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

DateTime1
Text2
Categorical3
Numeric5

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/A/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
'운동량' is highly skewed (γ1 = 74.76879789)Skewed
'운동량' has 192 (1.9%) zerosZeros
'탄소량' has 198 (2.0%) zerosZeros
'이동거리(M)' has 192 (1.9%) zerosZeros

Reproduction

Analysis started2024-05-04 03:16:47.786410
Analysis finished2024-05-04 03:16:56.412040
Duration8.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Date

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-08-01 00:00:00
Maximum2017-08-03 00:00:00
2024-05-04T03:16:56.491189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:56.669502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

'대여소번호'
Text

Distinct781
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T03:16:57.303669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.336
Min length5

Characters and Unicode

Total characters53360
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

Unique12 ?
Unique (%)0.1%

Sample

1st row'1525'
2nd row'587'
3rd row'328'
4th row'379'
5th row'610'
ValueCountFrequency (%)
907 34
 
0.3%
207 33
 
0.3%
502 33
 
0.3%
906 27
 
0.3%
194 27
 
0.3%
419 26
 
0.3%
565 26
 
0.3%
259 26
 
0.3%
525 26
 
0.3%
361 26
 
0.3%
Other values (772) 9718
97.2%
2024-05-04T03:16:58.206298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 20000
37.5%
1 6983
 
13.1%
2 5883
 
11.0%
3 4152
 
7.8%
0 3561
 
6.7%
5 3082
 
5.8%
4 2543
 
4.8%
6 2185
 
4.1%
8 1730
 
3.2%
9 1649
 
3.1%
Other values (9) 1592
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33344
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 6983
20.9%
2 5883
17.6%
3 4152
12.5%
0 3561
10.7%
5 3082
9.2%
4 2543
 
7.6%
6 2185
 
6.6%
8 1730
 
5.2%
9 1649
 
4.9%
7 1576
 
4.7%
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 53346
> 99.9%
Hangul 14
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
' 20000
37.5%
1 6983
 
13.1%
2 5883
 
11.0%
3 4152
 
7.8%
0 3561
 
6.7%
5 3082
 
5.8%
4 2543
 
4.8%
6 2185
 
4.1%
8 1730
 
3.2%
9 1649
 
3.1%
Other values (2) 1578
 
3.0%
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 53346
> 99.9%
Hangul 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 20000
37.5%
1 6983
 
13.1%
2 5883
 
11.0%
3 4152
 
7.8%
0 3561
 
6.7%
5 3082
 
5.8%
4 2543
 
4.8%
6 2185
 
4.1%
8 1730
 
3.2%
9 1649
 
3.1%
Other values (2) 1578
 
3.0%
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

Distinct781
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T03:16:58.603555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length12.8994
Min length6

Characters and Unicode

Total characters128994
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

Unique12 ?
Unique (%)0.1%

Sample

1st row' 미아동 복합청사'
2nd row' KEB하나은행 성수중앙지점'
3rd row' 탑골공원 앞'
4th row' 서울역9번출구'
5th row' 동대문중 교차로'
ValueCountFrequency (%)
9871
29.8%
앞 3723
 
11.3%
옆 839
 
2.5%
1번출구 470
 
1.4%
사거리 420
 
1.3%
출구 398
 
1.2%
2번출구 360
 
1.1%
4번출구 322
 
1.0%
뒤 317
 
1.0%
3번출구 224
 
0.7%
Other values (983) 16137
48.8%
2024-05-04T03:16:59.535850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23095
 
17.9%
' 20000
 
15.5%
앞 4099
 
3.2%
구 4004
 
3.1%
역 3335
 
2.6%
출 3027
 
2.3%
번 3026
 
2.3%
동 1692
 
1.3%
리 1463
 
1.1%
원 1379
 
1.1%
Other values (435) 63874
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78157
60.6%
Space Separator 23095
 
17.9%
Other Punctuation 20045
 
15.5%
Decimal Number 4933
 
3.8%
Uppercase Letter 1573
 
1.2%
Open Punctuation 510
 
0.4%
Close Punctuation 510
 
0.4%
Dash Punctuation 90
 
0.1%
Lowercase Letter 63
 
< 0.1%
Connector Punctuation 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 4099
 
