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

대여일자 has constant value ""Constant
대여구분코드 has constant value ""Constant
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
이동거리(M) has 183 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-13 16:28:23.315247
Analysis finished2024-03-13 16:28:25.673577
Duration2.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-01-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-01
2nd row2023-01-01
3rd row2023-01-01
4th row2023-01-01
5th row2023-01-01

Common Values

ValueCountFrequency (%)
2023-01-01 10000
100.0%

Length

2024-03-14T01:28:25.724023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:28:25.795094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-01 10000
100.0%

대여소번호
Real number (ℝ)

Distinct2357
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2105.2029
Minimum102
Maximum6053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:28:26.142512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile258
Q1838
median1669
Q33510.25
95-th percentile4812
Maximum6053
Range5951
Interquartile range (IQR)2672.25

Descriptive statistics

Standard deviation1496.6431
Coefficient of variation (CV)0.71092582
Kurtosis-0.8603914
Mean2105.2029
Median Absolute Deviation (MAD)1023
Skewness0.59608559
Sum21052029
Variance2239940.6
MonotonicityNot monotonic
2024-03-14T01:28:26.250405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2715 20
 
0.2%
4027 19
 
0.2%
1044 18
 
0.2%
1153 17
 
0.2%
2701 17
 
0.2%
5052 17
 
0.2%
1721 16
 
0.2%
3501 15
 
0.1%
1152 15
 
0.1%
1150 15
 
0.1%
Other values (2347) 9831
98.3%
ValueCountFrequency (%)
102 6
0.1%
103 5
0.1%
104 5
0.1%
105 1
 
< 0.1%
106 6
0.1%
107 4
< 0.1%
108 3
< 0.1%
109 4
< 0.1%
111 2
 
< 0.1%
112 5
0.1%
ValueCountFrequency (%)
6053 1
 
< 0.1%
5862 5
0.1%
5861 3
 
< 0.1%
5860 2
 
< 0.1%
5859 3
 
< 0.1%
5858 10
0.1%
5857 5
0.1%
5855 1
 
< 0.1%
5854 6
0.1%
5853 3
 
< 0.1%

대여소
Text

Distinct2357
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:28:26.469500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length15.5985
Min length7

Characters and Unicode

Total characters155985
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

Unique381 ?
Unique (%)3.8%

Sample

1st row723. SBS방송국
2nd row1375. 생명의 전화 종합복지관 앞 교차로
3rd row828. 숙대입구역 8번
4th row648. 장안동위더스빌옆
5th row2027. 중앙대학교 정문 1
ValueCountFrequency (%)
앞 2541
 
8.7%
출구 463
 
1.6%
옆 374
 
1.3%
1번출구 315
 
1.1%
교차로 247
 
0.8%
사거리 215
 
0.7%
뒤 210
 
0.7%
3번출구 209
 
0.7%
2번출구 196
 
0.7%
입구 173
 
0.6%
Other values (4739) 24309
83.1%
2024-03-14T01:28:26.813764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19458
 
12.5%
. 10015
 
6.4%
1 8315
 
5.3%
2 5687
 
3.6%
4 4664
 
3.0%
3 4615
 
3.0%
5 3884
 
2.5%
0 3678
 
2.4%
6 3469
 
2.2%
7 3358
 
2.2%
Other values (555) 88842
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80500
51.6%
Decimal Number 42900
27.5%
Space Separator 19458
 
12.5%
Other Punctuation 10149
 
6.5%
Uppercase Letter 1124
 
0.7%
Close Punctuation 809
 
0.5%
Open Punctuation 809
 
0.5%
Lowercase Letter 124
 
0.1%
Dash Punctuation 79
 
0.1%
Connector Punctuation 18
 
< 0.1%
Other values (3) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3336
 
4.1%
앞 2965
 
3.7%
역 2659
 
3.3%
번 2357
 
2.9%
출 2307
 
2.9%
동 2282
 
2.8%
교 1570
 
2.0%
파 1511
 
1.9%
아 1480
 
1.8%
트 1404
 
1.7%
Other values (493) 58629
72.8%
Uppercase Letter
ValueCountFrequency (%)
S 131
11.7%
K 111
9.9%
C 109
9.7%
T 107
9.5%
A 104
9.3%
B 72
 
