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/A/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 (64.6%)Imbalance
대여소번호 is highly skewed (γ1 = 40.00977812)Skewed
이동거리(M) has 416 (4.2%) zerosZeros

Reproduction

Analysis started2024-05-18 05:03:25.312754
Analysis finished2024-05-18 05:03:32.995205
Duration7.68 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
2021-02-01
7605 
2021-02-02
2395 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-02-01
2nd row2021-02-01
3rd row2021-02-02
4th row2021-02-02
5th row2021-02-02

Common Values

ValueCountFrequency (%)
2021-02-01 7605
76.0%
2021-02-02 2395
 
23.9%

Length

2024-05-18T14:03:33.182996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:03:33.473853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-01 7605
76.0%
2021-02-02 2395
 
23.9%

대여소번호
Real number (ℝ)

SKEWED 

Distinct1927
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1221.192
Minimum3
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:03:33.772405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile179
Q1481
median931
Q31833
95-th percentile3102
Maximum99999
Range99996
Interquartile range (IQR)1352

Descriptive statistics

Standard deviation1343.7791
Coefficient of variation (CV)1.1003831
Kurtosis2919.4145
Mean1221.192
Median Absolute Deviation (MAD)579.5
Skewness40.009778
Sum12211920
Variance1805742.2
MonotonicityNot monotonic
2024-05-18T14:03:34.204283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 24
 
0.2%
602 22
 
0.2%
152 22
 
0.2%
207 21
 
0.2%
497 19
 
0.2%
548 18
 
0.2%
703 17
 
0.2%
583 17
 
0.2%
648 17
 
0.2%
415 17
 
0.2%
Other values (1917) 9806
98.1%
ValueCountFrequency (%)
3 2
 
< 0.1%
10 2
 
< 0.1%
101 5
 
0.1%
102 11
0.1%
103 13
0.1%
104 4
 
< 0.1%
105 7
0.1%
106 9
0.1%
107 7
0.1%
108 8
0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
9997 1
 
< 0.1%
3588 6
0.1%
3587 2
 
< 0.1%
3586 2
 
< 0.1%
3582 6
0.1%
3581 3
 
< 0.1%
3579 7
0.1%
3578 3
 
< 0.1%
3575 8
0.1%

대여소
Text

Distinct1927
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:03:34.722751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length15.1622
Min length3

Characters and Unicode

Total characters151622
Distinct characters549
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

Unique228 ?
Unique (%)2.3%

Sample

1st row1185. 등촌9단지
2nd row2335. 3호선 매봉역 3번출구앞
3rd row166. 가재울 초등학교
4th row306. 광화문역 7번출구 앞
5th row932. 예일여중
ValueCountFrequency (%)
앞 2842
 
9.6%
옆 516
 
1.7%
출구 415
 
1.4%
1번출구 382
 
1.3%
사거리 304
 
1.0%
2번출구 276
 
0.9%
3번출구 255
 
0.9%
4번출구 222
 
0.8%
뒤 213
 
0.7%
교차로 208
 
0.7%
Other values (3818) 23954
81.0%
2024-05-18T14:03:35.638470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19778
 
13.0%
. 10017
 
6.6%
1 7778
 
5.1%
2 6120
 
4.0%
3 4465
 
2.9%
구 3677
 
2.4%
5 3517
 
2.3%
4 3452
 
2.3%
앞 3353
 
2.2%
6 3254
 
2.1%
Other values (539) 86211
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78622
51.9%
Decimal Number 39780
26.2%
Space Separator 19778
 
13.0%
Other Punctuation 10092
 
6.7%
Uppercase Letter 1437
 
0.9%
Open Punctuation 876
 
0.6%
Close Punctuation 876
 
0.6%
Dash Punctuation 74
 
< 0.1%
Lowercase Letter 68
 
< 0.1%
Math Symbol 10
 
< 0.1%
Other values (2) 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3677
 
4.7%
앞 3353
 
4.3%
역 2892
 
3.7%
번 2608
 
3.3%
출 2571
 
3.3%
동 2007
 
2.6%
교 1597
 
2.0%
리 1391
 
1.8%
사 1167
 
1.5%
원 1159
 
1.5%
Other values (482) 56200
71.5%
Uppercase Letter
ValueCountFrequency (%)
C 159
11.1%
K 151
10.5%
S 148
10.3%
T 120
 