5.2%
구 4004
 
5.1%
역 3335
 
4.3%
출 3027
 
3.9%
번 3026
 
3.9%
동 1692
 
2.2%
리 1463
 
1.9%
원 1379
 
1.8%
교 1367
 
1.7%
사 1228
 
1.6%
Other values (387) 53537
68.5%
Uppercase Letter
ValueCountFrequency (%)
C 213
13.5%
K 191
12.1%
M 137
 
8.7%
S 121
 
7.7%
A 99
 
6.3%
T 98
 
6.2%
L 94
 
6.0%
G 87
 
5.5%
D 84
 
5.3%
B 77
 
4.9%
Other values (13) 372
23.6%
Decimal Number
ValueCountFrequency (%)
1 1377
27.9%
2 954
19.3%
3 598
12.1%
4 559
11.3%
5 332
 
6.7%
8 260
 
5.3%
7 245
 
5.0%
6 238
 
4.8%
0 230
 
4.7%
9 140
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
e 18
28.6%
t 9
14.3%
l 9
14.3%
o 9
14.3%
c 9
14.3%
m 9
14.3%
Other Punctuation
ValueCountFrequency (%)
' 20000
99.8%
, 24
 
0.1%
& 11
 
0.1%
@ 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
23095
100.0%
Open Punctuation
ValueCountFrequency (%)
( 510
100.0%
Close Punctuation
ValueCountFrequency (%)
) 510
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 90
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78157
60.6%
Common 49201
38.1%
Latin 1636
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 4099
 
5.2%
구 4004
 
5.1%
역 3335
 
4.3%
출 3027
 
3.9%
번 3026
 
3.9%
동 1692
 
2.2%
리 1463
 
1.9%
원 1379
 
1.8%
교 1367
 
1.7%
사 1228
 
1.6%
Other values (387) 53537
68.5%
Latin
ValueCountFrequency (%)
C 213
13.0%
K 191
11.7%
M 137
 
8.4%
S 121
 
7.4%
A 99
 
6.1%
T 98
 
6.0%
L 94
 
5.7%
G 87
 
5.3%
D 84
 
5.1%
B 77
 
4.7%
Other values (19) 435
26.6%
Common
ValueCountFrequency (%)
23095
46.9%
' 20000
40.6%
1 1377
 
2.8%
2 954
 
1.9%
3 598
 
1.2%
4 559
 
1.1%
( 510
 
1.0%
) 510
 
1.0%
5 332
 
0.7%
8 260
 
0.5%
Other values (9) 1006
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78157
60.6%
ASCII 50837
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23095
45.4%
' 20000
39.3%
1 1377
 
2.7%
2 954
 
1.9%
3 598
 
1.2%
4 559
 
1.1%
( 510
 
1.0%
) 510
 
1.0%
5 332
 
0.7%
8 260
 
0.5%
Other values (38) 2642
 
5.2%
Hangul
ValueCountFrequency (%)
앞 4099
 
5.2%
구 4004
 
5.1%
역 3335
 
4.3%
출 3027
 
3.9%
번 3026
 
3.9%
동 1692
 
2.2%
리 1463
 
1.9%
원 1379
 
1.8%
교 1367
 
1.7%
사 1228
 
1.6%
Other values (387) 53537
68.5%

'대여구분코드'
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'정기'
7589 
'일일(회원)'
1901 
'일일(비회원)'
 
453
'단체'
 
57

Length

Max length9
Median length4
Mean length4.9869
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
'정기' 7589
75.9%
'일일(회원)' 1901
 
19.0%
'일일(비회원)' 453
 
4.5%
'단체' 57
 
0.6%

Length

2024-05-04T03:17:00.077396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:17:00.266737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7589
75.9%
일일(회원 1901
 
19.0%
일일(비회원 453
 
4.5%
단체 57
 
0.6%

'성별'
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'M'
5406 
'F'
4244 
''
 
350

Length

Max length3
Median length3
Mean length2.965
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
'M' 5406
54.1%
'F' 4244
42.4%
'' 350
 
3.5%

Length

2024-05-04T03:17:00.509923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:17:00.684138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 5406
54.1%
f 4244
42.4%
350
 
3.5%

'연령대코드'
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'20대'
3317 
'30대'
2469 
'40대'
1759 
'50대'
886 
'~10대'
646 
Other values (3)
923 

