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

DateTime1
Numeric4
Text3
Categorical3

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 (62.6%)Imbalance
이동거리(M) has 595 (5.9%) zerosZeros

Reproduction

Analysis started2024-05-18 05:02:53.483807
Analysis finished2024-05-18 05:03:03.518045
Duration10.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-03-01 00:00:00
Maximum2021-03-02 00:00:00
2024-05-18T14:03:03.658543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:04.001237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

대여소번호
Real number (ℝ)

Distinct1598
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean990.7636
Minimum5
Maximum3582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:03:04.594052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile170
Q1472.75
median931
Q31414
95-th percentile1964
Maximum3582
Range3577
Interquartile range (IQR)941.25

Descriptive statistics

Standard deviation628.32856
Coefficient of variation (CV)0.63418616
Kurtosis0.9797612
Mean990.7636
Median Absolute Deviation (MAD)475
Skewness0.81525449
Sum9907636
Variance394796.78
MonotonicityNot monotonic
2024-05-18T14:03:05.376818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
583 27
 
0.3%
284 21
 
0.2%
765 21
 
0.2%
117 20
 
0.2%
602 20
 
0.2%
207 19
 
0.2%
1167 19
 
0.2%
1152 19
 
0.2%
770 18
 
0.2%
114 18
 
0.2%
Other values (1588) 9798
98.0%
ValueCountFrequency (%)
5 1
 
< 0.1%
10 1
 
< 0.1%
101 9
0.1%
102 16
0.2%
103 9
0.1%
104 8
0.1%
105 10
0.1%
106 14
0.1%
107 13
0.1%
108 5
 
0.1%
ValueCountFrequency (%)
3582 1
 
< 0.1%
3581 2
< 0.1%
3579 1
 
< 0.1%
3578 3
< 0.1%
3575 1
 
< 0.1%
3571 2
< 0.1%
3570 2
< 0.1%
3569 2
< 0.1%
3566 1
 
< 0.1%
3563 1
 
< 0.1%

대여소
Text

Distinct1598
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:03:06.239749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length15.2017
Min length3

Characters and Unicode

Total characters152017
Distinct characters523
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

Unique216 ?
Unique (%)2.2%

Sample

1st row1320. LG베스트샵 종암점
2nd row1065.보훈병원 정문옆(중앙대영약국)
3rd row1468.먹골역 7번 출구
4th row1988. 고척LIGA아파트 앞
5th row234. 영등포구민체육센터 앞
ValueCountFrequency (%)
앞 2741
 
9.1%
옆 523
 
1.7%
출구 386
 
1.3%
1번출구 344
 
1.1%
사거리 279
 
0.9%
2번출구 243
 
0.8%
3번출구 239
 
0.8%
4번출구 228
 
0.8%
뒤 226
 
0.7%
교차로 217
 
0.7%
Other values (3229) 24751
82.0%
2024-05-18T14:03:07.516295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20380
 
13.4%
. 10019
 
6.6%
1 9340
 
6.1%
2 4732
 
3.1%
3 3844
 
2.5%
4 3612
 
2.4%
5 3509
 
2.3%
구 3471
 
2.3%
앞 3293
 
2.2%
6 3293
 
2.2%
Other values (513) 86524
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78587
51.7%
Decimal Number 39580
26.0%
Space Separator 20380
 
13.4%
Other Punctuation 10098
 
6.6%
Uppercase Letter 1485
 
1.0%
Open Punctuation 859
 
0.6%
Close Punctuation 859
 
0.6%
Lowercase Letter 97
 
0.1%
Dash Punctuation 51
 
< 0.1%
Math Symbol 10
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3471
 
4.4%
앞 3293
 
4.2%
역 2781
 
3.5%
번 2528
 
3.2%
출 2472
 
3.1%
동 2072
 
2.6%
교 1601
 
2.0%
리 1460
 
1.9%
아 1195
 
1.5%
파 1148
 
1.5%
Other values (457) 56566
72.0%
Uppercase Letter
ValueCountFrequency (%)
K 195
13.1%
S 179
12.1%
C 129
 
