Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory986.3 KiB
Average record size in memory101.0 B

Variable types

Categorical4
Text2
Numeric5

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/F/1/datasetView.do

Alerts

'이용건수' is highly overall correlated with '운동량' and 3 other fieldsHigh correlation
'운동량' is highly overall correlated with '이용건수' and 3 other fieldsHigh correlation
'탄소량' is highly overall correlated with '이용건수' and 3 other fieldsHigh correlation
'이동거리(M)' is highly overall correlated with '이용건수' and 3 other fieldsHigh correlation
'이동시간(분)' is highly overall correlated with '이용건수' and 3 other fieldsHigh correlation
'대여구분코드' is highly overall correlated with '성별'High correlation
'성별' is highly overall correlated with '대여구분코드'High correlation
'대여구분코드' is highly imbalanced (50.2%)Imbalance
'운동량' has 209 (2.1%) zerosZeros
'탄소량' has 213 (2.1%) zerosZeros
'이동거리(M)' has 209 (2.1%) zerosZeros

Reproduction

Analysis started2024-03-13 16:23:54.801043
Analysis finished2024-03-13 16:23:57.939156
Duration3.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'2017-08-01'
3832 
'2017-08-02'
3785 
'2017-08-03'
2383 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'2017-08-01'
2nd row'2017-08-03'
3rd row'2017-08-03'
4th row'2017-08-02'
5th row'2017-08-02'

Common Values

ValueCountFrequency (%)
'2017-08-01' 3832
38.3%
'2017-08-02' 3785
37.9%
'2017-08-03' 2383
23.8%

Length

2024-03-14T01:23:57.985640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:23:58.070925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-08-01 3832
38.3%
2017-08-02 3785
37.9%
2017-08-03 2383
23.8%

'대여소번호'
Text

Distinct782
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:23:58.396385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.3359
Min length5

Characters and Unicode

Total characters53359
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row'642'
2nd row'2137'
3rd row'2304'
4th row'240'
5th row'169'
ValueCountFrequency (%)
419 31
 
0.3%
305 29
 
0.3%
907 28
 
0.3%
634 27
 
0.3%
247 26
 
0.3%
816 26
 
0.3%
378 26
 
0.3%
415 26
 
0.3%
321 26
 
0.3%
222 26
 
0.3%
Other values (773) 9731
97.3%
2024-03-14T01:23:58.848459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 20000
37.5%
1 6995
 
13.1%
2 5979
 
11.2%
3 4201
 
7.9%
0 3493
 
6.5%
5 3063
 
5.7%
4 2475
 
4.6%
6 2189
 
4.1%
8 1756
 
3.3%
9 1646
 
3.1%
Other values (9) 1562
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33343
62.5%
Other Punctuation 20000
37.5%
Other Letter 14
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6995
21.0%
2 5979
17.9%
3 4201
12.6%
0 3493
10.5%
5 3063
9.2%
4 2475
 
7.4%
6 2189
 
6.6%
8 1756
 
5.3%
9 1646
 
4.9%
7 1546
 
4.6%
Other Letter
ValueCountFrequency (%)
상 2
14.3%
암 2
14.3%
센 2
14.3%
터 2
14.3%
정 2
14.3%
비 2
14.3%
실 2
14.3%
Other Punctuation
ValueCountFrequency (%)
' 20000
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53345
> 99.9%
Hangul 14
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
' 20000
37.5%
1 6995
 
13.1%
2 5979
 
11.2%
3 4201
 
7.9%
0 3493
 
6.5%
5 3063
 
5.7%
4 2475
 
4.6%
6 2189
 
4.1%
8 1756
 
3.3%
9 1646
 
3.1%
Other values (2) 1548
 
2.9%
Hangul
ValueCountFrequency (%)
상 2
14.3%
암 2
14.3%
센 2
14.3%
터 2
14.3%
정 2
14.3%
비 2
14.3%
실 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53345
> 99.9%
Hangul 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 20000
37.5%
1 6995
 