Length

Max length6
Median length5
Mean length5.0358
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'40대'
2nd row'30대'
3rd row'50대'
4th row'30대'
5th row'30대'

Common Values

ValueCountFrequency (%)
'20대' 3317
33.2%
'30대' 2469
24.7%
'40대' 1759
17.6%
'50대' 886
 
8.9%
'~10대' 646
 
6.5%
<NA> 445
 
4.5%
'60대' 321
 
3.2%
'70대~' 157
 
1.6%

Length

2024-05-04T03:17:01.003362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:17:01.339217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3317
33.2%
30대 2469
24.7%
40대 1759
17.6%
50대 886
 
8.9%
10대 646
 
6.5%
na 445
 
4.5%
60대 321
 
3.2%
70대 157
 
1.6%

'이용건수'
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9214
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:17:01.610928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum33
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.1400968
Coefficient of variation (CV)1.0748603
Kurtosis12.654215
Mean2.9214
Median Absolute Deviation (MAD)1
Skewness3.0132567
Sum29214
Variance9.8602081
MonotonicityNot monotonic
2024-05-04T03:17:01.839235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 4452
44.5%
2 1981
19.8%
3 1104
 
11.0%
4 681
 
6.8%
5 450
 
4.5%
6 323
 
3.2%
7 254
 
2.5%
8 162
 
1.6%
9 145
 
1.5%
10 90
 
0.9%
Other values (21) 358
 
3.6%
ValueCountFrequency (%)
1 4452
44.5%
2 1981
19.8%
3 1104
 
11.0%
4 681
 
6.8%
5 450
 
4.5%
6 323
 
3.2%
7 254
 
2.5%
8 162
 
1.6%
9 145
 
1.5%
10 90
 
0.9%
ValueCountFrequency (%)
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
28 1
 
< 0.1%
27 5
0.1%
26 2
 
< 0.1%
25 3
< 0.1%
24 2
 
< 0.1%
23 3
< 0.1%
22 6
0.1%

'운동량'
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8127
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.01737
Minimum0
Maximum79706.96
Zeros192
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:17:02.167441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.1675
Q165.14
median165.965
Q3380.025
95-th percentile939.511
Maximum79706.96
Range79706.96
Interquartile range (IQR)314.885

Descriptive statistics

Standard deviation876.29419
Coefficient of variation (CV)2.9503129
Kurtosis6746.8986
Mean297.01737
Median Absolute Deviation (MAD)121.66
Skewness74.768798
Sum2970173.7
Variance767891.51
MonotonicityNot monotonic
2024-05-04T03:17:02.613595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 192
 
1.9%
25.23 8
 
0.1%
39.64 8
 
0.1%
34.49 8
 
0.1%
30.89 8
 
0.1%
38.61 8
 
0.1%
46.33 7
 
0.1%
29.6 7
 
0.1%
36.81 7
 
0.1%
43.24 7
 
0.1%
Other values (8117) 9740
97.4%
ValueCountFrequency (%)
0.0 192
1.9%
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.31 1
 
< 0.1%
0.51 2
 
< 0.1%
0.62 1
 
< 0.1%
0.77 3
 
< 0.1%
0.85 1
 
< 0.1%
1.03 1
 
< 0.1%
1.11 1
 
< 0.1%
ValueCountFrequency (%)
79706.96 1
< 0.1%
7515.71 1
< 0.1%
6223.46 1
< 0.1%
6124.83 1
< 0.1%
4505.29 1
< 0.1%
4468.74 1
< 0.1%
4437.18 1
< 0.1%
4268.78 1
< 0.1%
4096.37 1
< 0.1%
3736.85 1
< 0.1%

'탄소량'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1146
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.503068
Minimum0
Maximum55.2
Zeros198
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:17:03.063325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.59
median1.48
Q33.31
95-th percentile7.9305
Maximum55.2
Range55.2
Interquartile range (IQR)2.72

Descriptive statistics

Standard deviation3.1318535
Coefficient of variation (CV)1.2512059
Kurtosis39.399968
Mean2.503068
Median Absolute Deviation (MAD)1.07
Skewness4.31664
Sum25030.68
Variance9.8085064
MonotonicityNot monotonic
2024-05-04T03:17:03.524403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 198
 