8.7%
G 111
 
7.5%
T 107
 
7.2%
B 104
 
7.0%
L 97
 
6.5%
A 82
 
5.5%
I 79
 
5.3%
D 62
 
4.2%
Other values (14) 340
22.9%
Decimal Number
ValueCountFrequency (%)
1 9340
23.6%
2 4732
12.0%
3 3844
9.7%
4 3612
 
9.1%
5 3509
 
8.9%
6 3293
 
8.3%
7 3073
 
7.8%
0 3051
 
7.7%
8 2567
 
6.5%
9 2559
 
6.5%
Lowercase Letter
ValueCountFrequency (%)
e 27
27.8%
n 16
16.5%
l 12
12.4%
t 12
12.4%
k 9
 
9.3%
y 8
 
8.2%
m 4
 
4.1%
o 4
 
4.1%
c 4
 
4.1%
s 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 10019
99.2%
, 64
 
0.6%
& 8
 
0.1%
? 7
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 9
90.0%
+ 1
 
10.0%
Space Separator
ValueCountFrequency (%)
20380
100.0%
Open Punctuation
ValueCountFrequency (%)
( 859
100.0%
Close Punctuation
ValueCountFrequency (%)
) 859
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78591
51.7%
Common 71844
47.3%
Latin 1582
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3471
 
4.4%
앞 3293
 
4.2%
역 2781
 
3.5%
번 2528
 
3.2%
출 2472
 
3.1%
동 2072
 
2.6%
교 1601
 
2.0%
리 1460
 
1.9%
아 1195
 
1.5%
파 1148
 
1.5%
Other values (458) 56570
72.0%
Latin
ValueCountFrequency (%)
K 195
12.3%
S 179
11.3%
C 129
 
8.2%
G 111
 
7.0%
T 107
 
6.8%
B 104
 
6.6%
L 97
 
6.1%
A 82
 
5.2%
I 79
 
5.0%
D 62
 
3.9%
Other values (24) 437
27.6%
Common
ValueCountFrequency (%)
20380
28.4%
. 10019
13.9%
1 9340
13.0%
2 4732
 
6.6%
3 3844
 
5.4%
4 3612
 
5.0%
5 3509
 
4.9%
6 3293
 
4.6%
7 3073
 
4.3%
0 3051
 
4.2%
Other values (11) 6991
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78587
51.7%
ASCII 73426
48.3%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20380
27.8%
. 10019
13.6%
1 9340
12.7%
2 4732
 
6.4%
3 3844
 
5.2%
4 3612
 
4.9%
5 3509
 
4.8%
6 3293
 
4.5%
7 3073
 
4.2%
0 3051
 
4.2%
Other values (45) 8573
11.7%
Hangul
ValueCountFrequency (%)
구 3471
 
4.4%
앞 3293
 
4.2%
역 2781
 
3.5%
번 2528
 
3.2%
출 2472
 
3.1%
동 2072
 
2.6%
교 1601
 
2.0%
리 1460
 
1.9%
아 1195
 
1.5%
파 1148
 
1.5%
Other values (457) 56566
72.0%
None
ValueCountFrequency (%)
㈜ 4
100.0%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7585 
일일(회원)
2321 
일일(비회원)
 
45
단체
 
38
BIL_021
 
11

Length

Max length7
Median length2
Mean length2.9564
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 7585
75.8%
일일(회원) 2321
 
23.2%
일일(비회원) 45
 
0.4%
단체 38
 
0.4%
BIL_021 11
 
0.1%

Length

2024-05-18T14:03:08.094213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:03:08.466988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7585
75.8%
일일(회원 2321
 
23.2%
일일(비회원 45
 
0.4%
단체 38
 
0.4%
bil_021 11
 
0.1%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3618 
M
3475 
F
2197 
<NA>
702 
m
 
4

Length

Max length4
Median length1
Mean length1.5724
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3618
36.2%
M 3475
34.8%
F 2197
22.0%
<NA> 702
 
7.0%
m 4
 
< 0.1%
f 4
 
< 0.1%

Length

2024-05-18T14:03:08.961425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:03:09.437268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3618
36.2%
m 3479
34.8%
f 2201
22.0%
na 702
 
7.0%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
3371 
AGE_003
2241 
AGE_004
1601 
AGE_005
1248 
AGE_001
719 
Other values (3)
820 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_004
2nd rowAGE_004
3rd rowAGE_008
4th rowAGE_004
5th rowAGE_003