8.4%
D 99
 
6.9%
M 98
 
6.8%
G 88
 
6.1%
B 81
 
5.6%
L 78
 
5.4%
A 78
 
5.4%
Other values (14) 337
23.5%
Lowercase Letter
ValueCountFrequency (%)
e 27
39.7%
k 11
16.2%
s 7
 
10.3%
v 6
 
8.8%
t 5
 
7.4%
n 4
 
5.9%
l 3
 
4.4%
y 2
 
2.9%
o 1
 
1.5%
m 1
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 7778
19.6%
2 6120
15.4%
3 4465
11.2%
5 3517
8.8%
4 3452
8.7%
6 3254
8.2%
0 3248
8.2%
7 3101
 
7.8%
9 2424
 
6.1%
8 2421
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 10017
99.3%
, 63
 
0.6%
& 8
 
0.1%
? 4
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 6
60.0%
+ 4
40.0%
Space Separator
ValueCountFrequency (%)
19778
100.0%
Open Punctuation
ValueCountFrequency (%)
( 876
100.0%
Close Punctuation
ValueCountFrequency (%)
) 876
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 74
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78626
51.9%
Common 71491
47.2%
Latin 1505
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3677
 
4.7%
앞 3353
 
4.3%
역 2892
 
3.7%
번 2608
 
3.3%
출 2571
 
3.3%
동 2007
 
2.6%
교 1597
 
2.0%
리 1391
 
1.8%
사 1167
 
1.5%
원 1159
 
1.5%
Other values (483) 56204
71.5%
Latin
ValueCountFrequency (%)
C 159
 
10.6%
K 151
 
10.0%
S 148
 
9.8%
T 120
 
8.0%
D 99
 
6.6%
M 98
 
6.5%
G 88
 
5.8%
B 81
 
5.4%
L 78
 
5.2%
A 78
 
5.2%
Other values (25) 405
26.9%
Common
ValueCountFrequency (%)
19778
27.7%
. 10017
14.0%
1 7778
 
10.9%
2 6120
 
8.6%
3 4465
 
6.2%
5 3517
 
4.9%
4 3452
 
4.8%
6 3254
 
4.6%
0 3248
 
4.5%
7 3101
 
4.3%
Other values (11) 6761
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78622
51.9%
ASCII 72996
48.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19778
27.1%
. 10017
13.7%
1 7778
 
10.7%
2 6120
 
8.4%
3 4465
 
6.1%
5 3517
 
4.8%
4 3452
 
4.7%
6 3254
 
4.5%
0 3248
 
4.4%
7 3101
 
4.2%
Other values (46) 8266
11.3%
Hangul
ValueCountFrequency (%)
구 3677
 
4.7%
앞 3353
 
4.3%
역 2892
 
3.7%
번 2608
 
3.3%
출 2571
 
3.3%
동 2007
 
2.6%
교 1597
 
2.0%
리 1391
 
1.8%
사 1167
 
1.5%
원 1159
 
1.5%
Other values (482) 56200
71.5%
None
ValueCountFrequency (%)
㈜ 4
100.0%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7885 
일일(회원)
2008 
일일(비회원)
 
58
단체
 
29
BIL_021
 
20

Length

Max length7
Median length2
Mean length2.8422
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 7885
78.8%
일일(회원) 2008
 
20.1%
일일(비회원) 58
 
0.6%
단체 29
 
0.3%
BIL_021 20
 
0.2%

Length

2024-05-18T14:03:36.079395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:03:36.448704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7885
78.8%
일일(회원 2008
 
20.1%
일일(비회원 58
 
0.6%
단체 29
 
0.3%
bil_021 20
 
0.2%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3643 
M
3406 
F
2221 
<NA>
727 
m
 
3

Length

Max length4
Median length1
Mean length1.5824
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3643
36.4%
M 3406
34.1%
F 2221
22.2%
<NA> 727
 
7.3%
m 3
 
< 0.1%

Length

2024-05-18T14:03:37.083678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:03:37.433509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3643
36.4%
m 3409
34.1%
f 2221
22.2%
na 727
 
7.3%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
3097 
AGE_003
2399 
AGE_004
1750 
AGE_005
1333 
AGE_001
572 
Other values (3)
849 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
AGE_002 3097
31.0%
AGE_003 2399
24.0%
AGE_004 1750
17.5%
AGE_005 1333
13.3%
AGE_001 572
 