2.0%
0.26 68
 
0.7%
0.36 65
 
0.7%
0.38 62
 
0.6%
0.35 62
 
0.6%
0.16 61
 
0.6%
0.29 61
 
0.6%
0.44 58
 
0.6%
0.42 57
 
0.6%
0.32 55
 
0.5%
Other values (1136) 9253
92.5%
ValueCountFrequency (%)
0.0 198
2.0%
0.01 5
 
0.1%
0.02 2
 
< 0.1%
0.03 2
 
< 0.1%
0.04 4
 
< 0.1%
0.05 2
 
< 0.1%
0.06 3
 
< 0.1%
0.07 8
 
0.1%
0.08 8
 
0.1%
0.09 18
 
0.2%
ValueCountFrequency (%)
55.2 1
< 0.1%
54.78 1
< 0.1%
48.33 1
< 0.1%
47.74 1
< 0.1%
42.51 1
< 0.1%
37.59 1
< 0.1%
35.86 1
< 0.1%
31.58 1
< 0.1%
30.75 1
< 0.1%
29.63 1
< 0.1%

'이동거리(M)'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3015
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10788.847
Minimum0
Maximum237950
Zeros192
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:17:03.821513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile720
Q12560
median6360
Q314250
95-th percentile34191
Maximum237950
Range237950
Interquartile range (IQR)11690

Descriptive statistics

Standard deviation13498.492
Coefficient of variation (CV)1.2511525
Kurtosis39.3963
Mean10788.847
Median Absolute Deviation (MAD)4620
Skewness4.316314
Sum1.0788847 × 108
Variance1.822093 × 108
MonotonicityNot monotonic
2024-05-04T03:17:04.071948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 192
 
1.9%
1540 20
 
0.2%
1640 19
 
0.2%
1830 18
 
0.2%
1100 18
 
0.2%
1340 17
 
0.2%
4450 17
 
0.2%
1560 17
 
0.2%
1650 17
 
0.2%
1240 17
 
0.2%
Other values (3005) 9648
96.5%
ValueCountFrequency (%)
0 192
1.9%
10 3
 
< 0.1%
20 3
 
< 0.1%
30 3
 
< 0.1%
40 2
 
< 0.1%
90 1
 
< 0.1%
100 1
 
< 0.1%
110 2
 
< 0.1%
160 2
 
< 0.1%
190 2
 
< 0.1%
ValueCountFrequency (%)
237950 1
< 0.1%
236130 1
< 0.1%
208300 1
< 0.1%
205660 1
< 0.1%
183220 1
< 0.1%
162000 1
< 0.1%
154610 1
< 0.1%
136110 1
< 0.1%
132510 1
< 0.1%
127650 1
< 0.1%

'이동시간(분)'
Real number (ℝ)

HIGH CORRELATION 

Distinct474
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.3159
Minimum0
Maximum1321
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:17:04.676219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q119
median47
Q399
95-th percentile234.05
Maximum1321
Range1321
Interquartile range (IQR)80

Descriptive statistics

Standard deviation86.293609
Coefficient of variation (CV)1.1611729
Kurtosis20.41124
Mean74.3159
Median Absolute Deviation (MAD)33
Skewness3.2764178
Sum743159
Variance7446.587
MonotonicityNot monotonic
2024-05-04T03:17:05.122404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 182
 
1.8%
8 179
 
1.8%
9 163
 
1.6%
7 162
 
1.6%
10 159
 
1.6%
13 151
 
1.5%
16 146
 
1.5%
11 143
 
1.4%
12 139
 
1.4%
5 139
 
1.4%
Other values (464) 8437
84.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 19
 
0.2%
2 83
0.8%
3 96
1.0%
4 138
1.4%
5 139
1.4%
6 182
1.8%
7 162
1.6%
8 179
1.8%
9 163
1.6%
ValueCountFrequency (%)
1321 1
< 0.1%
1269 1
< 0.1%
1080 1
< 0.1%
935 1
< 0.1%
843 1
< 0.1%
841 1
< 0.1%
827 1
< 0.1%
812 1
< 0.1%
802 1
< 0.1%
779 1
< 0.1%

