Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory109.0 B

Variable types

DateTime1
Numeric5
Text3
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15245/F/1/datasetView.do

Alerts

이동거리(M) is highly overall correlated with 이용시간(분)High correlation
이용시간(분) is highly overall correlated with 이동거리(M)High correlation
대여구분코드 is highly imbalanced (64.7%)Imbalance
대여시간 has 171 (1.7%) zerosZeros
이동거리(M) has 2042 (20.4%) zerosZeros

Reproduction

Analysis started2023-12-11 08:12:15.503516
Analysis finished2023-12-11 08:12:21.105383
Duration5.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 00:00:00
Maximum2022-01-03 00:00:00
2023-12-11T17:12:21.173903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:21.316719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

대여시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.7223
Minimum0
Maximum23
Zeros171
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:12:21.447729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median15
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.977194
Coefficient of variation (CV)0.36270844
Kurtosis0.11511247
Mean13.7223
Median Absolute Deviation (MAD)3
Skewness-0.62514516
Sum137223
Variance24.77246
MonotonicityNot monotonic
2023-12-11T17:12:21.615952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18 1055
 
10.5%
17 944
 
9.4%
16 883
 
8.8%
15 811
 
8.1%
14 738
 
7.4%
13 696
 
7.0%
8 593
 
5.9%
12 590
 
5.9%
11 490
 
4.9%
9 410
 
4.1%
Other values (14) 2790
27.9%
ValueCountFrequency (%)
0 171
 
1.7%
1 122
 
1.2%
2 88
 
0.9%
3 61
 
0.6%
4 53
 
0.5%
5 94
 
0.9%
6 168
 
1.7%
7 312
3.1%
8 593
5.9%
9 410
4.1%
ValueCountFrequency (%)
23 146
 
1.5%
22 221
 
2.2%
21 310
 
3.1%
20 260
 
2.6%
19 387
 
3.9%
18 1055
10.5%
17 944
9.4%
16 883
8.8%
15 811
8.1%
14 738
7.4%

대여소번호
Real number (ℝ)

Distinct2252
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1900.6109
Minimum102
Maximum5075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:12:21.791434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile221.95
Q1756
median1557.5
Q32733.5
95-th percentile4560
Maximum5075
Range4973
Interquartile range (IQR)1977.5

Descriptive statistics

Standard deviation1379.1228
Coefficient of variation (CV)0.72562082
Kurtosis-0.71142577
Mean1900.6109
Median Absolute Deviation (MAD)950.5
Skewness0.65823532
Sum19006109
Variance1901979.8
MonotonicityNot monotonic
2023-12-11T17:12:21.999546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2177 26
 
0.3%
1637 25
 
0.2%
247 25
 
0.2%
1158 24
 
0.2%
1124 23
 
0.2%
4217 23
 
0.2%
2715 22
 
0.2%
502 22
 
0.2%
1210 22
 
0.2%
2744 21
 
0.2%
Other values (2242) 9767
97.7%
ValueCountFrequency (%)
102 7
0.1%
103 13
0.1%
104 6
0.1%
105 4
 
< 0.1%
106 5
 
0.1%
107 3
 
< 0.1%
108 4
 
< 0.1%
109 3
 
< 0.1%
111 2
 
< 0.1%
112 3
 
< 0.1%
ValueCountFrequency (%)
5075 10
0.1%
5074 7
0.1%
5073 5
0.1%
5070 5
0.1%
5067 4
 
< 0.1%
5066 4
 
< 0.1%
5065 5
0.1%
5064 5
0.1%
5063 5
0.1%
5062 5
0.1%

대여소명
Text

Distinct2252
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T17:12:22.284071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length15.5107
Min length7

Characters and Unicode

Total characters155107
Distinct characters565
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique433 ?
Unique (%)4.3%

Sample

1st row543. 구의공원(테크노마트 앞)
2nd row1851. 가산디지털단지 7번출구
3rd row4360. 르노삼성자동차 성수사업소 앞
4th row157. 애오개역 4번출구 앞
5th row1212. 송파역 2번 출구앞
ValueCountFrequency (%)
앞 2533
 
8.7%
출구 471
 
1.6%
옆 392
 
1.3%
1번출구 366
 
1.3%
3번출구 242
 
0.8%
교차로 230
 
0.8%
뒤 223
 
0.8%
사거리 218
 
0.7%
2번출구 217
 
0.7%
5번출구 198
 
0.7%
Other values (4533) 24182
82.6%
2023-12-11T17:12:22.783338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19487
 
