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

대여구분코드 is highly imbalanced (56.4%)Imbalance
이동거리(M) has 3611 (36.1%) zerosZeros

Reproduction

Analysis started2024-04-20 17:42:14.377325
Analysis finished2024-04-20 17:42:21.255493
Duration6.88 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
2021-01-01
6542 
2021-01-02
3458 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021-01-01 6542
65.4%
2021-01-02 3458
34.6%

Length

2024-04-21T02:42:21.437934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:42:21.734801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-01 6542
65.4%
2021-01-02 3458
34.6%

대여소번호
Real number (ℝ)

Distinct1882
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1170.6289
Minimum101
Maximum3587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T02:42:22.059293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile163
Q1524
median1009
Q31651
95-th percentile2820
Maximum3587
Range3486
Interquartile range (IQR)1127

Descriptive statistics

Standard deviation841.62683
Coefficient of variation (CV)0.71895271
Kurtosis0.27483534
Mean1170.6289
Median Absolute Deviation (MAD)522
Skewness0.95672727
Sum11706289
Variance708335.71
MonotonicityNot monotonic
2024-04-21T02:42:22.497044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
565 29
 
0.3%
1295 28
 
0.3%
792 26
 
0.3%
272 23
 
0.2%
419 23
 
0.2%
853 22
 
0.2%
1153 22
 
0.2%
123 22
 
0.2%
1160 22
 
0.2%
956 21
 
0.2%
Other values (1872) 9762
97.6%
ValueCountFrequency (%)
101 5
0.1%
102 11
0.1%
103 12
0.1%
104 8
0.1%
105 12
0.1%
106 12
0.1%
107 11
0.1%
108 8
0.1%
109 6
0.1%
111 8
0.1%
ValueCountFrequency (%)
3587 2
 
< 0.1%
3586 1
 
< 0.1%
3582 5
0.1%
3581 2
 
< 0.1%
3579 5
0.1%
3578 3
< 0.1%
3575 4
< 0.1%
3573 5
0.1%
3571 5
0.1%
3570 3
< 0.1%

대여소
Text

Distinct1882
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T02:42:23.436734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length15.2453
Min length7

Characters and Unicode

Total characters152453
Distinct characters546
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

Unique254 ?
Unique (%)2.5%

Sample

1st row493.홍대입구역 6번출구
2nd row224. 롯데캐슬 앞
3rd row114. 홍대입구역 8번출구 앞
4th row2220. 반포본동 주민센터 앞
5th row1702. 녹천역 1번출구 앞
ValueCountFrequency (%)
앞 2638
 
8.9%
옆 450
 
1.5%
1번출구 406
 
1.4%
출구 406
 
1.4%
2번출구 258
 
0.9%
사거리 257
 
0.9%
3번출구 242
 
0.8%
뒤 239
 
0.8%
입구 205
 
0.7%
4번출구 205
 
0.7%
Other values (3733) 24371
82.1%
2024-04-21T02:42:24.516304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19890
 
13.0%
. 10015
 
6.6%
1 8777
 
5.8%
2 6228
 
4.1%
3 4075
 
2.7%
구 3538
 
2.3%
5 3522
 
2.3%
0 3360
 
2.2%
4 3266
 
2.1%
앞 3182
 
2.1%
Other values (536) 86600
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78643
51.6%
Decimal Number 40305
26.4%
Space Separator 19890
 
13.0%
Other Punctuation 10093
 
6.6%
Uppercase Letter 1486
 
1.0%
Close Punctuation 917
 
0.6%
Open Punctuation 917
 
0.6%
Lowercase Letter 90
 
0.1%
Dash Punctuation 82
 
0.1%
Math Symbol 19
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3538
 
4.5%
앞 3182
 
4.0%
역 2897
 
3.7%
번 2599
 
3.3%
출 2528
 
3.2%
동 2121
 
2.7%
교 1670
 
2.1%
리 1403
 
1.8%
아 1276
 
1.6%
파 1272
 
1.6%
Other values (480) 56157
71.4%
Uppercase Letter
ValueCountFrequency (%)
S 196
13.2%
K 166
11.2%
C 131
8.8%
G 118
 
7.9%
L 112
 
7.5%
B 111
 
7.5%
T 105
 
7.1%
I 96
 
6.5%
A 84
 
5.7%
M 64
 
4.3%
Other values (13) 303
20.4%
Lowercase Letter
ValueCountFrequency (%)
e 33
36.7%
t 11
 