Interactions

2024-05-04T03:16:54.628454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:50.132786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:51.015977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:52.101561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:53.387961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:54.839661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:50.326916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:51.206536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:52.363991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:53.647025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:55.092697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:50.515382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:51.432175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:52.731034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:53.923482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:55.352521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:50.680089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:51.608552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:52.991473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:54.261103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:55.520702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:50.840746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:51.820481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:53.183817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:54.459386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T03:17:05.413795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여일자''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
'대여일자'1.0000.2330.2400.0990.1070.0000.0520.0520.036
'대여구분코드'0.2331.0000.6030.1780.1850.0000.0300.0300.066
'성별'0.2400.6031.0000.0900.1660.0000.0830.0830.118
'연령대코드'0.0990.1780.0901.0000.2290.0000.1500.1500.179
'이용건수'0.1070.1850.1660.2291.0000.0000.6510.6510.691
'운동량'0.0000.0000.0000.0000.0001.0000.0120.0120.000
'탄소량'0.0520.0300.0830.1500.6510.0121.0001.0000.866
'이동거리(M)'0.0520.0300.0830.1500.6510.0121.0001.0000.866
'이동시간(분)'0.0360.0660.1180.1790.6910.0000.8660.8661.000
2024-05-04T03:17:05.727979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'성별''대여구분코드''연령대코드'
'성별'1.0000.6180.096
'대여구분코드'0.6181.0000.123
'연령대코드'0.0960.1231.000
2024-05-04T03:17:06.001879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'이용건수''운동량''탄소량''이동거리(M)''이동시간(분)''대여구분코드''성별''연령대코드'
'이용건수'1.0000.6750.6770.6770.6860.1110.0990.118
'운동량'0.6751.0000.9900.9900.8800.0000.0000.000
'탄소량'0.6770.9901.0001.0000.8950.0180.0490.076
'이동거리(M)'0.6770.9901.0001.0000.8950.0180.0490.076
'이동시간(분)'0.6860.8800.8950.8951.0000.0390.0700.091
'대여구분코드'0.1110.0000.0180.0180.0391.0000.6180.123
'성별'0.0990.0000.0490.0490.0700.6181.0000.096
'연령대코드'0.1180.0000.0760.0760.0910.1230.0961.000

Missing values

2024-05-04T03:16:55.769830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T03:16:56.213613image/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)'
8089'2017-08-02''1525'' 미아동 복합청사''정기''F''40대'144.790.45195011
5877'2017-08-01''587'' KEB하나은행 성수중앙지점''일일(회원)''M''30대'140.150.3213707
1607'2017-08-01''328'' 탑골공원 앞''정기''F''50대'134.490.3113408
11497'2017-08-02''379'' 서울역9번출구''일일(회원)''F''30대'191.370.96412038
900'2017-08-01''610'' 동대문중 교차로''정기''F''30대'3114.841.235300110
16238'2017-08-03''2122'' 낙성대로 입구''정기''M''30대'6813.616.3327290171
12349'2017-08-02''1834'' 월드메르디앙 벤처센터 2차''일일(회원)''M''30대'187.850.61261019
16977'2017-08-03''332'' 을지로2가 사거리 남측''정기''M''50대'2182.311.45623035
5358'2017-08-01''359'' 원남동사거리''일일(회원)''M''20대'262.350.55236011
7073'2017-08-02''926'' 불광역 8번출구''정기''F''20대'6290.162.81208096
'대여일자''대여소번호''대여소''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
12963'2017-08-02''1610'' 화랑대역 2번출구 앞''일일(비회원)''M'<NA>2107.080.96416046
1436'2017-08-01''388'' 동성중학교 앞''정기''F''40대'113.430.156404
12856'2017-08-02''2331'' 동영문화센터앞''일일(비회원)'''<NA>1128.961.16501040
3255'2017-08-01''2205'' 내곡3단지 어린이공원 앞''정기''M''30대'243.760.3917007
13711'2017-08-03''1201'' 가락시장역 3번 출구''정기''F''20대'4230.082.29985097
8204'2017-08-02''146'' 마포역 1번출구 뒤''정기''F''50대'141.340.54232033
4711'2017-08-01''812'' 용산전쟁기념관''일일(회원)''F''20대'4105.251.18506034
2769'2017-08-01''306'' 광화문역 7번출구 앞''정기''M''30대'13660.975.4623500496
13349'2017-08-03''181'' 망원초록길 입구''정기''F''20대'1220.081.98855037
8411'2017-08-02''911'' 은평평화공원(역촌역4번출구)''정기''F''60대'121.860.2192074