12.6%
. 10017
 
6.5%
1 8408
 
5.4%
2 6019
 
3.9%
3 4590
 
3.0%
4 4345
 
2.8%
5 3718
 
2.4%
구 3610
 
2.3%
0 3571
 
2.3%
7 3444
 
2.2%
Other values (555) 87898
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79847
51.5%
Decimal Number 42520
27.4%
Space Separator 19487
 
12.6%
Other Punctuation 10120
 
6.5%
Uppercase Letter 1208
 
0.8%
Close Punctuation 828
 
0.5%
Open Punctuation 828
 
0.5%
Lowercase Letter 154
 
0.1%
Dash Punctuation 92
 
0.1%
Connector Punctuation 9
 
< 0.1%
Other values (3) 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3610
 
4.5%
역 2937
 
3.7%
앞 2933
 
3.7%
번 2608
 
3.3%
출 2553
 
3.2%
동 2069
 
2.6%
교 1596
 
2.0%
리 1388
 
1.7%
파 1386
 
1.7%
아 1343
 
1.7%
Other values (494) 57424
71.9%
Uppercase Letter
ValueCountFrequency (%)
S 139
11.5%
T 131
10.8%
C 128
10.6%
K 115
9.5%
A 88
 
7.3%
G 76
 
6.3%
D 76
 
6.3%
I 67
 
5.5%
M 66
 
5.5%
B 65
 
5.4%
Other values (12) 257
21.3%
Lowercase Letter
ValueCountFrequency (%)
e 46
29.9%
k 15
 
9.7%
t 14
 
9.1%
s 13
 
8.4%
n 12
 
7.8%
l 11
 
7.1%
y 6
 
3.9%
c 5
 
3.2%
m 5
 
3.2%
o 5
 
3.2%
Other values (6) 22
14.3%
Decimal Number
ValueCountFrequency (%)
1 8408
19.8%
2 6019
14.2%
3 4590
10.8%
4 4345
10.2%
5 3718
8.7%
0 3571
8.4%
7 3444
8.1%
6 3403
8.0%
8 2670
 
6.3%
9 2352
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 10017
99.0%
, 76
 
0.8%
& 15
 
0.1%
? 9
 
0.1%
· 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
19487
100.0%
Close Punctuation
ValueCountFrequency (%)
) 828
100.0%
Open Punctuation
ValueCountFrequency (%)
( 828
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%
Other Number
ValueCountFrequency (%)
① 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79848
51.5%
Common 73897
47.6%
Latin 1362
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3610
 
4.5%
역 2937
 
3.7%
앞 2933
 
3.7%
번 2608
 
3.3%
출 2553
 
3.2%
동 2069
 
2.6%
교 1596
 
2.0%
리 1388
 
1.7%
파 1386
 
1.7%
아 1343
 
1.7%
Other values (495) 57425
71.9%
Latin
ValueCountFrequency (%)
S 139
 
10.2%
T 131
 
9.6%
C 128
 
9.4%
K 115
 
8.4%
A 88
 
6.5%
G 76
 
5.6%
D 76
 
5.6%
I 67
 
4.9%
M 66
 
4.8%
B 65
 
4.8%
Other values (28) 411
30.2%
Common
ValueCountFrequency (%)
19487
26.4%
. 10017
13.6%
1 8408
11.4%
2 6019
 
8.1%
3 4590
 
6.2%
4 4345
 
5.9%
5 3718
 
5.0%
0 3571
 
4.8%
7 3444
 
4.7%
6 3403
 
4.6%
Other values (12) 6895
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79847
51.5%
ASCII 75248
48.5%
Enclosed Alphanum 8
 
< 0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19487
25.9%
. 10017
13.3%
1 8408
11.2%
2 6019
 
8.0%
3 4590
 
6.1%
4 4345
 
5.8%
5 3718
 
4.9%
0 3571
 
4.7%
7 3444
 
4.6%
6 3403
 
4.5%
Other values (48) 8246
11.0%
Hangul
ValueCountFrequency (%)
구 3610
 
4.5%
역 2937
 
3.7%
앞 2933
 
3.7%
번 2608
 
3.3%
출 2553
 
3.2%
동 2069
 
2.6%
교 1596
 
2.0%
리 1388
 
1.7%
파 1386
 
1.7%
아 1343
 
1.7%
Other values (494) 57424
71.9%
Enclosed Alphanum
ValueCountFrequency (%)
① 8
100.0%
None
ValueCountFrequency (%)
· 3
75.0%
㈜ 1
 
25.0%

대여구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기권
8367 
일일권
1533 
일일권(비회원)
 
62
단체권
 
38

Length

Max length8
Median length3
Mean length3.031
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기권
2nd row정기권
3rd row정기권
4th row일일권
5th row정기권