13.1%
2 5979
 
11.2%
3 4201
 
7.9%
0 3493
 
6.5%
5 3063
 
5.7%
4 2475
 
4.6%
6 2189
 
4.1%
8 1756
 
3.3%
9 1646
 
3.1%
Other values (2) 1548
 
2.9%
Hangul
ValueCountFrequency (%)
상 2
14.3%
암 2
14.3%
센 2
14.3%
터 2
14.3%
정 2
14.3%
비 2
14.3%
실 2
14.3%

'대여소'
Text

Distinct782
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:23:59.088693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length12.8909
Min length6

Characters and Unicode

Total characters128909
Distinct characters445
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row' 신답역 사거리'
2nd row' KT&G 관악지점'
3rd row' 신영 ROYAL PALACE 앞'
4th row' 문래역 4번출구 앞'
5th row' 북가좌 삼거리'
ValueCountFrequency (%)
9849
29.9%
앞 3720
 
11.3%
옆 843
 
2.6%
1번출구 435
 
1.3%
사거리 430
 
1.3%
출구 388
 
1.2%
2번출구 356
 
1.1%
4번출구 322
 
1.0%
뒤 313
 
0.9%
3번출구 227
 
0.7%
Other values (984) 16096
48.8%
2024-03-14T01:23:59.416518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22993
 
17.8%
' 20000
 
15.5%
앞 4133
 
3.2%
구 3999
 
3.1%
역 3342
 
2.6%
출 3028
 
2.3%
번 2999
 
2.3%
동 1701
 
1.3%
리 1435
 
1.1%
교 1422
 
1.1%
Other values (435) 63857
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78185
60.7%
Space Separator 22993
 
17.8%
Other Punctuation 20035
 
15.5%
Decimal Number 4928
 
3.8%
Uppercase Letter 1531
 
1.2%
Close Punctuation 540
 
0.4%
Open Punctuation 540
 
0.4%
Dash Punctuation 81
 
0.1%
Lowercase Letter 63
 
< 0.1%
Connector Punctuation 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 4133
 
5.3%
구 3999
 
5.1%
역 3342
 
4.3%
출 3028
 
3.9%
번 2999
 
3.8%
동 1701
 
2.2%
리 1435
 
1.8%
교 1422
 
1.8%
원 1314
 
1.7%
사 1231
 
1.6%
Other values (387) 53581
68.5%
Uppercase Letter
ValueCountFrequency (%)
C 204
13.3%
K 196
12.8%
S 127
 
8.3%
M 124
 
8.1%
A 100
 
6.5%
T 93
 
6.1%
L 90
 
5.9%
B 81
 
5.3%
D 80
 
5.2%
E 78
 
5.1%
Other values (13) 358
23.4%
Decimal Number
ValueCountFrequency (%)
1 1351
27.4%
2 950
19.3%
3 611
12.4%
4 578
11.7%
5 321
 
6.5%
8 251
 
5.1%
6 242
 
4.9%
7 237
 
4.8%
0 229
 
4.6%
9 158
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
e 18
28.6%
t 9
14.3%
l 9
14.3%
m 9
14.3%
o 9
14.3%
c 9
14.3%
Other Punctuation
ValueCountFrequency (%)
' 20000
99.8%
, 23
 
0.1%
@ 8
 
< 0.1%
& 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
22993
100.0%
Close Punctuation
ValueCountFrequency (%)
) 540
100.0%
Open Punctuation
ValueCountFrequency (%)
( 540
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78185
60.7%
Common 49130
38.1%
Latin 1594
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 4133
 
5.3%
구 3999
 
5.1%
역 3342
 
4.3%
출 3028
 
3.9%
번 2999
 
3.8%
동 1701
 
2.2%
리 1435
 
1.8%
교 1422
 
1.8%
원 1314
 
1.7%
사 1231
 
1.6%
Other values (387) 53581
68.5%
Latin
ValueCountFrequency (%)
C 204
12.8%
K 196
12.3%
S 127
 