5.7%
AGE_006 535
 
5.3%
AGE_008 218
 
2.2%
AGE_007 96
 
1.0%

Length

2024-05-18T14:03:37.804857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:03:38.152246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 3097
31.0%
age_003 2399
24.0%
age_004 1750
17.5%
age_005 1333
13.3%
age_001 572
 
5.7%
age_006 535
 
5.3%
age_008 218
 
2.2%
age_007 96
 
1.0%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.848
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:03:38.674281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum19
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4770627
Coefficient of variation (CV)0.79927634
Kurtosis13.620472
Mean1.848
Median Absolute Deviation (MAD)0
Skewness3.0006793
Sum18480
Variance2.1817142
MonotonicityNot monotonic
2024-05-18T14:03:39.196985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 5887
58.9%
2 2149
 
21.5%
3 952
 
9.5%
4 462
 
4.6%
5 215
 
2.1%
6 129
 
1.3%
7 82
 
0.8%
8 54
 
0.5%
9 27
 
0.3%
10 19
 
0.2%
Other values (8) 24
 
0.2%
ValueCountFrequency (%)
1 5887
58.9%
2 2149
 
21.5%
3 952
 
9.5%
4 462
 
4.6%
5 215
 
2.1%
6 129
 
1.3%
7 82
 
0.8%
8 54
 
0.5%
9 27
 
0.3%
10 19
 
0.2%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 3
 
< 0.1%
12 6
 
0.1%
11 10
 
0.1%
10 19
0.2%
9 27
0.3%

운동량
Text

Distinct7936
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:03:40.261935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.1229
Min length1

Characters and Unicode

Total characters51229
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

Unique6563 ?
Unique (%)65.6%

Sample

1st row0
2nd row0
3rd row93.08
4th row195.79
5th row34.32
ValueCountFrequency (%)
0 380
 
3.8%
n 39
 
0.4%
25.23 5
 
< 0.1%
35.55 5
 
< 0.1%
2.86 5
 
< 0.1%
14.32 4
 
< 0.1%
39.27 4
 
< 0.1%
18.71 4
 
< 0.1%
40.18 4
 
< 0.1%
8.59 4
 
< 0.1%
Other values (7926) 9546
95.5%
2024-05-18T14:03:42.089628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9490
18.5%
1 6264
12.2%
2 5083
9.9%
3 4579
8.9%
4 4210
8.2%
5 4017
7.8%
6 3846
7.5%
7 3740
 
7.3%
8 3587
 
7.0%
9 3564
 
7.0%
Other values (3) 2849
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41661
81.3%
Other Punctuation 9529
 
18.6%
Uppercase Letter 39
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6264
15.0%
2 5083
12.2%
3 4579
11.0%
4 4210
10.1%
5 4017
9.6%
6 3846
9.2%
7 3740
9.0%
8 3587
8.6%
9 3564
8.6%
0 2771
6.7%
Other Punctuation
ValueCountFrequency (%)
. 9490
99.6%
\ 39
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51190
99.9%
Latin 39
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9490
18.5%
1 6264
12.2%
2 5083
9.9%
3 4579
8.9%
4 4210
8.2%
5 4017
7.8%
6 3846
7.5%
7 3740
 
7.3%
8 3587
 
7.0%
9 3564
 
7.0%
Other values (2) 2810
 
5.5%
Latin
ValueCountFrequency (%)
N 39
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9490
18.5%
1 6264
12.2%
2 5083
9.9%
3 4579
8.9%
4 4210
8.2%
5 4017
7.8%
6 3846
7.5%
7 3740
 
7.3%
8 3587
 
7.0%
9 3564
 
7.0%
Other values (3) 2849
 
5.6%

탄소량
Text

Distinct672
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:03:43.115353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7713
Min length1

Characters and Unicode

Total characters37713
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

Unique167 ?
Unique (%)1.7%

Sample

1st row0
2nd row0
3rd row0.66
4th row1.51
5th row0.34
ValueCountFrequency (%)
0 387
 
3.9%
0.24 106
 
1.1%
0.23 100
 
1.0%
0.2 99
 
1.0%
0.3 99
 
1.0%
0.26 97
 
1.0%
0.21 96
 
1.0%
0.27 95
 
0.9%
0.18 95
 
0.9%
0.25 92
 
0.9%
Other values (662) 8734
87.3%
2024-05-18T14:03:44.951052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9509
25.2%
0 7034
18.7%
1 4316
11.4%
2 3138
 