Common Values

ValueCountFrequency (%)
정기권 8367
83.7%
일일권 1533
 
15.3%
일일권(비회원) 62
 
0.6%
단체권 38
 
0.4%

Length

2023-12-11T17:12:22.967106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:12:23.127061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기권 8367
83.7%
일일권 1533
 
15.3%
일일권(비회원 62
 
0.6%
단체권 38
 
0.4%

성별
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
4063 
<NA>
3733 
F
2200 
m
 
4

Length

Max length4
Median length1
Mean length2.1199
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 4063
40.6%
<NA> 3733
37.3%
F 2200
22.0%
m 4
 
< 0.1%

Length

2023-12-11T17:12:23.302512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:12:23.458623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 4067
40.7%
na 3733
37.3%
f 2200
22.0%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
3381 
30대
2258 
40대
1443 
50대
1050 
기타
1010 
Other values (3)
858 

Length

Max length5
Median length3
Mean length2.9581
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30대
2nd row20대
3rd row50대
4th row기타
5th row20대

Common Values

ValueCountFrequency (%)
20대 3381
33.8%
30대 2258
22.6%
40대 1443
14.4%
50대 1050
 
10.5%
기타 1010
 
10.1%
~10대 457
 
4.6%
60대 334
 
3.3%
70대이상 67
 
0.7%

Length

2023-12-11T17:12:23.625399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:12:23.788056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3381
33.8%
30대 2258
22.6%
40대 1443
14.4%
50대 1050
 
10.5%
기타 1010
 
10.1%
10대 457
 
4.6%
60대 334
 
3.3%
70대이상 67
 
0.7%

이용건수
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.057
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:12:23.942265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.27084744
Coefficient of variation (CV)0.25624167
Kurtosis63.356788
Mean1.057
Median Absolute Deviation (MAD)0
Skewness6.4276323
Sum10570
Variance0.073358336
MonotonicityNot monotonic
2023-12-11T17:12:24.096425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 9501
95.0%
2 445
 
4.5%
3 44
 
0.4%
4 5
 
0.1%
5 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
1 9501
95.0%
2 445
 
4.5%
3 44
 
0.4%
4 5
 
0.1%
5 4
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 4
 
< 0.1%
4 5
 
0.1%
3 44
 
0.4%
2 445
 
4.5%
1 9501
95.0%

운동량
Text

Distinct5057
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T17:12:24.572344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.8885
Min length2

Characters and Unicode

Total characters48885
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

Unique3549 ?
Unique (%)35.5%

Sample

1st row0.00
2nd row22.95
3rd row278.67
4th row0.00
5th row21.01
ValueCountFrequency (%)
0.00 1993
 
19.9%
n 52
 
0.5%
27.80 18
 
0.2%
35.26 14
 
0.1%
17.50 14
 
0.1%
18.79 13
 
0.1%
24.71 13
 
0.1%
21.62 13
 
0.1%
18.53 12
 
0.1%
31.66 11
 
0.1%
Other values (5047) 7847
78.5%
2023-12-11T17:12:25.257946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9948
20.3%
0 8584
17.6%
1 4889
10.0%
2 4276
8.7%
3 3667
 
7.5%
4 3285
 
6.7%
5 3088
 
6.3%
6 2826
 
5.8%
7 2777
 
5.7%
8 2727
 
5.6%
Other values (3) 2818
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38833
79.4%
Other Punctuation 10000
 
20.5%
Uppercase Letter 52
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8584
22.1%
1 4889
12.6%
2 4276
11.0%
3 3667
9.4%
4 3285
 
8.5%
5 3088
 
8.0%
6 2826
 
7.3%
7 2777
 
7.2%
8 2727
 
7.0%
9 2714
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 9948
99.5%
\ 52
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48833
99.9%
Latin 52
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9948
20.4%
0 8584
17.6%
1 4889
10.0%
2 4276
8.8%
3 3667
 
7.5%
4 3285
 
6.7%
5 3088
 
6.3%
6 2826
 
5.8%
7 2777
 
5.7%
8 2727
 
5.6%
Other values (2) 2766
 
5.7%
Latin
ValueCountFrequency (%)
N 52
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9948
20.3%
0 8584
17.6%
1 4889
10.0%
2 4276
8.7%
3 3667
 
7.5%
4 3285
 
6.7%
5 3088
 
6.3%
6 2826
 
5.8%
7 2777
 
5.7%
8 2727
 
5.6%
Other values (3) 2818
 
5.8%

탄소량
Text

Distinct386
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T17:12:25.813339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9897
Min length2