8.0%
M 124
 
7.8%
A 100
 
6.3%
T 93
 
5.8%
L 90
 
5.6%
B 81
 
5.1%
D 80
 
5.0%
E 78
 
4.9%
Other values (19) 421
26.4%
Common
ValueCountFrequency (%)
22993
46.8%
' 20000
40.7%
1 1351
 
2.7%
2 950
 
1.9%
3 611
 
1.2%
4 578
 
1.2%
) 540
 
1.1%
( 540
 
1.1%
5 321
 
0.7%
8 251
 
0.5%
Other values (9) 995
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78185
60.7%
ASCII 50724
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22993
45.3%
' 20000
39.4%
1 1351
 
2.7%
2 950
 
1.9%
3 611
 
1.2%
4 578
 
1.1%
) 540
 
1.1%
( 540
 
1.1%
5 321
 
0.6%
8 251
 
0.5%
Other values (38) 2589
 
5.1%
Hangul
ValueCountFrequency (%)
앞 4133
 
5.3%
구 3999
 
5.1%
역 3342
 
4.3%
출 3028
 
3.9%
번 2999
 
3.8%
동 1701
 
2.2%
리 1435
 
1.8%
교 1422
 
1.8%
원 1314
 
1.7%
사 1231
 
1.6%
Other values (387) 53581
68.5%

'대여구분코드'
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'정기'
7609 
'일일(회원)'
1868 
'일일(비회원)'
 
483
'단체'
 
40

Length

Max length9
Median length4
Mean length4.9887
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'정기'
2nd row'정기'
3rd row'정기'
4th row'일일(회원)'
5th row'정기'

Common Values

ValueCountFrequency (%)
'정기' 7609
76.1%
'일일(회원)' 1868
 
18.7%
'일일(비회원)' 483
 
4.8%
'단체' 40
 
0.4%

Length

2024-03-14T01:23:59.519086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:23:59.593947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7609
76.1%
일일(회원 1868
 
18.7%
일일(비회원 483
 
4.8%
단체 40
 
0.4%

'성별'
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'M'
5379 
'F'
4247 
''
 
374

Length

Max length3
Median length3
Mean length2.9626
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'F'
2nd row'F'
3rd row'M'
4th row'M'
5th row'M'

Common Values

ValueCountFrequency (%)
'M' 5379
53.8%
'F' 4247
42.5%
'' 374
 
3.7%

Length

2024-03-14T01:23:59.677898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:23:59.755024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 5379
53.8%
f 4247
42.5%
374
 
3.7%

'연령대코드'
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'20대'
3281 
'30대'
2366 
'40대'
1804 
'50대'
962 
'~10대'
632 
Other values (3)
955 

Length

Max length6
Median length5
Mean length5.0303
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'60대'
2nd row'20대'
3rd row'40대'
4th row'~10대'
5th row'20대'

Common Values

ValueCountFrequency (%)
'20대' 3281
32.8%
'30대' 2366
23.7%
'40대' 1804
18.0%
'50대' 962
 
9.6%
'~10대' 632
 
6.3%
<NA> 479
 
4.8%
'60대' 326
 
3.3%
'70대~' 150
 
1.5%

Length

2024-03-14T01:23:59.840560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:23:59.949972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3281
32.8%
30대 2366
23.7%
40대 1804
18.0%
50대 962
 
9.6%
10대 632
 
6.3%
na 479
 
4.8%
60대 326
 
3.3%
70대 150
 
1.5%

'이용건수'
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9014
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:00.060889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum55
Range54
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.1262846
Coefficient of variation (CV)1.077509
Kurtosis21.423213
Mean2.9014
Median Absolute Deviation (MAD)1
Skewness3.398013
Sum29014
Variance9.7736554
MonotonicityNot monotonic
2024-03-14T01:24:00.164933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 4451
44.5%
2 2035
20.3%
3 1072
 