12.2%
k 10
 
11.1%
l 8
 
8.9%
n 8
 
8.9%
c 4
 
4.4%
m 4
 
4.4%
o 4
 
4.4%
y 4
 
4.4%
s 3
 
3.3%
Decimal Number
ValueCountFrequency (%)
1 8777
21.8%
2 6228
15.5%
3 4075
10.1%
5 3522
8.7%
0 3360
 
8.3%
4 3266
 
8.1%
6 3148
 
7.8%
7 3023
 
7.5%
9 2513
 
6.2%
8 2393
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 10015
99.2%
, 65
 
0.6%
& 8
 
0.1%
? 5
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 18
94.7%
+ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
19890
100.0%
Close Punctuation
ValueCountFrequency (%)
) 917
100.0%
Open Punctuation
ValueCountFrequency (%)
( 917
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78645
51.6%
Common 72232
47.4%
Latin 1576
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3538
 
4.5%
앞 3182
 
4.0%
역 2897
 
3.7%
번 2599
 
3.3%
출 2528
 
3.2%
동 2121
 
2.7%
교 1670
 
2.1%
리 1403
 
1.8%
아 1276
 
1.6%
파 1272
 
1.6%
Other values (481) 56159
71.4%
Latin
ValueCountFrequency (%)
S 196
12.4%
K 166
10.5%
C 131
 
8.3%
G 118
 
7.5%
L 112
 
7.1%
B 111
 
7.0%
T 105
 
6.7%
I 96
 
6.1%
A 84
 
5.3%
M 64
 
4.1%
Other values (24) 393
24.9%
Common
ValueCountFrequency (%)
19890
27.5%
. 10015
13.9%
1 8777
12.2%
2 6228
 
8.6%
3 4075
 
5.6%
5 3522
 
4.9%
0 3360
 
4.7%
4 3266
 
4.5%
6 3148
 
4.4%
7 3023
 
4.2%
Other values (11) 6928
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78643
51.6%
ASCII 73808
48.4%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19890
26.9%
. 10015
13.6%
1 8777
11.9%
2 6228
 
8.4%
3 4075
 
5.5%
5 3522
 
4.8%
0 3360
 
4.6%
4 3266
 
4.4%
6 3148
 
4.3%
7 3023
 
4.1%
Other values (45) 8504
11.5%
Hangul
ValueCountFrequency (%)
구 3538
 
4.5%
앞 3182
 
4.0%
역 2897
 
3.7%
번 2599
 
3.3%
출 2528
 
3.2%
동 2121
 
2.7%
교 1670
 
2.1%
리 1403
 
1.8%
아 1276
 
1.6%
파 1272
 
1.6%
Other values (480) 56157
71.4%
None
ValueCountFrequency (%)
㈜ 2
100.0%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7032 
일일(회원)
2759 
단체
 
124
일일(비회원)
 
73
BIL_021
 
12

Length

Max length7
Median length2
Mean length3.1461
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기
2nd row정기
3rd row정기
4th row정기
5th row일일(비회원)

Common Values

ValueCountFrequency (%)
정기 7032
70.3%
일일(회원) 2759
 
27.6%
단체 124
 
1.2%
일일(비회원) 73
 
0.7%
BIL_021 12
 
0.1%

Length

2024-04-21T02:42:24.747923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:42:24.931862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7032
70.3%
일일(회원 2759
 
27.6%
단체 124
 
1.2%
일일(비회원 73
 
0.7%
bil_021 12
 
0.1%

성별
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3794 
M
3458 
F
2165 
<NA>
583 

Length

Max length4
Median length1
Mean length1.5543
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3794
37.9%
M 3458
34.6%
F 2165
21.6%
<NA> 583
 
5.8%

Length

2024-04-21T02:42:25.137699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:42:25.319781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3794
37.9%
m 3458
34.6%
f 2165
21.6%
na 583
 
5.8%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
3334 
AGE_003
2421 
AGE_004
1787 
AGE_005
1148 
AGE_001
586 
Other values (3)
724 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_003
2nd rowAGE_003
3rd rowAGE_006
4th rowAGE_003
5th rowAGE_008

Common Values

ValueCountFrequency (%)
AGE_002 3334
33.3%
AGE_003 2421
24.2%
AGE_004 1787
17.9%
AGE_005 1148
 