Common Values

ValueCountFrequency (%)
AGE_002 3371
33.7%
AGE_003 2241
22.4%
AGE_004 1601
16.0%
AGE_005 1248
 
12.5%
AGE_001 719
 
7.2%
AGE_006 490
 
4.9%
AGE_008 236
 
2.4%
AGE_007 94
 
0.9%

Length

2024-05-18T14:03:09.920431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:03:10.402066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 3371
33.7%
age_003 2241
22.4%
age_004 1601
16.0%
age_005 1248
 
12.5%
age_001 719
 
7.2%
age_006 490
 
4.9%
age_008 236
 
2.4%
age_007 94
 
0.9%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.005
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:03:11.118080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum27
Range26
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8754004
Coefficient of variation (CV)0.9353618
Kurtosis21.430993
Mean2.005
Median Absolute Deviation (MAD)0
Skewness3.6552463
Sum20050
Variance3.5171267
MonotonicityNot monotonic
2024-05-18T14:03:11.615405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 5865
58.7%
2 1926
 
19.3%
3 893
 
8.9%
4 518
 
5.2%
5 304
 
3.0%
6 176
 
1.8%
8 89
 
0.9%
7 87
 
0.9%
9 41
 
0.4%
10 23
 
0.2%
Other values (13) 78
 
0.8%
ValueCountFrequency (%)
1 5865
58.7%
2 1926
 
19.3%
3 893
 
8.9%
4 518
 
5.2%
5 304
 
3.0%
6 176
 
1.8%
7 87
 
0.9%
8 89
 
0.9%
9 41
 
0.4%
10 23
 
0.2%
ValueCountFrequency (%)
27 1
 
< 0.1%
24 1
 
< 0.1%
23 2
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
18 2
 
< 0.1%
17 4
 
< 0.1%
16 6
0.1%
15 7
0.1%
14 10
0.1%

운동량
Text

Distinct7878
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:03:12.659899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.0615
Min length1

Characters and Unicode

Total characters50615
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

Unique6620 ?
Unique (%)66.2%

Sample

1st row162.4
2nd row0
3rd row92.82
4th row254.22
5th row17.61
ValueCountFrequency (%)
0 575
 
5.8%
n 22
 
0.2%
12.67 9
 
0.1%
21.26 6
 
0.1%
34.63 5
 
< 0.1%
41.23 5
 
< 0.1%
46.85 5
 
< 0.1%
12.24 5
 
< 0.1%
4.54 5
 
< 0.1%
47.96 4
 
< 0.1%
Other values (7868) 9359
93.6%
2024-05-18T14:03:14.590770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9309
18.4%
1 6093
12.0%
2 5082
10.0%
3 4393
8.7%
4 4223
8.3%
6 3915
7.7%
5 3899
7.7%
7 3579
 
7.1%
9 3522
 
7.0%
8 3515
 
6.9%
Other values (3) 3085
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41262
81.5%
Other Punctuation 9331
 
18.4%
Uppercase Letter 22
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6093
14.8%
2 5082
12.3%
3 4393
10.6%
4 4223
10.2%
6 3915
9.5%
5 3899
9.4%
7 3579
8.7%
9 3522
8.5%
8 3515
8.5%
0 3041
7.4%
Other Punctuation
ValueCountFrequency (%)
. 9309
99.8%
\ 22
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50593
> 99.9%
Latin 22
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9309
18.4%
1 6093
12.0%
2 5082
10.0%
3 4393
8.7%
4 4223
8.3%
6 3915
7.7%
5 3899
7.7%
7 3579
 
7.1%
9 3522
 
7.0%
8 3515
 
6.9%
Other values (2) 3063
 
6.1%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9309
18.4%
1 6093
12.0%
2 5082
10.0%
3 4393
8.7%
4 4223
8.3%
6 3915
7.7%
5 3899
7.7%
7 3579
 
7.1%
9 3522
 
7.0%
8 3515
 
6.9%
Other values (3) 3085
 
6.1%

탄소량
Text

Distinct766
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:03:15.741693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7223
Min length1

Characters and Unicode

Total characters37223
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

Unique213 ?
Unique (%)2.1%

Sample

1st row1.12
2nd row0
3rd row0.54
4th row2.17
5th row0.12
ValueCountFrequency (%)
0 580
 