6.4%
G 69
 
6.1%
D 67
 
6.0%
M 62
 
5.5%
P 58
 
5.2%
Other values (14) 234
20.8%
Lowercase Letter
ValueCountFrequency (%)
e 50
40.3%
s 19
 
15.3%
k 17
 
13.7%
n 8
 
6.5%
v 5
 
4.0%
y 4
 
3.2%
l 4
 
3.2%
r 4
 
3.2%
f 4
 
3.2%
h 4
 
3.2%
Other values (3) 5
 
4.0%
Decimal Number
ValueCountFrequency (%)
1 8315
19.4%
2 5687
13.3%
4 4664
10.9%
3 4615
10.8%
5 3884
9.1%
0 3678
8.6%
6 3469
8.1%
7 3358
7.8%
8 2719
 
6.3%
9 2511
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 10015
98.7%
, 95
 
0.9%
& 18
 
0.2%
? 12
 
0.1%
· 9
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 4
50.0%
+ 4
50.0%
Other Number
ValueCountFrequency (%)
① 3
60.0%
② 2
40.0%
Space Separator
ValueCountFrequency (%)
19458
100.0%
Close Punctuation
ValueCountFrequency (%)
) 809
100.0%
Open Punctuation
ValueCountFrequency (%)
( 809
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 18
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80502
51.6%
Common 74235
47.6%
Latin 1248
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3336
 
4.1%
앞 2965
 
3.7%
역 2659
 
3.3%
번 2357
 
2.9%
출 2307
 
2.9%
동 2282
 
2.8%
교 1570
 
2.0%
파 1511
 
1.9%
아 1480
 
1.8%
트 1404
 
1.7%
Other values (494) 58631
72.8%
Latin
ValueCountFrequency (%)
S 131
 
10.5%
K 111
 
8.9%
C 109
 
8.7%
T 107
 
8.6%
A 104
 
8.3%
B 72
 
5.8%
G 69
 
5.5%
D 67
 
5.4%
M 62
 
5.0%
P 58
 
4.6%
Other values (27) 358
28.7%
Common
ValueCountFrequency (%)
19458
26.2%
. 10015
13.5%
1 8315
11.2%
2 5687
 
7.7%
4 4664
 
6.3%
3 4615
 
6.2%
5 3884
 
5.2%
0 3678
 
5.0%
6 3469
 
4.7%
7 3358
 
4.5%
Other values (14) 7092
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80500
51.6%
ASCII 75469
48.4%
None 11
 
< 0.1%
Enclosed Alphanum 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19458
25.8%
. 10015
13.3%
1 8315
11.0%
2 5687
 
7.5%
4 4664
 
6.2%
3 4615
 
6.1%
5 3884
 
5.1%
0 3678
 
4.9%
6 3469
 
4.6%
7 3358
 
4.4%
Other values (48) 8326
11.0%
Hangul
ValueCountFrequency (%)
구 3336
 
4.1%
앞 2965
 
3.7%
역 2659
 
3.3%
번 2357
 
2.9%
출 2307
 
2.9%
동 2282
 
2.8%
교 1570
 
2.0%
파 1511
 
1.9%
아 1480
 
1.8%
트 1404
 
1.7%
Other values (493) 58629
72.8%
None
ValueCountFrequency (%)
· 9
81.8%
㈜ 2
 
18.2%
Enclosed Alphanum
ValueCountFrequency (%)
① 3
60.0%
② 2
40.0%

대여구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기권
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기권 10000
100.0%

Length

2024-03-14T01:28:26.918452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:28:27.002142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기권 10000
100.0%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3670 
M
3590 
F
2736 
m
 
3
f
 
1

Length

Max length4
Median length1
Mean length2.101
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3670
36.7%
M 3590
35.9%
F 2736
27.4%
m 3
 
< 0.1%
f 1
 
< 0.1%

Length

2024-03-14T01:28:27.108979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:28:27.203665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3670
36.7%
m 3593
35.9%
f 2737
27.4%

연령대
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
3373 
30대
2559 
40대
1621 
50대
850 
~10대
716 
Other values (3)
881 