8.3%
3 2619
 
6.9%
4 2194
 
5.8%
5 2032
 
5.4%
6 1848
 
4.9%
8 1699
 
4.5%
7 1696
 
4.5%
Other values (3) 1628
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28126
74.6%
Other Punctuation 9548
 
25.3%
Uppercase Letter 39
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7034
25.0%
1 4316
15.3%
2 3138
11.2%
3 2619
 
9.3%
4 2194
 
7.8%
5 2032
 
7.2%
6 1848
 
6.6%
8 1699
 
6.0%
7 1696
 
6.0%
9 1550
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 9509
99.6%
\ 39
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37674
99.9%
Latin 39
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9509
25.2%
0 7034
18.7%
1 4316
11.5%
2 3138
 
8.3%
3 2619
 
7.0%
4 2194
 
5.8%
5 2032
 
5.4%
6 1848
 
4.9%
8 1699
 
4.5%
7 1696
 
4.5%
Other values (2) 1589
 
4.2%
Latin
ValueCountFrequency (%)
N 39
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37713
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9509
25.2%
0 7034
18.7%
1 4316
11.4%
2 3138
 
8.3%
3 2619
 
6.9%
4 2194
 
5.8%
5 2032
 
5.4%
6 1848
 
4.9%
8 1699
 
4.5%
7 1696
 
4.5%
Other values (3) 1628
 
4.3%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9316
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4970.265
Minimum0
Maximum159350.59
Zeros416
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:03:45.602327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile222.39
Q11328.09
median2965.3
Q36210.835
95-th percentile16806.268
Maximum159350.59
Range159350.59
Interquartile range (IQR)4882.745

Descriptive statistics

Standard deviation6193.1729
Coefficient of variation (CV)1.2460448
Kurtosis54.48508
Mean4970.265
Median Absolute Deviation (MAD)1966.245
Skewness4.5001361
Sum49702650
Variance38355390
MonotonicityNot monotonic
2024-05-18T14:03:46.205070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 416
 
4.2%
111.2 17
 
0.2%
333.59 9
 
0.1%
444.78 7
 
0.1%
222.39 7
 
0.1%
780.0 5
 
0.1%
1000.0 4
 
< 0.1%
1340.0 4
 
< 0.1%
1050.0 4
 
< 0.1%
555.97 4
 
< 0.1%
Other values (9306) 9523
95.2%
ValueCountFrequency (%)
0.0 416
4.2%
0.1 3
 
< 0.1%
0.2 1
 
< 0.1%
0.26 1
 
< 0.1%
0.48 2
 
< 0.1%
1.19 1
 
< 0.1%
10.0 1
 
< 0.1%
10.74 1
 
< 0.1%
88.04 1
 
< 0.1%
88.05 1
 
< 0.1%
ValueCountFrequency (%)
159350.59 1
< 0.1%
92170.24 1
< 0.1%
70647.15 1
< 0.1%
66128.57 1
< 0.1%
65348.76 1
< 0.1%
63394.19 1
< 0.1%
56332.51 1
< 0.1%
55370.16 1
< 0.1%
54249.69 1
< 0.1%
52999.46 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct307
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.2283
Minimum0
Maximum1168
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:03:46.772018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112
median26
Q356
95-th percentile136
Maximum1168
Range1168
Interquartile range (IQR)44

Descriptive statistics

Standard deviation50.418941
Coefficient of variation (CV)1.166341
Kurtosis36.936709
Mean43.2283
Median Absolute Deviation (MAD)18
Skewness3.7738863
Sum432283
Variance2542.0696
MonotonicityNot monotonic
2024-05-18T14:03:47.326653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 318
 
3.2%
7 292
 
2.9%
5 276
 
2.8%
8 258
 
2.6%
4 255
 
2.5%
10 232
 
2.3%
9 230
 
2.3%
11 230
 
2.3%
13 228
 
2.3%
12 228
 
2.3%
Other values (297) 7453
74.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 35
 
