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 (61.7%)Imbalance
이동거리(M) has 387 (3.9%) zerosZeros

Reproduction

Analysis started2024-03-13 16:27:32.478445
Analysis finished2024-03-13 16:27:34.869544
Duration2.39 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
2019-12-02
7028 
2019-12-01
2972 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-12-01
2nd row2019-12-02
3rd row2019-12-01
4th row2019-12-01
5th row2019-12-01

Common Values

ValueCountFrequency (%)
2019-12-02 7028
70.3%
2019-12-01 2972
29.7%

Length

2024-03-14T01:27:34.915860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:27:35.000932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-12-02 7028
70.3%
2019-12-01 2972
29.7%

대여소번호
Real number (ℝ)

Distinct1498
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1223.362
Minimum5
Maximum3542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:27:35.142188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile160
Q1522
median1167
Q31823
95-th percentile2508
Maximum3542
Range3537
Interquartile range (IQR)1301

Descriptive statistics

Standard deviation811.57147
Coefficient of variation (CV)0.66339438
Kurtosis-0.11756524
Mean1223.362
Median Absolute Deviation (MAD)649
Skewness0.62108535
Sum12233620
Variance658648.25
MonotonicityNot monotonic
2024-03-14T01:27:35.263391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 25
 
0.2%
1210 21
 
0.2%
1625 18
 
0.2%
1911 18
 
0.2%
1906 17
 
0.2%
2102 17
 
0.2%
346 17
 
0.2%
1308 17
 
0.2%
2219 17
 
0.2%
421 16
 
0.2%
Other values (1488) 9817
98.2%
ValueCountFrequency (%)
5 1
 
< 0.1%
101 7
0.1%
102 13
0.1%
103 14
0.1%
104 10
0.1%
105 8
0.1%
106 16
0.2%
107 9
0.1%
108 9
0.1%
109 10
0.1%
ValueCountFrequency (%)
3542 5
 
0.1%
3541 9
0.1%
3538 1
 
< 0.1%
3537 6
0.1%
3536 6
0.1%
3535 6
0.1%
3534 3
 
< 0.1%
3533 14
0.1%
3532 3
 
< 0.1%
3531 3
 
< 0.1%

대여소
Text

Distinct1498
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:27:35.461128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.4441
Min length8

Characters and Unicode

Total characters154441
Distinct characters518
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

Unique66 ?
Unique (%)0.7%

Sample

1st row1911. 구로디지털단지역 앞
2nd row2410. 포스코피앤에스타워 (역삼역 3번출구 부근)
3rd row3538. 서울숲 IT캐슬
4th row1673. 노원역 5번출구
5th row1139. 용문사 버스정류장
ValueCountFrequency (%)
앞 2642
 
8.3%
옆 527
 
1.7%
출구 428
 
1.4%
1번출구 412
 
1.3%
사거리 318
 
1.0%
2번출구 305
 
1.0%
교차로 278
 
0.9%
뒤 268
 
0.8%
3번출구 253
 
0.8%
4번출구 210
 
0.7%
Other values (3292) 26044
82.2%
2024-03-14T01:27:35.824695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21827
 
14.1%
. 10024
 
6.5%
1 9436
 
6.1%
2 6494
 
4.2%
3 4587
 
3.0%
구 3859
 
2.5%
5 3598
 
2.3%
0 3397
 
2.2%
4 3290
 
2.1%
앞 3198
 
2.1%
Other values (508) 84731
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78705
51.0%
Decimal Number 40655
26.3%
Space Separator 21827
 
14.1%
Other Punctuation 10103
 
6.5%
Uppercase Letter 1291
 
0.8%
Close Punctuation 809
 
0.5%
Open Punctuation 809
 
0.5%
Lowercase Letter 114
 
0.1%
Dash Punctuation 84
 
0.1%
Math Symbol 28
 
< 0.1%
Other values (2) 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3859
 
4.9%
앞 3198
 
4.1%
역 3108
 
3.9%
번 2809
 
3.6%
출 2729
 
3.5%
동 1785
 
2.3%
교 1591
 
2.0%
리 1465
 
1.9%
사 1222
 
1.6%
원 1151
 
1.5%
Other values (452) 55788
70.9%
Uppercase Letter
ValueCountFrequency (%)
S 168
13.0%
K 168
13.0%
C 134
10.4%
G 99
 