5.8%
0.19 103
 
1.0%
0.18 95
 
0.9%
0.35 94
 
0.9%
0.16 94
 
0.9%
0.32 93
 
0.9%
0.29 92
 
0.9%
0.27 92
 
0.9%
0.24 92
 
0.9%
0.17 85
 
0.9%
Other values (756) 8580
85.8%
2024-05-18T14:03:17.566612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9334
25.1%
0 6885
18.5%
1 4193
11.3%
2 3021
 
8.1%
3 2555
 
6.9%
4 2310
 
6.2%
5 2078
 
5.6%
6 1882
 
5.1%
7 1738
 
4.7%
8 1634
 
4.4%
Other values (3) 1593
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27845
74.8%
Other Punctuation 9356
 
25.1%
Uppercase Letter 22
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6885
24.7%
1 4193
15.1%
2 3021
10.8%
3 2555
 
9.2%
4 2310
 
8.3%
5 2078
 
7.5%
6 1882
 
6.8%
7 1738
 
6.2%
8 1634
 
5.9%
9 1549
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 9334
99.8%
\ 22
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37201
99.9%
Latin 22
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9334
25.1%
0 6885
18.5%
1 4193
11.3%
2 3021
 
8.1%
3 2555
 
6.9%
4 2310
 
6.2%
5 2078
 
5.6%
6 1882
 
5.1%
7 1738
 
4.7%
8 1634
 
4.4%
Other values (2) 1571
 
4.2%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37223
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9334
25.1%
0 6885
18.5%
1 4193
11.3%
2 3021
 
8.1%
3 2555
 
6.9%
4 2310
 
6.2%
5 2078
 
5.6%
6 1882
 
5.1%
7 1738
 
4.7%
8 1634
 
4.4%
Other values (3) 1593
 
4.3%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9186
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5522.0506
Minimum0
Maximum153707.07
Zeros595
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:03:18.455614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11318.0825
median2989.18
Q36741.8375
95-th percentile19376.467
Maximum153707.07
Range153707.07
Interquartile range (IQR)5423.755

Descriptive statistics

Standard deviation7528.1167
Coefficient of variation (CV)1.3632828
Kurtosis42.676665
Mean5522.0506
Median Absolute Deviation (MAD)2086.31
Skewness4.5029779
Sum55220506
Variance56672541
MonotonicityNot monotonic
2024-05-18T14:03:18.973384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 595
 
5.9%
222.39 12
 
0.1%
111.2 7
 
0.1%
333.59 5
 
0.1%
480.0 4
 
< 0.1%
1080.0 4
 
< 0.1%
370.0 4
 
< 0.1%
830.0 4
 
< 0.1%
1270.0 4
 
< 0.1%
555.97 4
 
< 0.1%
Other values (9176) 9357
93.6%
ValueCountFrequency (%)
0.0 595
5.9%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.26 1
 
< 0.1%
0.39 1
 
< 0.1%
10.0 2
 
< 0.1%
20.0 1
 
< 0.1%
30.0 1
 
< 0.1%
88.04 1
 
< 0.1%
88.05 1
 
< 0.1%
ValueCountFrequency (%)
153707.07 1
< 0.1%
122544.55 1
< 0.1%
121956.43 1
< 0.1%
103581.03 1
< 0.1%
92613.76 1
< 0.1%
86050.0 1
< 0.1%
82242.01 1
< 0.1%
82026.46 1
< 0.1%
68370.0 1
< 0.1%
68248.86 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct351
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.0306
Minimum0
Maximum1733
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:03:19.415143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112
median28
Q362
95-th percentile162
Maximum1733
Range1733
Interquartile range (IQR)50

Descriptive statistics

Standard deviation63.026722
Coefficient of variation (CV)1.2854569
Kurtosis72.967311
Mean49.0306
Median Absolute Deviation (MAD)19
Skewness5.1596079
Sum490306
Variance3972.3677
MonotonicityNot monotonic
2024-05-18T14:03:19.943430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 311
 
3.1%
5 284
 
2.8%
7 282
 
2.8%
9 274
 
2.7%
8 250
 
2.5%
10 249
 
2.5%
11 245
 
2.5%
4 221
 
2.2%
13 216
 
2.2%
15 205
 
2.1%
Other values (341) 7463
74.6%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 35
 