11.5%
AGE_001 586
 
5.9%
AGE_006 431
 
4.3%
AGE_008 233
 
2.3%
AGE_007 60
 
0.6%

Length

2024-04-21T02:42:25.507472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:42:25.705153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 3334
33.3%
age_003 2421
24.2%
age_004 1787
17.9%
age_005 1148
 
11.5%
age_001 586
 
5.9%
age_006 431
 
4.3%
age_008 233
 
2.3%
age_007 60
 
0.6%

이용건수
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.506
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T02:42:26.062407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.98775582
Coefficient of variation (CV)0.65588036
Kurtosis11.525779
Mean1.506
Median Absolute Deviation (MAD)0
Skewness2.9147426
Sum15060
Variance0.97566157
MonotonicityNot monotonic
2024-04-21T02:42:26.410017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 6952
69.5%
2 1913
 
19.1%
3 666
 
6.7%
4 253
 
2.5%
5 113
 
1.1%
6 49
 
0.5%
7 31
 
0.3%
8 14
 
0.1%
9 6
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
1 6952
69.5%
2 1913
 
19.1%
3 666
 
6.7%
4 253
 
2.5%
5 113
 
1.1%
6 49
 
0.5%
7 31
 
0.3%
8 14
 
0.1%
9 6
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
9 6
 
0.1%
8 14
 
0.1%
7 31
 
0.3%
6 49
 
0.5%
5 113
 
1.1%
4 253
 
2.5%
3 666
 
6.7%
2 1913
 
19.1%
1 6952
69.5%

운동량
Text

Distinct5489
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T02:42:27.481393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.8611
Min length2

Characters and Unicode

Total characters48611
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

Unique4739 ?
Unique (%)47.4%

Sample

1st row14.88
2nd row0.00
3rd row49.90
4th row0.00
5th row0.00
ValueCountFrequency (%)
0.00 3560
35.6%
n 51
 
0.5%
36.38 5
 
< 0.1%
27.76 5
 
< 0.1%
17.21 5
 
< 0.1%
23.29 5
 
< 0.1%
24.71 4
 
< 0.1%
20.28 4
 
< 0.1%
14.12 4
 
< 0.1%
56.89 4
 
< 0.1%
Other values (5479) 6353
63.5%
2024-04-21T02:42:29.042517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12963
26.7%
. 9949
20.5%
1 4146
 
8.5%
2 3504
 
7.2%
3 2920
 
6.0%
4 2785
 
5.7%
5 2623
 
5.4%
6 2491
 
5.1%
7 2413
 
5.0%
8 2388
 
4.9%
Other values (3) 2429
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38560
79.3%
Other Punctuation 10000
 
20.6%
Uppercase Letter 51
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12963
33.6%
1 4146
 
10.8%
2 3504
 
9.1%
3 2920
 
7.6%
4 2785
 
7.2%
5 2623
 
6.8%
6 2491
 
6.5%
7 2413
 
6.3%
8 2388
 
6.2%
9 2327
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 9949
99.5%
\ 51
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48560
99.9%
Latin 51
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12963
26.7%
. 9949
20.5%
1 4146
 
8.5%
2 3504
 
7.2%
3 2920
 
6.0%
4 2785
 
5.7%
5 2623
 
5.4%
6 2491
 
5.1%
7 2413
 
5.0%
8 2388
 
4.9%
Other values (2) 2378
 
4.9%
Latin
ValueCountFrequency (%)
N 51
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12963
26.7%
. 9949
20.5%
1 4146
 
8.5%
2 3504
 
7.2%
3 2920
 
6.0%
4 2785
 
5.7%
5 2623
 
5.4%
6 2491
 
5.1%
7 2413
 
5.0%
8 2388
 
4.9%
Other values (3) 2429
 
5.0%

탄소량
Text

Distinct564
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T02:42:30.650205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9908
Min length2

Characters and Unicode

Total characters39908
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

Unique150 ?
Unique (%)1.5%

Sample

1st row0.12
2nd row0.00
3rd row0.49
4th row0.00
5th row0.00
ValueCountFrequency (%)
0.00 3560
35.6%
0.15 86
 
0.9%
0.19 82
 
0.8%
0.16 81
 
0.8%
0.18 80
 
0.8%
0.29 75
 
0.8%
0.17 75
 
0.8%
0.22 75
 
0.8%
0.24 74
 
0.7%
0.23 72
 
0.7%
Other values (554) 5740
57.4%
2024-04-21T02:42:32.616135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15970
40.0%
. 9949
24.9%
1 2829
 