7.7%
L 98
 
7.6%
B 72
 
5.6%
M 71
 
5.5%
A 70
 
5.4%
T 66
 
5.1%
I 58
 
4.5%
Other values (14) 287
22.2%
Decimal Number
ValueCountFrequency (%)
1 9436
23.2%
2 6494
16.0%
3 4587
11.3%
5 3598
 
8.9%
0 3397
 
8.4%
4 3290
 
8.1%
6 3114
 
7.7%
7 2354
 
5.8%
9 2212
 
5.4%
8 2173
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
e 46
40.4%
l 12
 
10.5%
n 12
 
10.5%
t 9
 
7.9%
k 7
 
6.1%
y 6
 
5.3%
m 6
 
5.3%
o 6
 
5.3%
c 6
 
5.3%
s 4
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 10024
99.2%
, 61
 
0.6%
& 13
 
0.1%
? 5
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 23
82.1%
+ 5
 
17.9%
Space Separator
ValueCountFrequency (%)
21827
100.0%
Close Punctuation
ValueCountFrequency (%)
) 809
100.0%
Open Punctuation
ValueCountFrequency (%)
( 809
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 11
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78710
51.0%
Common 74326
48.1%
Latin 1405
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3859
 
4.9%
앞 3198
 
4.1%
역 3108
 
3.9%
번 2809
 
3.6%
출 2729
 
3.5%
동 1785
 
2.3%
교 1591
 
2.0%
리 1465
 
1.9%
사 1222
 
1.6%
원 1151
 
1.5%
Other values (453) 55793
70.9%
Latin
ValueCountFrequency (%)
S 168
12.0%
K 168
12.0%
C 134
 
9.5%
G 99
 
7.0%
L 98
 
7.0%
B 72
 
5.1%
M 71
 
5.1%
A 70
 
5.0%
T 66
 
4.7%
I 58
 
4.1%
Other values (24) 401
28.5%
Common
ValueCountFrequency (%)
21827
29.4%
. 10024
13.5%
1 9436
12.7%
2 6494
 
8.7%
3 4587
 
6.2%
5 3598
 
4.8%
0 3397
 
4.6%
4 3290
 
4.4%
6 3114
 
4.2%
7 2354
 
3.2%
Other values (11) 6205
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78705
51.0%
ASCII 75731
49.0%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21827
28.8%
. 10024
13.2%
1 9436
12.5%
2 6494
 
8.6%
3 4587
 
6.1%
5 3598
 
4.8%
0 3397
 
4.5%
4 3290
 
4.3%
6 3114
 
4.1%
7 2354
 
3.1%
Other values (45) 7610
 
10.0%
Hangul
ValueCountFrequency (%)
구 3859
 
4.9%
앞 3198
 
4.1%
역 3108
 
3.9%
번 2809
 
3.6%
출 2729
 
3.5%
동 1785
 
2.3%
교 1591
 
2.0%
리 1465
 
1.9%
사 1222
 
1.6%
원 1151
 
1.5%
Other values (452) 55788
70.9%
None
ValueCountFrequency (%)
㈜ 5
100.0%

대여구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
8286 
일일(회원)
1511 
일일(비회원)
 
162
단체
 
41

Length

Max length7
Median length2
Mean length2.6854
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 8286
82.9%
일일(회원) 1511
 
15.1%
일일(비회원) 162
 
1.6%
단체 41
 
0.4%

Length

2024-03-14T01:27:35.933255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:27:36.018132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 8286
82.9%
일일(회원 1511
 
15.1%
일일(비회원 162
 
1.6%
단체 41
 
0.4%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
4287 
M
2955 
F
1549 
<NA>
1201 
m
 
6

Length

Max length4
Median length2
Mean length1.789
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
\N 4287
42.9%
M 2955
29.5%
F 1549
 
15.5%
<NA> 1201
 
12.0%
m 6
 
0.1%
f 2
 
< 0.1%

Length

2024-03-14T01:27:36.111399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:27:36.196120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 4287
42.9%
m 2961
29.6%
f 1551
 
15.5%
na 1201
 
12.0%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
3385 
AGE_003
2238 
AGE_004
1671 
AGE_005
1258 
AGE_001
585 
Other values (3)
863 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_003
2nd rowAGE_002
3rd rowAGE_002
4th rowAGE_005
5th rowAGE_001