Length

Max length5
Median length3
Mean length3.026
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20대 3373
33.7%
30대 2559
25.6%
40대 1621
16.2%
50대 850
 
8.5%
~10대 716
 
7.2%
기타 550
 
5.5%
60대 284
 
2.8%
70대이상 47
 
0.5%

Length

2024-03-14T01:28:27.291672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:28:27.376600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3373
33.7%
30대 2559
25.6%
40대 1621
16.2%
50대 850
 
8.5%
10대 716
 
7.2%
기타 550
 
5.5%
60대 284
 
2.8%
70대이상 47
 
0.5%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5639
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:28:27.460134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum17
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.145272
Coefficient of variation (CV)0.73231792
Kurtosis19.95401
Mean1.5639
Median Absolute Deviation (MAD)0
Skewness3.5459437
Sum15639
Variance1.311648
MonotonicityNot monotonic
2024-03-14T01:28:27.540151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 6876
68.8%
2 1862
 
18.6%
3 671
 
6.7%
4 295
 
2.9%
5 140
 
1.4%
6 75
 
0.8%
7 29
 
0.3%
8 19
 
0.2%
9 16
 
0.2%
10 8
 
0.1%
Other values (5) 9
 
0.1%
ValueCountFrequency (%)
1 6876
68.8%
2 1862
 
18.6%
3 671
 
6.7%
4 295
 
2.9%
5 140
 
1.4%
6 75
 
0.8%
7 29
 
0.3%
8 19
 
0.2%
9 16
 
0.2%
10 8
 
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
12 2
 
< 0.1%
11 3
 
< 0.1%
10 8
 
0.1%
9 16
 
0.2%
8 19
 
0.2%
7 29
 
0.3%
6 75
0.8%

운동량
Text

Distinct6562
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:28:27.831269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1527
Min length2

Characters and Unicode

Total characters51527
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

Unique4599 ?
Unique (%)46.0%

Sample

1st row26.81
2nd row656.48
3rd row188.21
4th row93.41
5th row26.03
ValueCountFrequency (%)
0.00 187
 
1.9%
n 15
 
0.1%
23.17 13
 
0.1%
19.56 13
 
0.1%
17.25 12
 
0.1%
20.59 11
 
0.1%
15.70 11
 
0.1%
37.07 10
 
0.1%
16.47 10
 
0.1%
27.28 10
 
0.1%
Other values (6552) 9708
97.1%
2024-03-14T01:28:28.288783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9985
19.4%
1 6010
11.7%
2 5003
9.7%
3 4534
8.8%
4 4089
7.9%
5 3891
 
7.6%
0 3838
 
7.4%
6 3706
 
7.2%
7 3554
 
6.9%
8 3493
 
6.8%
Other values (3) 3424
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41512
80.6%
Other Punctuation 10000
 
19.4%
Uppercase Letter 15
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6010
14.5%
2 5003
12.1%
3 4534
10.9%
4 4089
9.9%
5 3891
9.4%
0 3838
9.2%
6 3706
8.9%
7 3554
8.6%
8 3493
8.4%
9 3394
8.2%
Other Punctuation
ValueCountFrequency (%)
. 9985
99.9%
\ 15
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51512
> 99.9%
Latin 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9985
19.4%
1 6010
11.7%
2 5003
9.7%
3 4534
8.8%
4 4089
7.9%
5 3891
 
7.6%
0 3838
 
7.5%
6 3706
 
7.2%
7 3554
 
6.9%
8 3493
 
6.8%
Other values (2) 3409
 
6.6%
Latin
ValueCountFrequency (%)
N 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9985
19.4%
1 6010
11.7%
2 5003
9.7%
3 4534
8.8%
4 4089
7.9%
5 3891
 
7.6%
0 3838
 
7.4%
6 3706
 
7.2%
7 3554
 
6.9%
8 3493
 
6.8%
Other values (3) 3424
 
6.6%

탄소량
Text

Distinct397
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:28:28.651147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.997
Min length2

Characters and Unicode

Total characters39970
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

Unique102 ?
Unique (%)1.0%

Sample

1st row0.18
2nd row4.93
3rd row1.70
4th row0.77
5th row0.19
ValueCountFrequency (%)
0.23 190
 