10.7%
4 678
 
6.8%
5 435
 
4.3%
6 323
 
3.2%
7 249
 
2.5%
8 166
 
1.7%
9 140
 
1.4%
10 93
 
0.9%
Other values (20) 358
 
3.6%
ValueCountFrequency (%)
1 4451
44.5%
2 2035
20.3%
3 1072
 
10.7%
4 678
 
6.8%
5 435
 
4.3%
6 323
 
3.2%
7 249
 
2.5%
8 166
 
1.7%
9 140
 
1.4%
10 93
 
0.9%
ValueCountFrequency (%)
55 1
 
< 0.1%
41 1
 
< 0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
27 4
< 0.1%
23 2
 
< 0.1%
22 3
< 0.1%
21 6
0.1%

'운동량'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8037
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.56549
Minimum0
Maximum12664.62
Zeros209
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:00.266172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.53
Q164.09
median163.16
Q3375.8225
95-th percentile923.484
Maximum12664.62
Range12664.62
Interquartile range (IQR)311.7325

Descriptive statistics

Standard deviation391.67717
Coefficient of variation (CV)1.3715844
Kurtosis144.80886
Mean285.56549
Median Absolute Deviation (MAD)119.34
Skewness7.4734474
Sum2855654.9
Variance153411
MonotonicityNot monotonic
2024-03-14T01:24:00.630448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 209
 
2.1%
25.23 10
 
0.1%
30.89 10
 
0.1%
22.65 9
 
0.1%
52.51 7
 
0.1%
51.99 7
 
0.1%
39.64 7
 
0.1%
38.61 7
 
0.1%
44.79 7
 
0.1%
74.13 7
 
0.1%
Other values (8027) 9720
97.2%
ValueCountFrequency (%)
0.0 209
2.1%
0.26 1
 
< 0.1%
0.51 3
 
< 0.1%
1.03 1
 
< 0.1%
2.04 1
 
< 0.1%
2.08 1
 
< 0.1%
2.83 1
 
< 0.1%
2.85 1
 
< 0.1%
3.86 1
 
< 0.1%
4.06 1
 
< 0.1%
ValueCountFrequency (%)
12664.62 1
< 0.1%
7515.71 1
< 0.1%
6824.49 1
< 0.1%
6223.46 1
< 0.1%
6216.76 1
< 0.1%
6045.3 1
< 0.1%
4505.29 1
< 0.1%
4468.74 1
< 0.1%
4437.18 1
< 0.1%
4268.78 1
< 0.1%

'탄소량'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1123
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.474511
Minimum0
Maximum114.16
Zeros213
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:00.736259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.59
median1.45
Q33.28
95-th percentile7.73
Maximum114.16
Range114.16
Interquartile range (IQR)2.69

Descriptive statistics

Standard deviation3.3184124
Coefficient of variation (CV)1.3410376
Kurtosis164.58858
Mean2.474511
Median Absolute Deviation (MAD)1.05
Skewness7.7453822
Sum24745.11
Variance11.011861
MonotonicityNot monotonic
2024-03-14T01:24:00.856995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 213
 
2.1%
0.29 65
 
0.7%
0.45 63
 
0.6%
0.26 63
 
0.6%
0.36 62
 
0.6%
0.35 62
 
0.6%
0.28 61
 
0.6%
0.31 58
 
0.6%
0.2 56
 
0.6%
0.27 55
 
0.5%
Other values (1113) 9242
92.4%
ValueCountFrequency (%)
0.0 213
2.1%
0.01 2
 
< 0.1%
0.02 1
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 3
 
< 0.1%
0.06 6
 
0.1%
0.07 3
 
< 0.1%
0.08 12
 
0.1%
0.09 14
 
0.1%
ValueCountFrequency (%)
114.16 1
< 0.1%
54.78 1
< 0.1%
54.49 1
< 0.1%
52.03 1
< 0.1%
48.33 1
< 0.1%
47.74 1
< 0.1%
46.89 1
< 0.1%
37.59 1
< 0.1%
35.86 1
< 0.1%
31.58 1
< 0.1%