7.1%
2 2147
 
5.4%
3 1719
 
4.3%
4 1503
 
3.8%
5 1365
 
3.4%
6 1130
 
2.8%
8 1096
 
2.7%
7 1078
 
2.7%
Other values (3) 1122
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29857
74.8%
Other Punctuation 10000
 
25.1%
Uppercase Letter 51
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15970
53.5%
1 2829
 
9.5%
2 2147
 
7.2%
3 1719
 
5.8%
4 1503
 
5.0%
5 1365
 
4.6%
6 1130
 
3.8%
8 1096
 
3.7%
7 1078
 
3.6%
9 1020
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 9949
99.5%
\ 51
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39857
99.9%
Latin 51
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15970
40.1%
. 9949
25.0%
1 2829
 
7.1%
2 2147
 
5.4%
3 1719
 
4.3%
4 1503
 
3.8%
5 1365
 
3.4%
6 1130
 
2.8%
8 1096
 
2.7%
7 1078
 
2.7%
Other values (2) 1071
 
2.7%
Latin
ValueCountFrequency (%)
N 51
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15970
40.0%
. 9949
24.9%
1 2829
 
7.1%
2 2147
 
5.4%
3 1719
 
4.3%
4 1503
 
3.8%
5 1365
 
3.4%
6 1130
 
2.8%
8 1096
 
2.7%
7 1078
 
2.7%
Other values (3) 1122
 
2.8%

이동거리(M)
Real number (ℝ)

ZEROS 

Distinct6145
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3028.1013
Minimum0
Maximum81987.37
Zeros3611
Zeros (%)36.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T02:42:33.037688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1151.11
Q33780.5325
95-th percentile12684.429
Maximum81987.37
Range81987.37
Interquartile range (IQR)3780.5325

Descriptive statistics

Standard deviation5051.1241
Coefficient of variation (CV)1.6680829
Kurtosis22.930348
Mean3028.1013
Median Absolute Deviation (MAD)1151.11
Skewness3.6295736
Sum30281013
Variance25513855
MonotonicityNot monotonic
2024-04-21T02:42:33.457474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3611
36.1%
111.2 11
 
0.1%
222.39 7
 
0.1%
630.0 6
 
0.1%
970.0 5
 
0.1%
141.9 5
 
0.1%
555.97 4
 
< 0.1%
790.0 4
 
< 0.1%
1320.0 4
 
< 0.1%
350.42 4
 
< 0.1%
Other values (6135) 6339
63.4%
ValueCountFrequency (%)
0.0 3611
36.1%
40.0 2
 
< 0.1%
88.01 1
 
< 0.1%
88.08 2
 
< 0.1%
88.13 2
 
< 0.1%
88.15 1
 
< 0.1%
88.16 1
 
< 0.1%
88.17 3
 
< 0.1%
88.2 1
 
< 0.1%
88.21 2
 
< 0.1%
ValueCountFrequency (%)
81987.37 1
< 0.1%
67774.41 1
< 0.1%
60273.26 1
< 0.1%
52797.53 1
< 0.1%
52449.85 1
< 0.1%
49363.75 1
< 0.1%
46740.0 1
< 0.1%
46414.56 1
< 0.1%
45730.0 1
< 0.1%
44055.72 1
< 0.1%

이용시간(분)
Real number (ℝ)

Distinct317
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.5636
Minimum0
Maximum1491
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T02:42:33.855792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112
median29
Q361
95-th percentile141
Maximum1491
Range1491
Interquartile range (IQR)49

Descriptive statistics

Standard deviation56.75096
Coefficient of variation (CV)1.2187838
Kurtosis80.799473
Mean46.5636
Median Absolute Deviation (MAD)20
Skewness5.6587659
Sum465636
Variance3220.6714
MonotonicityNot monotonic
2024-04-21T02:42:34.294864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 301
 
3.0%
10 276
 
2.8%
6 269
 
2.7%
9 264
 
2.6%
8 259
 
2.6%
7 256
 
2.6%
11 237
 
2.4%
12 225
 
2.2%
4 214
 
2.1%
3 210
 
2.1%
Other values (307) 7489
74.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 30
 