1.9%
0.19 188
 
1.9%
0.00 180
 
1.8%
0.16 178
 
1.8%
0.20 176
 
1.8%
0.21 173
 
1.7%
0.14 166
 
1.7%
0.30 162
 
1.6%
0.22 162
 
1.6%
0.18 161
 
1.6%
Other values (387) 8264
82.6%
2024-03-14T01:28:29.096062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10306
25.8%
. 9985
25.0%
1 4141
10.4%
2 3051
 
7.6%
3 2552
 
6.4%
4 2096
 
5.2%
5 1852
 
4.6%
6 1678
 
4.2%
7 1456
 
3.6%
8 1444
 
3.6%
Other values (3) 1409
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29955
74.9%
Other Punctuation 10000
 
25.0%
Uppercase Letter 15
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10306
34.4%
1 4141
13.8%
2 3051
 
10.2%
3 2552
 
8.5%
4 2096
 
7.0%
5 1852
 
6.2%
6 1678
 
5.6%
7 1456
 
4.9%
8 1444
 
4.8%
9 1379
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 9985
99.9%
\ 15
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39955
> 99.9%
Latin 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10306
25.8%
. 9985
25.0%
1 4141
10.4%
2 3051
 
7.6%
3 2552
 
6.4%
4 2096
 
5.2%
5 1852
 
4.6%
6 1678
 
4.2%
7 1456
 
3.6%
8 1444
 
3.6%
Other values (2) 1394
 
3.5%
Latin
ValueCountFrequency (%)
N 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10306
25.8%
. 9985
25.0%
1 4141
10.4%
2 3051
 
7.6%
3 2552
 
6.4%
4 2096
 
5.2%
5 1852
 
4.6%
6 1678
 
4.2%
7 1456
 
3.6%
8 1444
 
3.6%
Other values (3) 1409
 
3.5%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6726
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2673.9201
Minimum0
Maximum28802.95
Zeros183
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:28:29.222257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile397.945
Q1960
median1771.455
Q33310.285
95-th percentile7987.6335
Maximum28802.95
Range28802.95
Interquartile range (IQR)2350.285

Descriptive statistics

Standard deviation2812.6029
Coefficient of variation (CV)1.051865
Kurtosis12.896353
Mean2673.9201
Median Absolute Deviation (MAD)973.595
Skewness2.9312294
Sum26739201
Variance7910734.9
MonotonicityNot monotonic
2024-03-14T01:28:29.323286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 183
 
1.8%
900.0 26
 
0.3%
670.0 25
 
0.2%
850.0 25
 
0.2%
800.0 24
 
0.2%
1000.0 23
 
0.2%
1140.0 23
 
0.2%
890.0 23
 
0.2%
1320.0 23
 
0.2%
860.0 23
 
0.2%
Other values (6716) 9602
96.0%
ValueCountFrequency (%)
0.0 183
1.8%
0.1 1
 
< 0.1%
0.13 3
 
< 0.1%
0.2 1
 
< 0.1%
10.0 2
 
< 0.1%
20.0 4
 
< 0.1%
20.72 1
 
< 0.1%
22.19 1
 
< 0.1%
23.63 1
 
< 0.1%
37.45 1
 
< 0.1%
ValueCountFrequency (%)
28802.95 1
< 0.1%
28060.0 1
< 0.1%
27080.83 1
< 0.1%
25383.99 1
< 0.1%
24461.03 1
< 0.1%
24316.09 1
< 0.1%
24170.13 1
< 0.1%
23986.19 1
< 0.1%
23724.74 1
< 0.1%
23278.4 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.3303
Minimum0
Maximum696
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:28:29.424151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median13
Q327
95-th percentile64
Maximum696
Range696
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.54899
Coefficient of variation (CV)1.1977792
Kurtosis105.44383
Mean21.3303
Median Absolute Deviation (MAD)8
Skewness6.327818
Sum213303
Variance652.75088
MonotonicityNot monotonic
2024-03-14T01:28:29.532016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 542
 