Characters and Unicode

Total characters39897
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

Unique109 ?
Unique (%)1.1%

Sample

1st row0.00
2nd row0.19
3rd row2.59
4th row0.00
5th row0.19
ValueCountFrequency (%)
0.00 2006
 
20.1%
0.16 188
 
1.9%
0.19 187
 
1.9%
0.22 173
 
1.7%
0.17 167
 
1.7%
0.29 165
 
1.7%
0.18 159
 
1.6%
0.23 154
 
1.5%
0.26 153
 
1.5%
0.15 147
 
1.5%
Other values (376) 6501
65.0%
2023-12-11T17:12:26.501799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14197
35.6%
. 9948
24.9%
1 3181
 
8.0%
2 2616
 
6.6%
3 2093
 
5.2%
4 1650
 
4.1%
5 1441
 
3.6%
6 1265
 
3.2%
7 1171
 
2.9%
8 1124
 
2.8%
Other values (3) 1211
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29845
74.8%
Other Punctuation 10000
 
25.1%
Uppercase Letter 52
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14197
47.6%
1 3181
 
10.7%
2 2616
 
8.8%
3 2093
 
7.0%
4 1650
 
5.5%
5 1441
 
4.8%
6 1265
 
4.2%
7 1171
 
3.9%
8 1124
 
3.8%
9 1107
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 9948
99.5%
\ 52
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39845
99.9%
Latin 52
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14197
35.6%
. 9948
25.0%
1 3181
 
8.0%
2 2616
 
6.6%
3 2093
 
5.3%
4 1650
 
4.1%
5 1441
 
3.6%
6 1265
 
3.2%
7 1171
 
2.9%
8 1124
 
2.8%
Other values (2) 1159
 
2.9%
Latin
ValueCountFrequency (%)
N 52
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14197
35.6%
. 9948
24.9%
1 3181
 
8.0%
2 2616
 
6.6%
3 2093
 
5.2%
4 1650
 
4.1%
5 1441
 
3.6%
6 1265
 
3.2%
7 1171
 
2.9%
8 1124
 
2.8%
Other values (3) 1211
 
3.0%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4052
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.6388
Minimum0
Maximum61649.5
Zeros2042
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:12:26.702458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1438.3775
median1171.015
Q32330
95-th percentile7168.1665
Maximum61649.5
Range61649.5
Interquartile range (IQR)1891.6225

Descriptive statistics

Standard deviation2830.1253
Coefficient of variation (CV)1.433237
Kurtosis36.197745
Mean1974.6388
Median Absolute Deviation (MAD)938.985
Skewness4.2316282
Sum19746388
Variance8009609.5
MonotonicityNot monotonic
2023-12-11T17:12:26.896776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2042
 
20.4%
730.0 32
 
0.3%
1080.0 31
 
0.3%
1230.0 31
 
0.3%
680.0 30
 
0.3%
840.0 30
 
0.3%
770.0 30
 
0.3%
700.0 28
 
0.3%
810.0 26
 
0.3%
940.0 26
 
0.3%
Other values (4042) 7694
76.9%
ValueCountFrequency (%)
0.0 2042
20.4%
0.1 3
 
< 0.1%
0.2 1
 
< 0.1%
0.26 4
 
< 0.1%
10.0 6
 
0.1%
20.0 2
 
< 0.1%
30.0 6
 
0.1%
40.0 2
 
< 0.1%
50.0 2
 
< 0.1%
60.0 2
 
< 0.1%
ValueCountFrequency (%)
61649.5 1
< 0.1%
32812.58 1
< 0.1%
30930.0 1
< 0.1%
29910.0 1
< 0.1%
28504.49 1
< 0.1%
27002.9 1
< 0.1%
26970.0 1
< 0.1%
26210.0 1
< 0.1%
26140.0 1
< 0.1%
24650.0 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.9469
Minimum0
Maximum344
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:12:27.093994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median12
Q327
95-th percentile77
Maximum344
Range344
Interquartile range (IQR)21

Descriptive statistics

Standard deviation25.853845
Coefficient of variation (CV)1.1780181
Kurtosis11.869229
Mean21.9469
Median Absolute Deviation (MAD)7
Skewness2.732661
Sum219469
Variance668.42132
MonotonicityNot monotonic
2023-12-11T17:12:27.263023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 598
 
