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/F/1/datasetView.do

Alerts

'이용건수' is highly overall correlated with '운동량' and 2 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 '운동량' and 2 other fieldsHigh correlation
'대여구분코드' is highly imbalanced (55.7%)Imbalance

Reproduction

Analysis started2024-03-13 16:24:02.970039
Analysis finished2024-03-13 16:24:06.067761
Duration3.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Date

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-01-01 00:00:00
Maximum2017-01-09 00:00:00
2024-03-14T01:24:06.106063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:06.193677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

'대여소번호'
Text

Distinct444
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:24:06.530007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters50000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row'220'
2nd row'267'
3rd row'105'
4th row'511'
5th row'340'
ValueCountFrequency (%)
502 57
 
0.6%
113 54
 
0.5%
419 52
 
0.5%
207 51
 
0.5%
259 48
 
0.5%
418 46
 
0.5%
907 46
 
0.5%
501 46
 
0.5%
221 45
 
0.4%
339 44
 
0.4%
Other values (434) 9511
95.1%
2024-03-14T01:24:06.973164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 20000
40.0%
1 5300
 
10.6%
3 4084
 
8.2%
2 4015
 
8.0%
5 3522
 
7.0%
0 2988
 
6.0%
4 2547
 
5.1%
6 2390
 
4.8%
8 1836
 
3.7%
9 1666
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
60.0%
Other Punctuation 20000
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5300
17.7%
3 4084
13.6%
2 4015
13.4%
5 3522
11.7%
0 2988
10.0%
4 2547
8.5%
6 2390
8.0%
8 1836
 
6.1%
9 1666
 
5.6%
7 1652
 
5.5%
Other Punctuation
ValueCountFrequency (%)
' 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
' 20000
40.0%
1 5300
 
10.6%
3 4084
 
8.2%
2 4015
 
8.0%
5 3522
 
7.0%
0 2988
 
6.0%
4 2547
 
5.1%
6 2390
 
4.8%
8 1836
 
3.7%
9 1666
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 20000
40.0%
1 5300
 
10.6%
3 4084
 
8.2%
2 4015
 
8.0%
5 3522
 
7.0%
0 2988
 
6.0%
4 2547
 
5.1%
6 2390
 
4.8%
8 1836
 
3.7%
9 1666
 
3.3%

'대여소'
Text

Distinct444
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:24:07.201527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length12.4953
Min length6

Characters and Unicode

Total characters124953
Distinct characters351
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row' 미성아파트 A동 앞'
2nd row' 삼성화재 사옥 옆'
3rd row' 합정역 5번출구 앞'
4th row' 서울숲역 4번 출구 옆'
5th row' 혜화동 로터리'
ValueCountFrequency (%)
10067
30.1%
앞 4567
 
13.7%
옆 980
 
2.9%
사거리 537
 
1.6%
1번출구 511
 
1.5%
2번출구 471
 
1.4%
뒤 408
 
1.2%
4번출구 387
 
1.2%
출구 321
 
1.0%
3번출구 272
 
0.8%
Other values (526) 14880
44.5%
2024-03-14T01:24:07.497916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23401
 
18.7%
' 20000
 
16.0%
앞 4713
 
3.8%
구 3981
 
3.2%
역 3487
 
2.8%
출 3191
 
2.6%
번 3138
 
2.5%
리 1490
 
1.2%
동 1456
 
1.2%
원 1307
 
1.0%
Other values (341) 58789
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74509
59.6%
Space Separator 23401
 
18.7%
Other Punctuation 20000
 
16.0%
Decimal Number 4533
 
3.6%
Uppercase Letter 1899
 
1.5%
Close Punctuation 296
 
0.2%
Open Punctuation 296
 
0.2%
Dash Punctuation 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 4713
 
6.3%
구 3981
 
5.3%
역 3487
 
4.7%
출 3191
 
4.3%
번 3138
 
4.2%
리 1490
 
2.0%
동 1456
 
2.0%
원 1307
 
1.8%
사 1295
 
1.7%
교 1167
 
1.6%
Other values (308) 49284
66.1%
Uppercase Letter
ValueCountFrequency (%)
C 293
15.4%
K 284
15.0%
S 152
8.0%
M 151
8.0%
B 141
 
7.4%
D 124
 
6.5%
E 118
 
6.2%
T 106
 
5.6%
I 82
 
4.3%
J 74
 
3.9%
Other values (8) 374
19.7%
Decimal Number
ValueCountFrequency (%)
1 1171
25.8%
2 930
20.5%
4 668
14.7%
3 499
11.0%
5 265
 
5.8%
8 258
 
5.7%
7 254
 
5.6%
6 211
 
4.7%
0 144
 
3.2%
9 133
 
2.9%
Space Separator
ValueCountFrequency (%)
23401
100.0%
Other Punctuation
ValueCountFrequency (%)
' 20000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 296
100.0%
Open Punctuation
ValueCountFrequency (%)
( 296
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74509
59.6%
Common 48545
38.9%
Latin 1899
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 4713
 