5.4%
4 523
 
5.2%
6 516
 
5.2%
7 474
 
4.7%
3 445
 
4.5%
9 436
 
4.4%
8 430
 
4.3%
12 362
 
3.6%
10 356
 
3.6%
11 355
 
3.5%
Other values (164) 5561
55.6%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 100
 
1.0%
2 267
2.7%
3 445
4.5%
4 523
5.2%
5 542
5.4%
6 516
5.2%
7 474
4.7%
8 430
4.3%
9 436
4.4%
ValueCountFrequency (%)
696 1
< 0.1%
647 1
< 0.1%
431 1
< 0.1%
331 1
< 0.1%
329 1
< 0.1%
324 1
< 0.1%
281 1
< 0.1%
264 1
< 0.1%
244 1
< 0.1%
231 1
< 0.1%

Interactions

2024-03-14T01:28:25.115546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.194491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.502262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.812157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:25.188936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.263564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.572772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.886651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:25.281826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.353857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.652357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.966004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:25.360082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.426623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:24.734370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:28:25.039624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:28:29.622021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호성별연령대이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0180.0680.0670.0120.000
성별0.0181.0000.4630.1540.0620.000
연령대0.0680.4631.0000.1650.0820.062
이용건수0.0670.1540.1651.0000.5650.467
이동거리(M)0.0120.0620.0820.5651.0000.527
이용시간(분)0.0000.0000.0620.4670.5271.000
2024-03-14T01:28:29.707901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별연령대
성별1.0000.221
연령대0.2211.000
2024-03-14T01:28:29.783720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)성별연령대
대여소번호1.000-0.022-0.034-0.0350.0110.032
이용건수-0.0221.0000.5600.5490.0700.081
이동거리(M)-0.0340.5601.0000.8620.0370.039
이용시간(분)-0.0350.5490.8621.0000.0000.033
성별0.0110.0700.0370.0001.0000.221
연령대0.0320.0810.0390.0330.2211.000

Missing values

2024-03-14T01:28:25.464664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:28:25.595339image/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

대여일자대여소번호대여소대여구분코드성별연령대이용건수운동량탄소량이동거리(M)이용시간(분)
89702023-01-01723723. SBS방송국정기권M20대126.810.18760.666
17972023-01-0113751375. 생명의 전화 종합복지관 앞 교차로정기권<NA>30대1656.484.9321253.4160
52072023-01-01828828. 숙대입구역 8번정기권F30대1188.211.707311.9660
77422023-01-01648648. 장안동위더스빌옆정기권M20대293.410.773329.2223
24012023-01-0120272027. 중앙대학교 정문 1정기권<NA>30대126.030.19821.6714
29462023-01-01585585. 성수2가1동 공영주차장 인근정기권<NA>40대297.580.783374.420
72262023-01-0110901090.상일동역 2번출구 앞정기권M~10대137.950.311331.0911
11672023-01-01709709. 신정3동 현장민원실 앞정기권<NA>20대179.440.602572.0224
56172023-01-0141794179. 보라매병원역 1번출구정기권F30대195.520.863710.9249
6112023-01-01781781.신정보도육교 아래정기권<NA>20대7268.742.219538.0250
대여일자대여소번호대여소대여구분코드성별연령대이용건수운동량탄소량이동거리(M)이용시간(분)
71862023-01-01660660. 동의보감타워정기권M~10대138.330.261100.07
85932023-01-0149514951. 강동농협 버스정류소 앞(광채빌라)정기권M20대123.350.20880.04
86902023-01-01912912. 응암오거리정기권M20대390.280.803469.1628
105732023-01-0141214121. 서울문화재단 앞정기권M40대2143.700.954120.027
45392023-01-01785785.양천구청, 보건소 사잇길정기권F20대258.430.602588.8917
110162023-01-0112711271. 송파도서관정기권M50대128.710.281208.487
6572023-01-0129202920.상계중학교(당현2교사거리)정기권<NA>20대2144.421.044468.9424
107922023-01-01574574. 아차산역4번출구정기권M40대148.120.351518.798
75022023-01-0110111011. LIGA 아파트 앞정기권M~10대126.270.241020.7437
78562023-01-01340340. 혜화동 로터리정기권M20대226.100.21901.9350