0.4%
2 93
 
0.9%
3 189
1.9%
4 221
2.2%
5 284
2.8%
6 311
3.1%
7 282
2.8%
8 250
2.5%
9 274
2.7%
ValueCountFrequency (%)
1733 1
< 0.1%
1117 1
< 0.1%
781 1
< 0.1%
755 1
< 0.1%
714 1
< 0.1%
685 1
< 0.1%
572 1
< 0.1%
552 2
< 0.1%
530 1
< 0.1%
527 1
< 0.1%

Interactions

2024-05-18T14:03:01.208412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:56.687633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:58.110966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:59.555691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:01.486068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:57.056331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:58.469436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:59.980994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:01.767303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:57.414649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:58.794535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:00.427585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:02.070093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:57.761853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:59.169763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:00.767321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:03:20.340401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.5850.0420.1020.1070.1840.0820.057
대여소번호0.5851.0000.0810.0220.0660.1150.0420.060
대여구분코드0.0420.0811.0000.1030.4110.1520.0680.046
성별0.1020.0220.1031.0000.1630.1000.0000.000
연령대코드0.1070.0660.4110.1631.0000.1460.0670.056
이용건수0.1840.1150.1520.1000.1461.0000.7180.784
이동거리(M)0.0820.0420.0680.0000.0670.7181.0000.841
이용시간(분)0.0570.0600.0460.0000.0560.7840.8411.000
2024-05-18T14:03:20.790537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.1000.267
성별0.1001.0000.038
대여구분코드0.2670.0381.000
2024-05-18T14:03:21.235365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.120-0.080-0.1080.0340.0090.031
이용건수-0.1201.0000.6010.6310.0630.0410.070
이동거리(M)-0.0800.6011.0000.8390.0390.0000.033
이용시간(분)-0.1080.6310.8391.0000.0290.0000.030
대여구분코드0.0340.0630.0390.0291.0000.0380.267
성별0.0090.0410.0000.0000.0381.0000.100
연령대코드0.0310.0700.0330.0300.2670.1001.000

Missing values

2024-05-18T14:03:02.658363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:03:03.283880image/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)
149492021-03-0213201320. LG베스트샵 종암점정기\NAGE_0042162.41.124824.7688
123342021-03-0210651065.보훈병원 정문옆(중앙대영약국)정기MAGE_0041000.04
161612021-03-0214681468.먹골역 7번 출구일일(회원)\NAGE_008192.820.542343.9122
194052021-03-0219881988. 고척LIGA아파트 앞정기\NAGE_0044254.222.179355.2475
43012021-03-02234234. 영등포구민체육센터 앞정기\NAGE_003117.610.12523.122
38112021-03-02206206. KBS 앞일일(회원)MAGE_0031205.081.747505.4794
29532021-03-02129129. 신촌역(2호선) 6번출구 옆정기MAGE_002291.090.713088.578
46922021-03-02259259. 대방역6번출구정기MAGE_0045231.471.868063.6171
107222021-03-02841841. 신용산역 1번 출구일일(회원)<NA>AGE_0041000.029
94802021-03-02732732. 신월동 이마트정기\NAGE_004121.790.2873.4829
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
82722021-03-02593593.자양중앙나들목정기<NA>AGE_0033223.162.19044.8244
171382021-03-0216501650. 중계근린공원내정기FAGE_0011295.912.6311321.8186
92592021-03-02708708. 서울출입국관리사무소 앞정기FAGE_0026147.861.426150.4987
12402021-03-0112241224. 아시아지하보도 2번 출구정기MAGE_0021000.05
58492021-03-02358358. 성대입구 사거리정기MAGE_0044350.793.0313048.82126
40622021-03-02221221. 여의도초교 앞일일(회원)\NAGE_0023191.681.898169.65106
130682021-03-0211401140. 목동사거리 버스정류장정기FAGE_0041198.31.295563.963
24142021-03-0132153215.강변삼성래미안정기\NAGE_002155.510.462002.3914
160582021-03-0214551455. 상봉역 2번 출구일일(회원)\NAGE_004116.640.17724.448
52662021-03-02301301. 경복궁역 7번출구 앞정기MAGE_0052229.3128589.4266