Common Values

ValueCountFrequency (%)
AGE_002 3385
33.9%
AGE_003 2238
22.4%
AGE_004 1671
16.7%
AGE_005 1258
 
12.6%
AGE_001 585
 
5.9%
AGE_006 431
 
4.3%
AGE_008 322
 
3.2%
AGE_007 110
 
1.1%

Length

2024-03-14T01:27:36.281825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:27:36.361820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 3385
33.9%
age_003 2238
22.4%
age_004 1671
16.7%
age_005 1258
 
12.6%
age_001 585
 
5.9%
age_006 431
 
4.3%
age_008 322
 
3.2%
age_007 110
 
1.1%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8845
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:27:36.457821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum53
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9210228
Coefficient of variation (CV)1.0193807
Kurtosis93.446776
Mean1.8845
Median Absolute Deviation (MAD)0
Skewness6.5639469
Sum18845
Variance3.6903288
MonotonicityNot monotonic
2024-03-14T01:27:36.547949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 6173
61.7%
2 1996
 
20.0%
3 776
 
7.8%
4 414
 
4.1%
5 247
 
2.5%
6 131
 
1.3%
7 77
 
0.8%
8 51
 
0.5%
9 37
 
0.4%
11 23
 
0.2%
Other values (18) 75
 
0.8%
ValueCountFrequency (%)
1 6173
61.7%
2 1996
 
20.0%
3 776
 
7.8%
4 414
 
4.1%
5 247
 
2.5%
6 131
 
1.3%
7 77
 
0.8%
8 51
 
0.5%
9 37
 
0.4%
10 18
 
0.2%
ValueCountFrequency (%)
53 1
 
< 0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
28 1
 
< 0.1%
24 2
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 4
< 0.1%

운동량
Text

Distinct6720
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:27:36.803581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.3849
Min length2

Characters and Unicode

Total characters53849
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

Unique5248 ?
Unique (%)52.5%

Sample

1st row8.21
2nd row25.48
3rd row36.31
4th row185.87
5th row0.00
ValueCountFrequency (%)
0.00 358
 
3.6%
n 29
 
0.3%
19.82 14
 
0.1%
16.47 13
 
0.1%
30.89 12
 
0.1%
27.80 11
 
0.1%
37.58 11
 
0.1%
46.33 10
 
0.1%
38.10 10
 
0.1%
25.23 10
 
0.1%
Other values (6710) 9522
95.2%
2024-03-14T01:27:37.165823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9971
18.5%
1 6235
11.6%
2 5124
9.5%
3 4710
8.7%
0 4517
8.4%
4 4300
8.0%
5 4017
7.5%
6 3849
 
7.1%
7 3746
 
7.0%
8 3737
 
6.9%
Other values (3) 3643
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43820
81.4%
Other Punctuation 10000
 
18.6%
Uppercase Letter 29
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6235
14.2%
2 5124
11.7%
3 4710
10.7%
0 4517
10.3%
4 4300
9.8%
5 4017
9.2%
6 3849
8.8%
7 3746
8.5%
8 3737
8.5%
9 3585
8.2%
Other Punctuation
ValueCountFrequency (%)
. 9971
99.7%
\ 29
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53820
99.9%
Latin 29
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9971
18.5%
1 6235
11.6%
2 5124
9.5%
3 4710
8.8%
0 4517
8.4%
4 4300
8.0%
5 4017
7.5%
6 3849
 
7.2%
7 3746
 
7.0%
8 3737
 
6.9%
Other values (2) 3614
 
6.7%
Latin
ValueCountFrequency (%)
N 29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9971
18.5%
1 6235
11.6%
2 5124
9.5%
3 4710
8.7%
0 4517
8.4%
4 4300
8.0%
5 4017
7.5%
6 3849
 
7.1%
7 3746
 
7.0%
8 3737
 
6.9%
Other values (3) 3643
 
6.8%

탄소량
Text

Distinct976
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:27:37.536763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0295
Min length2

Characters and Unicode

Total characters40295
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

Unique475 ?
Unique (%)4.8%

Sample

1st row0.06
2nd row0.27
3rd row0.30
4th row1.46
5th row0.00
ValueCountFrequency (%)
0.00 361
 