0.3%
2 124
1.2%
3 210
2.1%
4 214
2.1%
5 301
3.0%
6 269
2.7%
7 256
2.6%
8 259
2.6%
9 264
2.6%
ValueCountFrequency (%)
1491 1
< 0.1%
1001 1
< 0.1%
986 1
< 0.1%
919 1
< 0.1%
870 1
< 0.1%
809 1
< 0.1%
740 1
< 0.1%
639 1
< 0.1%
619 1
< 0.1%
568 1
< 0.1%

Interactions

2024-04-21T02:42:19.357693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:16.040622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:17.105000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:18.133485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:19.633712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:16.313452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:17.374013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:18.400212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:19.889267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:16.572057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:17.619361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:18.849636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:20.146197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:16.827592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:17.864384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:42:19.092321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T02:42:34.572657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.5930.0360.0030.0450.0360.0480.000
대여소번호0.5931.0000.0480.0110.0540.0000.1020.044
대여구분코드0.0360.0481.0000.1180.4600.2060.2260.160
성별0.0030.0110.1181.0000.2230.1430.0000.032
연령대코드0.0450.0540.4600.2231.0000.1690.0440.084
이용건수0.0360.0000.2060.1430.1691.0000.3160.347
이동거리(M)0.0480.1020.2260.0000.0440.3161.0000.584
이용시간(분)0.0000.0440.1600.0320.0840.3470.5841.000
2024-04-21T02:42:34.864456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자연령대코드대여구분코드성별
대여일자1.0000.0340.0440.006
연령대코드0.0341.0000.3040.144
대여구분코드0.0440.3041.0000.089
성별0.0060.1440.0891.000
2024-04-21T02:42:35.331961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여일자대여구분코드성별연령대코드
대여소번호1.000-0.035-0.005-0.0140.4590.0200.0070.025
이용건수-0.0351.0000.3570.4690.0280.0870.0850.081
이동거리(M)-0.0050.3571.0000.4280.0370.0960.0000.021
이용시간(분)-0.0140.4690.4281.0000.0000.0980.0200.028
대여일자0.4590.0280.0370.0001.0000.0440.0060.034
대여구분코드0.0200.0870.0960.0980.0441.0000.0890.304
성별0.0070.0850.0000.0200.0060.0891.0000.144
연령대코드0.0250.0810.0210.0280.0340.3040.1441.000

Missing values

2024-04-21T02:42:20.527014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T02:42:21.033060image/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)
175122021-01-02493493.홍대입구역 6번출구정기MAGE_003114.880.12521.832
10082021-01-01224224. 롯데캐슬 앞정기MAGE_00310.000.000.07
1302021-01-01114114. 홍대입구역 8번출구 앞정기MAGE_006149.900.492100.031
116942021-01-0122202220. 반포본동 주민센터 앞정기\NAGE_00320.000.000.067
95272021-01-0117021702. 녹천역 1번출구 앞일일(비회원)\NAGE_00810.000.000.0203
176802021-01-02518518. 청계천 박물관 앞정기\NAGE_006224.980.251087.5113
81942021-01-0114381438. 홈플러스 신내점 앞일일(회원)<NA>AGE_002133.200.23998.146
216832021-01-0211731173. 강서구청사거리(SH타워)일일(회원)\NAGE_0022147.991.456228.5357
91892021-01-0116561656. 중앙하이츠 아파트 입구단체\NAGE_0043218.602.139200.18173
67832021-01-0111901190. 마곡역 교차로(2번출구)정기FAGE_006115.820.14614.755
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
24692021-01-01441441.염천교사거리정기MAGE_00410.000.000.03
16802021-01-01302302. 경복궁역 4번출구 뒤일일(회원)\NAGE_004146.040.361550.024
13772021-01-01263263. 근로자회관 사거리일일(회원)FAGE_0041144.531.576758.9990
110342021-01-0120842084.e편한세상 상도노빌리티 앞정기FAGE_00310.000.000.010
114632021-01-0121752175. 신림동걷고싶은문화의거리입구정기\NAGE_00420.000.000.0115
202782021-01-02943943. 은평구청 보건소단체MAGE_004249.470.441921.8920
107592021-01-0120152015. 신대방삼거리역 6번출구쪽일일(회원)\NAGE_00210.000.000.011
25522021-01-01472472.삼일교(시그니쳐 타워)정기MAGE_0023163.221.476341.0695
124772021-01-0124082408. 강남한양수자인아파트정기MAGE_005162.000.461981.9811
95242021-01-0117001700.상계보람아파트 202동정기\NAGE_00810.000.000.039