'이동거리(M)'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2972
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10665.944
Minimum0
Maximum492020
Zeros209
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:00.965189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile739.5
Q12530
median6235
Q314140
95-th percentile33320
Maximum492020
Range492020
Interquartile range (IQR)11610

Descriptive statistics

Standard deviation14302.878
Coefficient of variation (CV)1.3409857
Kurtosis164.55554
Mean10665.944
Median Absolute Deviation (MAD)4525
Skewness7.7445137
Sum1.0665944 × 108
Variance2.0457232 × 108
MonotonicityNot monotonic
2024-03-14T01:24:01.073623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 209
 
2.1%
1210 20
 
0.2%
1540 20
 
0.2%
960 19
 
0.2%
1340 19
 
0.2%
880 19
 
0.2%
1330 19
 
0.2%
1170 18
 
0.2%
1570 17
 
0.2%
1740 17
 
0.2%
Other values (2962) 9623
96.2%
ValueCountFrequency (%)
0 209
2.1%
10 1
 
< 0.1%
20 3
 
< 0.1%
40 1
 
< 0.1%
50 1
 
< 0.1%
70 1
 
< 0.1%
110 1
 
< 0.1%
130 1
 
< 0.1%
160 1
 
< 0.1%
190 2
 
< 0.1%
ValueCountFrequency (%)
492020 1
< 0.1%
236130 1
< 0.1%
234860 1
< 0.1%
224270 1
< 0.1%
208300 1
< 0.1%
205660 1
< 0.1%
202140 1
< 0.1%
162000 1
< 0.1%
154610 1
< 0.1%
136110 1
< 0.1%

'이동시간(분)'
Real number (ℝ)

HIGH CORRELATION 

Distinct464
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.2642
Minimum0
Maximum4338
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:01.230746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q119
median46
Q399
95-th percentile232
Maximum4338
Range4338
Interquartile range (IQR)80

Descriptive statistics

Standard deviation96.372701
Coefficient of variation (CV)1.2977007
Kurtosis397.07776
Mean74.2642
Median Absolute Deviation (MAD)32.5
Skewness11.13648
Sum742642
Variance9287.6976
MonotonicityNot monotonic
2024-03-14T01:24:01.350654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 197
 
2.0%
6 187
 
1.9%
7 178
 
1.8%
13 160
 
1.6%
10 159
 
1.6%
16 154
 
1.5%
12 147
 
1.5%
9 145
 
1.5%
11 143
 
1.4%
14 140
 
1.4%
Other values (454) 8390
83.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 13
 
0.1%
2 74
 
0.7%
3 105
1.1%
4 133
1.3%
5 137
1.4%
6 187
1.9%
7 178
1.8%
8 197
2.0%
9 145
1.5%
ValueCountFrequency (%)
4338 1
< 0.1%
1321 1
< 0.1%
1269 1
< 0.1%
1080 1
< 0.1%
1047 1
< 0.1%
1027 1
< 0.1%
843 1
< 0.1%
841 1
< 0.1%
827 1
< 0.1%
826 1
< 0.1%