6.0%
6 586
 
5.9%
5 544
 
5.4%
7 543
 
5.4%
3 483
 
4.8%
8 479
 
4.8%
10 420
 
4.2%
9 416
 
4.2%
11 368
 
3.7%
2 323
 
3.2%
Other values (158) 5240
52.4%
ValueCountFrequency (%)
0 6
 
0.1%
1 97
 
1.0%
2 323
3.2%
3 483
4.8%
4 598
6.0%
5 544
5.4%
6 586
5.9%
7 543
5.4%
8 479
4.8%
9 416
4.2%
ValueCountFrequency (%)
344 1
< 0.1%
315 1
< 0.1%
290 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
217 1
< 0.1%
212 1
< 0.1%
205 1
< 0.1%
196 1
< 0.1%

Interactions

2023-12-11T17:12:19.947323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:17.145678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:17.732844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:18.310509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:18.916258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:20.092979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:17.268389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:17.846309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:18.429700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:19.053771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:20.252836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:17.378163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:17.957609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:18.562237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:19.531367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:20.413776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:17.511369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:18.071249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:18.683238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:19.669797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:20.566214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:17.610949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:18.191655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:18.778989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:12:19.796253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:12:27.398735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여시간대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.4100.0730.0870.0800.0610.0930.0500.080
대여시간0.4101.0000.0720.1060.1150.1040.0540.0600.121
대여소번호0.0730.0721.0000.0150.0420.0670.0410.0610.051
대여구분코드0.0870.1060.0151.0000.0080.3590.2100.1030.145
성별0.0800.1150.0420.0081.0000.1160.0000.0000.000
연령대코드0.0610.1040.0670.3590.1161.0000.0450.0650.093
이용건수0.0930.0540.0410.2100.0000.0451.0000.0720.138
이동거리(M)0.0500.0600.0610.1030.0000.0650.0721.0000.622
이용시간(분)0.0800.1210.0510.1450.0000.0930.1380.6221.000
2023-12-11T17:12:27.539122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.0020.167
성별0.0021.0000.073
연령대코드0.1670.0731.000
2023-12-11T17:12:27.662138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여시간대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여시간1.000-0.0210.0410.0310.1050.0640.0680.050
대여소번호-0.0211.000-0.0270.011-0.0180.0090.0250.032
이용건수0.041-0.0271.0000.1470.1830.1360.0000.025
이동거리(M)0.0310.0110.1471.0000.5550.0710.0000.035
이용시간(분)0.105-0.0180.1830.5551.0000.0930.0000.046
대여구분코드0.0640.0090.1360.0710.0931.0000.0020.167
성별0.0680.0250.0000.0000.0000.0021.0000.073
연령대코드0.0500.0320.0250.0350.0460.1670.0731.000

Missing values

2023-12-11T17:12:20.765647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:12:20.998425image/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

대여일자대여시간대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
670932022-01-039543543. 구의공원(테크노마트 앞)정기권<NA>30대10.000.000.02
87352022-01-011318511851. 가산디지털단지 7번출구정기권<NA>20대122.950.19840.04
839252022-01-031643604360. 르노삼성자동차 성수사업소 앞정기권<NA>50대1278.672.5911170.087
346092022-01-0212157157. 애오개역 4번출구 앞일일권M기타10.000.000.08
529272022-01-022112121212. 송파역 2번 출구앞정기권M20대121.010.19829.04
555132022-01-0223176176. 명지대학교 도서관정기권M50대146.670.391660.08
2812022-01-01037993799. 국립항공박물관정기권M기타131.400.281220.08
886552022-01-031742134213. 마포구중앙도서관 정문 앞정기권M50대189.320.813470.022
86022022-01-0112786786.목동아파트 8단지 상가동 앞정기권M40대131.660.291230.06
941552022-01-0318540540. 군자역 7번출구 베스트샵 앞정기권M30대10.000.000.090
대여일자대여시간대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
626612022-01-03816851685. 불암고등학교 앞 횡단보도정기권M~10대10.000.000.015
403552022-01-021523422342. 대청역 1번출구 뒤정기권F50대121.620.18780.06
332292022-01-021139083908. 오류동 다숲오피스텔 앞정기권<NA>기타124.450.22950.012
425142022-01-021642204220. 신촌 청년주택정기권<NA>20대10.000.000.012
396682022-01-0215511511. 서울숲역 5번 출구 옆정기권<NA>20대117.820.21900.09
534672022-01-022123012301. 현대고등학교 건너편정기권<NA>20대2137.351.315640.039
780502022-01-0314164164. 북가좌1동 주민센터정기권<NA>40대10.000.000.070
882542022-01-031729092909. 석계역 3번 출구정기권F기타132.180.291250.011
887362022-01-031736343634. 국악고교 앞 교차로정기권M70대이상10.000.000.01
422072022-01-0215253253. 신풍역 5번출구 인근정기권M40대140.410.361570.020