3.6%
0.35 129
 
1.3%
0.23 128
 
1.3%
0.29 119
 
1.2%
0.39 113
 
1.1%
0.26 113
 
1.1%
0.30 110
 
1.1%
0.19 109
 
1.1%
0.32 107
 
1.1%
0.34 104
 
1.0%
Other values (966) 8607
86.1%
2024-03-14T01:27:37.964418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9971
24.7%
0 8808
21.9%
1 4264
10.6%
2 3112
 
7.7%
3 2711
 
6.7%
4 2334
 
5.8%
5 2156
 
5.4%
6 1922
 
4.8%
7 1687
 
4.2%
8 1669
 
4.1%
Other values (3) 1661
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30266
75.1%
Other Punctuation 10000
 
24.8%
Uppercase Letter 29
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8808
29.1%
1 4264
14.1%
2 3112
 
10.3%
3 2711
 
9.0%
4 2334
 
7.7%
5 2156
 
7.1%
6 1922
 
6.4%
7 1687
 
5.6%
8 1669
 
5.5%
9 1603
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 9971
99.7%
\ 29
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40266
99.9%
Latin 29
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9971
24.8%
0 8808
21.9%
1 4264
10.6%
2 3112
 
7.7%
3 2711
 
6.7%
4 2334
 
5.8%
5 2156
 
5.4%
6 1922
 
4.8%
7 1687
 
4.2%
8 1669
 
4.1%
Other values (2) 1632
 
4.1%
Latin
ValueCountFrequency (%)
N 29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9971
24.7%
0 8808
21.9%
1 4264
10.6%
2 3112
 
7.7%
3 2711
 
6.7%
4 2334
 
5.8%
5 2156
 
5.4%
6 1922
 
4.8%
7 1687
 
4.2%
8 1669
 
4.1%
Other values (3) 1661
 
4.1%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2066
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7473.651
Minimum0
Maximum262570
Zeros387
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:27:38.296355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile420
Q11370
median2780
Q36080
95-th percentile26712
Maximum262570
Range262570
Interquartile range (IQR)4710

Descriptive statistics

Standard deviation18446.455
Coefficient of variation (CV)2.4681986
Kurtosis65.499524
Mean7473.651
Median Absolute Deviation (MAD)1760
Skewness7.1194817
Sum74736510
Variance3.402717 × 108
MonotonicityNot monotonic
2024-03-14T01:27:38.402285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 387
 
3.9%
1360 37
 
0.4%
980 35
 
0.4%
1310 34
 
0.3%
1090 32
 
0.3%
1340 31
 
0.3%
1300 31
 
0.3%
950 31
 
0.3%
1060 31
 
0.3%
900 30
 
0.3%
Other values (2056) 9321
93.2%
ValueCountFrequency (%)
0 387
3.9%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 1
 
< 0.1%
60 1
 
< 0.1%
70 2
 
< 0.1%
90 1
 
< 0.1%
100 2
 
< 0.1%
110 1
 
< 0.1%
120 1
 
< 0.1%
ValueCountFrequency (%)
262570 1
< 0.1%
257920 1
< 0.1%
257680 1
< 0.1%
252720 1
< 0.1%
247290 1
< 0.1%
241260 1
< 0.1%
239950 1
< 0.1%
237820 1
< 0.1%
235250 1
< 0.1%
228280 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct285
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8065
Minimum0
Maximum836
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:27:38.503966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median20
Q343
95-th percentile115
Maximum836
Range836
Interquartile range (IQR)34

Descriptive statistics

Standard deviation45.102928
Coefficient of variation (CV)1.2958191
Kurtosis39.110604
Mean34.8065
Median Absolute Deviation (MAD)13
Skewness4.4208403
Sum348065
Variance2034.2741
MonotonicityNot monotonic
2024-03-14T01:27:38.603034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 368
 
3.7%
7 367
 
3.7%
6 357
 
3.6%
8 352
 
3.5%
5 346
 
3.5%
10 329
 
3.3%
4 312
 
3.1%
12 310
 
3.1%
11 290
 
2.9%
3 289
 
2.9%
Other values (275) 6680
66.8%
ValueCountFrequency (%)
0 16
 
0.2%
1 40
 
0.4%
2 140
 
1.4%
3 289
2.9%
4 312
3.1%
5 346
3.5%
6 357
3.6%
7 367
3.7%
8 352
3.5%
9 368
3.7%
ValueCountFrequency (%)
836 2
< 0.1%
605 1
< 0.1%
576 1
< 0.1%
556 1
< 0.1%
521 1
< 0.1%
488 1
< 0.1%
456 1
< 0.1%
426 1
< 0.1%
425 1
< 0.1%
413 1
< 0.1%