0.4%
2 137
1.4%
3 220
2.2%
4 255
2.5%
5 276
2.8%
6 318
3.2%
7 292
2.9%
8 258
2.6%
9 230
2.3%
ValueCountFrequency (%)
1168 1
< 0.1%
608 1
< 0.1%
586 1
< 0.1%
555 1
< 0.1%
457 1
< 0.1%
454 1
< 0.1%
436 1
< 0.1%
432 1
< 0.1%
423 1
< 0.1%
422 1
< 0.1%

Interactions

2024-05-18T14:03:30.973633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:27.647179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:28.653924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:29.714884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:31.520553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:27.852761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:28.905258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:30.086377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:31.810539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:28.078042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:29.165515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:30.326069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:32.098708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:28.337411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:29.450627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:30.602892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:03:47.683170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.0000.0490.0000.0470.0000.0400.035
대여소번호0.0001.0000.0000.0070.0000.0000.0000.000
대여구분코드0.0490.0001.0000.0770.4580.1760.0210.026
성별0.0000.0070.0771.0000.2520.1100.0300.026
연령대코드0.0470.0000.4580.2521.0000.1550.0360.049
이용건수0.0000.0000.1760.1100.1551.0000.5750.608
이동거리(M)0.0400.0000.0210.0300.0360.5751.0000.920
이용시간(분)0.0350.0000.0260.0260.0490.6080.9201.000
2024-05-18T14:03:48.089638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여일자대여구분코드
연령대코드1.0000.1150.0350.303
성별0.1151.0000.0000.063
대여일자0.0350.0001.0000.060
대여구분코드0.3030.0630.0601.000
2024-05-18T14:03:48.458369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여일자대여구분코드성별연령대코드
대여소번호1.000-0.062-0.013-0.0210.0000.0000.0040.000
이용건수-0.0621.0000.5480.5630.0000.0740.0660.074
이동거리(M)-0.0130.5481.0000.8390.0430.0130.0200.019
이용시간(분)-0.0210.5630.8391.0000.0370.0170.0180.026
대여일자0.0000.0000.0430.0371.0000.0600.0000.035
대여구분코드0.0000.0740.0130.0170.0601.0000.0630.303
성별0.0040.0660.0200.0180.0000.0631.0000.115
연령대코드0.0000.0740.0190.0260.0350.3030.1151.000

Missing values

2024-05-18T14:03:32.457540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:03:32.820707image/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)
97342021-02-0111851185. 등촌9단지정기FAGE_0021000.07
173902021-02-0123352335. 3호선 매봉역 3번출구앞정기\NAGE_0041000.05
217732021-02-02166166. 가재울 초등학교정기\NAGE_004293.080.662849.4625
231332021-02-02306306. 광화문역 7번출구 앞정기MAGE_0043195.791.516485.351
274982021-02-02932932. 예일여중정기\NAGE_002234.320.341444.49
202152021-02-0134073407.안국동사거리(신)정기MAGE_0033102.420.913935.6164
10922021-02-01209209. 유진투자증권빌딩 앞정기MAGE_0034276.962.089010.62174
235572021-02-02361361. 동묘앞역 1번출구 뒤일일(회원)FAGE_004124.830.241044.857
100032021-02-0112101210. 롯데월드타워(잠실역2번출구 쪽)일일(회원)<NA>AGE_003137.620.331418.0310
236532021-02-02374374. 청구역 2번출구 앞일일(회원)\NAGE_002131.410.281220.3811
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
88812021-02-0111151115. 등촌역 1번출구옆일일(회원)FAGE_002157.180.522221.3426
210912021-02-0135753575.자양사거리 (LG 유플러스)정기FAGE_005130.420.251097.487
46892021-02-01568568. 청계8가사거리 부근일일(회원)\NAGE_003187.270.73018.7939
149832021-02-0119621962. 가리봉동주민센터일일(회원)\NAGE_00217.310.07283.91
16842021-02-01252252. 보라매역4번출구정기FAGE_003133.690.441890.817
3632021-02-01131131. 증산2교정기MAGE_005298.380.913907.0328
90442021-02-0111251125. 명덕고교입구(영종빌딩)정기MAGE_004122.480.17727.664
168872021-02-0122642264. 이수역 1번출구정기MAGE_0054117.450.954102.828
211692021-02-0135883588.세종대학교(영실관)정기MAGE_0025186.231.56485.0973
91502021-02-0111351135. 강서구의회정기FAGE_0023249.352.4210450.050