
< 0.1%
444.78 4
 
< 0.1%
Other values (9207) 9383
93.8%
ValueCountFrequency (%)
0.0 571
5.7%
0.26 1
 
< 0.1%
10.0 2
 
< 0.1%
20.0 1
 
< 0.1%
30.0 1
 
< 0.1%
60.0 1
 
< 0.1%
88.04 2
 
< 0.1%
88.12 1
 
< 0.1%
88.14 1
 
< 0.1%
88.15 2
 
< 0.1%
ValueCountFrequency (%)
167189.48 1
< 0.1%
153707.07 1
< 0.1%
110902.45 1
< 0.1%
103581.03 1
< 0.1%
92613.76 1
< 0.1%
90475.26 1
< 0.1%
82026.46 1
< 0.1%
79014.61 1
< 0.1%
66717.18 1
< 0.1%
62534.59 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct353
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.3384
Minimum0
Maximum1428
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:26:04.932131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112
median28
Q362
95-th percentile163
Maximum1428
Range1428
Interquartile range (IQR)50

Descriptive statistics

Standard deviation63.373311
Coefficient of variation (CV)1.2844622
Kurtosis52.060758
Mean49.3384
Median Absolute Deviation (MAD)20
Skewness4.7368742
Sum493384
Variance4016.1765
MonotonicityNot monotonic
2024-03-14T01:26:05.035698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 322
 
3.2%
7 297
 
3.0%
9 268
 
2.7%
11 260
 
2.6%
5 259
 
2.6%
10 238
 
2.4%
8 233
 
2.3%
4 220
 
2.2%
3 217
 
2.2%
12 216
 
2.2%
Other values (343) 7470
74.7%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 34
 
0.3%
2 98
 
1.0%
3 217
2.2%
4 220
2.2%
5 259
2.6%
6 322
3.2%
7 297
3.0%
8 233
2.3%
9 268
2.7%
ValueCountFrequency (%)
1428 1
< 0.1%
1117 1
< 0.1%
1013 1
< 0.1%
971 1
< 0.1%
722 1
< 0.1%
714 1
< 0.1%
685 1
< 0.1%
655 1
< 0.1%
608 1
< 0.1%
571 1
< 0.1%

Interactions

2024-03-14T01:26:00.800233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:59.563887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.138836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.451954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.879060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:59.641940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.216044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.537112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.953685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:59.934270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.285510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.630636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:01.039391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.034013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.374306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:00.716377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:26:05.120157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.5740.0390.0880.1210.1780.0990.078
대여소번호0.5741.0000.0450.0340.0600.0980.0400.050
대여구분코드0.0390.0451.0000.1200.4100.1480.0000.028
성별0.0880.0340.1201.0000.1610.1000.0140.000
연령대코드0.1210.0600.4100.1611.0000.1430.0790.058
이용건수0.1780.0980.1480.1000.1431.0000.7480.836
이동거리(M)0.0990.0400.0000.0140.0790.7481.0000.814
이용시간(분)0.0780.0500.0280.0000.0580.8360.8141.000
2024-03-14T01:26:05.208521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자성별대여구분코드연령대코드
대여일자1.0000.1070.0480.091
성별0.1071.0000.0450.099
대여구분코드0.0480.0451.0000.266
연령대코드0.0910.0990.2661.000
2024-03-14T01:26:05.284926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여일자대여구분코드성별연령대코드
대여소번호1.000-0.125-0.080-0.1110.4430.0190.0140.028
이용건수-0.1251.0000.5890.6260.1360.0620.0420.069
이동거리(M)-0.0800.5891.0000.8360.0740.0000.0080.026
이용시간(분)-0.1110.6260.8361.0000.0780.0160.0000.028
대여일자0.4430.1360.0740.0781.0000.0480.1070.091
대여구분코드0.0190.0620.0000.0160.0481.0000.0450.266
성별0.0140.0420.0080.0000.1070.0451.0000.099
연령대코드0.0280.0690.0260.0280.0910.2660.0991.000

Missing values

2024-03-14T01:26:01.156374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:26:01.290722image/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

대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
173892021-03-0216691669. 중계역 3번출구정기MAGE_0022130.521.154956.1632
166932021-03-0215541554.번동사거리정기FAGE_0042167.781.536555.840
90812021-03-02676676.FITI시험연구원 앞정기MAGE_002265.370.582496.6615
120122021-03-0210241024. 강동구청 앞정기FAGE_005130.160.31312.9931
36722021-03-02195195. 모래내고가차도정기\NAGE_0021124.130.974179.6124
169342021-03-0216191619. 중계동 하나프라자빌딩 앞(중1-1)정기MAGE_0014177.111.596829.391
97282021-03-02754754. 목동1단지아파트 118동 앞 (월촌초등학교 정류소 옆)일일(회원)FAGE_002120.110.18781.2510
792021-03-01146146. 마포역 2번출구 뒤정기MAGE_006129.020.251062.210
12822021-03-0112651265. 문정동 근린공원정기\NAGE_002166.930.62600.1919
44482021-03-02243243. 이앤씨드림타워 앞정기FAGE_0043173.381.566735.7645
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
17882021-03-0119121912. 한신휴아파트 앞정기MAGE_003124.080.18779.596
175142021-03-0216821682. 중계종합사회복지관 교차로정기\NAGE_0023447.093.3314360.7162
134342021-03-0211671167. 마곡수명산파크3단지 교차로정기\NAGE_0044170.821.395988.3742
104422021-03-02811811. 녹사평역1번출구정기MAGE_0023147.111.144913.9525
126712021-03-0211101110. 공항중학교앞정기MAGE_0042159.691.325685.27105
184692021-03-0218331833. 독산역 1번출구 앞 자전거보관소정기<NA>AGE_002132.020.261107.56
183632021-03-0218211821. 홈플러스 시흥점 맞은편 다비치안경 앞정기FAGE_005124.250.23972.1918
165922021-03-0215391539. 4.19민주묘지역일일(회원)FAGE_0024534.814.8220777.56136
180412021-03-0217411741. 제일강산수산입구일일(회원)FAGE_002164.520.652809.220
159782021-03-0214491449. 상봉역 1번출구일일(회원)\NAGE_0023107.61.074599.5362