Interactions

2024-03-14T01:27:34.381275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:33.424171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:33.754650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:34.084062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:34.451843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:33.505393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:33.835967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:34.167107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:34.519194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:33.597405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:33.924201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:34.238384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:34.592412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:33.682069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:34.005540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:27:34.307235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:27:38.670794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.0590.1550.0790.1370.1100.0250.098
대여소번호0.0591.0000.0080.0370.0710.0420.0000.028
대여구분코드0.1550.0081.0000.1060.7720.0650.0000.032
성별0.0790.0370.1061.0000.1980.0590.0000.005
연령대코드0.1370.0710.7720.1981.0000.1230.0420.064
이용건수0.1100.0420.0650.0590.1231.0000.3180.776
이동거리(M)0.0250.0000.0000.0000.0420.3181.0000.304
이용시간(분)0.0980.0280.0320.0050.0640.7760.3041.000
2024-03-14T01:27:38.758112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자성별대여구분코드연령대코드
대여일자1.0000.0970.1030.103
성별0.0971.0000.0870.122
대여구분코드0.1030.0871.0000.438
연령대코드0.1030.1220.4381.000
2024-03-14T01:27:38.832011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여일자대여구분코드성별연령대코드
대여소번호1.000-0.057-0.011-0.0330.0450.0050.0160.034
이용건수-0.0571.0000.5610.5780.0820.0290.0360.041
이동거리(M)-0.0110.5611.0000.7870.0190.0000.0000.020
이용시간(분)-0.0330.5780.7871.0000.0980.0210.0030.031
대여일자0.0450.0820.0190.0981.0000.1030.0970.103
대여구분코드0.0050.0290.0000.0210.1031.0000.0870.438
성별0.0160.0360.0000.0030.0970.0871.0000.122
연령대코드0.0340.0410.0200.0310.1030.4380.1221.000

Missing values

2024-03-14T01:27:34.687452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:27:34.813589image/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)
52992019-12-0119111911. 구로디지털단지역 앞정기\NAGE_00318.210.062801
223002019-12-0224102410. 포스코피앤에스타워 (역삼역 3번출구 부근)정기<NA>AGE_002125.480.27117011
69252019-12-0135383538. 서울숲 IT캐슬정기MAGE_002136.310.3013106
49742019-12-0116731673. 노원역 5번출구정기\NAGE_0053185.871.46631062
32732019-12-0111391139. 용문사 버스정류장정기\NAGE_00110.000.0003
102752019-12-02405405. DMC빌 앞정기\NAGE_0052582.974.451914096
224492019-12-0226082608. 송파구청정기\NAGE_0028398.683.321436091
103412019-12-02410410. 상암중학교 옆정기MAGE_003353.350.4720309
183852019-12-0216671667. 중계중학교정기<NA>AGE_001127.280.2510604
229372019-12-0235153515. 서울숲 관리사무소일일(회원)\NAGE_0041848.039.94428308
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
86532019-12-02247247. 당산역 10번출구 앞정기<NA>AGE_0043124.361.074590121
155472019-12-0212081208. 풍납현대아파트쉼터정기\NAGE_0028432.233.7616180184
137512019-12-02950950. 구산역 2번 출구정기\NAGE_004293.420.68293035
66612019-12-0126212621. 한성백제역 2번 출구정기\NAGE_0022120.421.26543044
141562019-12-0210221022. 길동 사거리(초소앞)정기\NAGE_008162.340.4017309
99402019-12-02368368. SK 서린빌딩 앞정기\NAGE_0024222.001.91820071
164832019-12-0213081308. 안암로터리 버스정류장 앞정기<NA>AGE_006141.980.4017107
107522019-12-02455455. 삼익한의원정기\NAGE_004292.300.793400134
2722019-12-01137137. NH농협 신촌지점 앞정기FAGE_002158.170.52226014
222912019-12-0224092409. 역삼동 디오슈페리움 (우성아파트 사거리)정기FAGE_0051134.621.21523030