6.3%
구 3981
 
5.3%
역 3487
 
4.7%
출 3191
 
4.3%
번 3138
 
4.2%
리 1490
 
2.0%
동 1456
 
2.0%
원 1307
 
1.8%
사 1295
 
1.7%
교 1167
 
1.6%
Other values (308) 49284
66.1%
Latin
ValueCountFrequency (%)
C 293
15.4%
K 284
15.0%
S 152
8.0%
M 151
8.0%
B 141
 
7.4%
D 124
 
6.5%
E 118
 
6.2%
T 106
 
5.6%
I 82
 
4.3%
J 74
 
3.9%
Other values (8) 374
19.7%
Common
ValueCountFrequency (%)
23401
48.2%
' 20000
41.2%
1 1171
 
2.4%
2 930
 
1.9%
4 668
 
1.4%
3 499
 
1.0%
) 296
 
0.6%
( 296
 
0.6%
5 265
 
0.5%
8 258
 
0.5%
Other values (5) 761
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74509
59.6%
ASCII 50444
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23401
46.4%
' 20000
39.6%
1 1171
 
2.3%
2 930
 
1.8%
4 668
 
1.3%
3 499
 
1.0%
) 296
 
0.6%
( 296
 
0.6%
C 293
 
0.6%
K 284
 
0.6%
Other values (23) 2606
 
5.2%
Hangul
ValueCountFrequency (%)
앞 4713
 
6.3%
구 3981
 
5.3%
역 3487
 
4.7%
출 3191
 
4.3%
번 3138
 
4.2%
리 1490
 
2.0%
동 1456
 
2.0%
원 1307
 
1.8%
사 1295
 
1.7%
교 1167
 
1.6%
Other values (308) 49284
66.1%

'대여구분코드'
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'정기'
7772 
'일일(회원)'
1667 
'일일(비회원)'
 
243
'일일(2시간권)'
 
235
'단체'
 
83

Length

Max length10
Median length4
Mean length4.9293
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
'정기' 7772
77.7%
'일일(회원)' 1667
 
16.7%
'일일(비회원)' 243
 
2.4%
'일일(2시간권)' 235
 
2.4%
'단체' 83
 
0.8%

Length

2024-03-14T01:24:07.923962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:24:08.002896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7772
77.7%
일일(회원 1667
 
16.7%
일일(비회원 243
 
2.4%
일일(2시간권 235
 
2.4%
단체 83
 
0.8%

'성별'
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'M'
6084 
'F'
3916 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
'M' 6084
60.8%
'F' 3916
39.2%

Length

2024-03-14T01:24:08.088545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:24:08.159669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 6084
60.8%
f 3916
39.2%

'연령대코드'
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'20대'
3789 
'30대'
2683 
'40대'
1892 
'50대'
939 
'~10대'
 
269
Other values (2)
428 

Length

Max length6
Median length5
Mean length5.043
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
'20대' 3789
37.9%
'30대' 2683
26.8%
'40대' 1892
18.9%
'50대' 939
 
9.4%
'~10대' 269
 
2.7%
'60대' 267
 
2.7%
'70대~' 161
 
1.6%

Length

2024-03-14T01:24:08.237306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:24:08.321386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3789
37.9%
30대 2683
26.8%
40대 1892
18.9%
50대 939
 
9.4%
10대 269
 
2.7%
60대 267
 
2.7%
70대 161
 
1.6%

'이용건수'
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8148
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:08.428278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum16
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.438926
Coefficient of variation (CV)0.79288406
Kurtosis13.951449
Mean1.8148
Median Absolute Deviation (MAD)0
Skewness3.0821811
Sum18148
Variance2.070508
MonotonicityNot monotonic
2024-03-14T01:24:08.513909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 5970
59.7%
2 2136
 
21.4%
3 940
 
9.4%
4 447
 
4.5%
5 220
 
2.2%
6 106
 
1.1%
7 73
 
0.7%
8 39
 
0.4%
9 22
 
0.2%
10 15
 
0.1%
Other values (6) 32
 
0.3%
ValueCountFrequency (%)
1 5970
59.7%
2 2136
 
21.4%
3 940
 
9.4%
4 447
 
4.5%
5 220
 
2.2%
6 106
 
1.1%
7 73
 
0.7%
8 39
 
0.4%
9 22
 
0.2%
10 15
 
0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 2
 
< 0.1%
14 4
 
< 0.1%
13 2
 
< 0.1%
12 10
 
0.1%
11 13
 
0.1%
10 15
 
0.1%
9 22
 
0.2%
8 39
0.4%
7 73
0.7%

'운동량'
Real number (ℝ)

HIGH CORRELATION 

Distinct6976
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.06945
Minimum0
Maximum8827.87
Zeros90
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:08.618977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.02
Q145.21
median88.42
Q3178.865
95-th percentile415.6845
Maximum8827.87
Range8827.87
Interquartile range (IQR)133.655