Interactions

2024-03-14T01:23:57.384852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:55.833371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.194192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.605166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.021378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.452699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:55.903123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.275396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.681132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.093521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.522119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:55.981418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.380445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.778393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.169103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.591089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.050675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.459536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.878932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.243878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.658229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.121609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.535310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:56.955649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:57.316080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:24:01.423931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여일자''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
'대여일자'1.0000.2330.2510.1040.1110.0000.0170.0170.014
'대여구분코드'0.2331.0000.6040.1700.1360.0260.0400.0400.053
'성별'0.2510.6041.0000.0920.2160.0870.1040.1040.077
'연령대코드'0.1040.1700.0921.0000.1730.0690.0730.0730.066
'이용건수'0.1110.1360.2160.1731.0000.6910.7530.7530.745
'운동량'0.0000.0260.0870.0690.6911.0000.9120.9120.718
'탄소량'0.0170.0400.1040.0730.7530.9121.0001.0000.706
'이동거리(M)'0.0170.0400.1040.0730.7530.9121.0001.0000.706
'이동시간(분)'0.0140.0530.0770.0660.7450.7180.7060.7061.000
2024-03-14T01:24:01.517074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여일자''연령대코드''대여구분코드''성별'
'대여일자'1.0000.0700.2220.081
'연령대코드'0.0701.0000.1170.099
'대여구분코드'0.2220.1171.0000.619
'성별'0.0810.0990.6191.000
2024-03-14T01:24:01.595561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'이용건수''운동량''탄소량''이동거리(M)''이동시간(분)''대여일자''대여구분코드''성별''연령대코드'
'이용건수'1.0000.6750.6780.6780.6860.0490.0870.0960.093
'운동량'0.6751.0000.9900.9900.8790.0000.0180.0580.041
'탄소량'0.6780.9901.0001.0000.8930.0070.0260.0430.047
'이동거리(M)'0.6780.9901.0001.0000.8930.0070.0260.0430.047
'이동시간(분)'0.6860.8790.8930.8931.0000.0110.0430.0570.044
'대여일자'0.0490.0000.0070.0070.0111.0000.2220.0810.070
'대여구분코드'0.0870.0180.0260.0260.0430.2221.0000.6190.117
'성별'0.0960.0580.0430.0430.0570.0810.6191.0000.099
'연령대코드'0.0930.0410.0470.0470.0440.0700.1170.0991.000

Missing values

2024-03-14T01:23:57.752813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:23:57.881495image/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

'대여일자''대여소번호''대여소''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
1836'2017-08-01''642'' 신답역 사거리''정기''F''60대'121.780.26110018
13640'2017-08-03''2137'' KT&G 관악지점''정기''F''20대'139.010.46197026
16875'2017-08-03''2304'' 신영 ROYAL PALACE 앞''정기''M''40대'1379.213.171368076
11712'2017-08-02''240'' 문래역 4번출구 앞''일일(회원)''M''~10대'281.220.68293045
8792'2017-08-02''169'' 북가좌 삼거리''정기''M''20대'137.840.34147012
11720'2017-08-02''186'' 월드컵공원''일일(회원)''M''~10대'1333.12.871237080
9319'2017-08-02''414'' 상암동주민센터 옆''정기''M''20대'247.120.45194010
2564'2017-08-01''1823'' 상신정비공업 앞''정기''M''20대'2385.653.1613630103
14489'2017-08-03''1809'' LG전자 별관동(호서대 벤처타워 맞은편)''정기''F''40대'148.060.38164021
15615'2017-08-03''1214'' 오금역 7번 출구 인근''정기''M''20대'2165.81.47633076
'대여일자''대여소번호''대여소''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
2142'2017-08-01''341'' 혜화역 3번출구 뒤''정기''M''20대'8270.162.361014057
2059'2017-08-01''2219'' 고속터미널역 8-1번, 8-2번 출구 사이''정기''M''~10대'158.690.5322809
4663'2017-08-01''600'' 휘경2동 주민센터''일일(회원)''F''20대'2265.442.731179080
13192'2017-08-03''359'' 원남동사거리''정기''F''20대'240.870.52225016
16637'2017-08-03''260'' 여의도 마리나선착장 앞''정기''M''40대'133.260.2611204
10942'2017-08-02''267'' 삼성화재 사옥 옆''정기''M''60대'181.980.72309021
12053'2017-08-02''1617'' 하계동 중평어린이공원 앞''일일(회원)''M''20대'164.630.59255010
9041'2017-08-02''1002'' 해공공원(천호동)''정기''M''20대'198.330.89382019
1212'2017-08-01''407'' 마포구 육아종합지원센터''정기''F''30대'2345.273.0313060195
15722'2017-08-03''2113'' 관악동작견인차량보관소''정기''M''20대'3200.11.75752072