Descriptive statistics

Standard deviation186.7234
Coefficient of variation (CV)1.3330773
Kurtosis551.32083
Mean140.06945
Median Absolute Deviation (MAD)53.835
Skewness15.114745
Sum1400694.5
Variance34865.629
MonotonicityNot monotonic
2024-03-14T01:24:08.724749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 90
 
0.9%
35.01 11
 
0.1%
64.86 11
 
0.1%
77.22 10
 
0.1%
18.02 10
 
0.1%
29.09 10
 
0.1%
24.97 9
 
0.1%
48.39 9
 
0.1%
32.43 9
 
0.1%
27.03 9
 
0.1%
Other values (6966) 9822
98.2%
ValueCountFrequency (%)
0.0 90
0.9%
0.51 1
 
< 0.1%
1.08 1
 
< 0.1%
1.81 1
 
< 0.1%
3.29 1
 
< 0.1%
3.33 1
 
< 0.1%
4.38 1
 
< 0.1%
4.63 1
 
< 0.1%
5.15 1
 
< 0.1%
5.49 1
 
< 0.1%
ValueCountFrequency (%)
8827.87 1
< 0.1%
4570.83 1
< 0.1%
4536.02 1
< 0.1%
3248.86 1
< 0.1%
3198.61 1
< 0.1%
2059.61 1
< 0.1%
1968.89 1
< 0.1%
1751.61 1
< 0.1%
1632.17 1
< 0.1%
1611.56 1
< 0.1%

'탄소량'
Real number (ℝ)

HIGH CORRELATION 

Distinct607
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.191917
Minimum0
Maximum54.99
Zeros91
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:08.841214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16
Q10.41
median0.78
Q31.55
95-th percentile3.51
Maximum54.99
Range54.99
Interquartile range (IQR)1.14

Descriptive statistics

Standard deviation1.4440194
Coefficient of variation (CV)1.21151
Kurtosis306.36252
Mean1.191917
Median Absolute Deviation (MAD)0.46
Skewness10.979901
Sum11919.17
Variance2.0851919
MonotonicityNot monotonic
2024-03-14T01:24:08.960176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23 104
 
1.0%
0.26 103
 
1.0%
0.46 102
 
1.0%
0.32 98
 
1.0%
0.29 97
 
1.0%
0.37 96
 
1.0%
0.25 95
 
0.9%
0.55 94
 
0.9%
0.47 91
 
0.9%
0.0 91
 
0.9%
Other values (597) 9029
90.3%
ValueCountFrequency (%)
0.0 91
0.9%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.04 5
 
0.1%
0.05 3
 
< 0.1%
0.06 8
 
0.1%
0.07 6
 
0.1%
0.08 17
 
0.2%
0.09 22
 
0.2%
0.1 38
0.4%
ValueCountFrequency (%)
54.99 1
< 0.1%
44.29 1
< 0.1%
30.71 1
< 0.1%
26.77 1
< 0.1%
17.09 1
< 0.1%
15.9 1
< 0.1%
14.71 1
< 0.1%
13.81 1
< 0.1%
13.58 1
< 0.1%
12.72 1
< 0.1%

'이동거리(M)'
Real number (ℝ)

HIGH CORRELATION 

Distinct1776
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5137.474
Minimum0
Maximum237010
Zeros90
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:09.065108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile700
Q11750
median3350
Q36672.5
95-th percentile15140.5
Maximum237010
Range237010
Interquartile range (IQR)4922.5

Descriptive statistics

Standard deviation6224.4649
Coefficient of variation (CV)1.2115808
Kurtosis306.30694
Mean5137.474
Median Absolute Deviation (MAD)1990
Skewness10.979336
Sum51374740
Variance38743963
MonotonicityNot monotonic
2024-03-14T01:24:09.173700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90
 
0.9%
1990 32
 
0.3%
910 31
 
0.3%
970 31
 
0.3%
1590 30
 
0.3%
1680 30
 
0.3%
1420 29
 
0.3%
1150 28
 
0.3%
2400 28
 
0.3%
1130 28
 
0.3%
Other values (1766) 9643
96.4%
ValueCountFrequency (%)
0 90
0.9%
20 1
 
< 0.1%
40 1
 
< 0.1%
80 1
 
< 0.1%
160 1
 
< 0.1%
170 2
 
< 0.1%
180 2
 
< 0.1%
200 1
 
< 0.1%
210 1
 
< 0.1%
230 1
 
< 0.1%
ValueCountFrequency (%)
237010 1
< 0.1%
190910 1
< 0.1%
132430 1
< 0.1%
115390 1
< 0.1%
73660 1
< 0.1%
68570 1
< 0.1%
63410 1
< 0.1%
59560 1
< 0.1%
58530 1
< 0.1%
54850 1
< 0.1%

'이동시간(분)'
Real number (ℝ)

HIGH CORRELATION 

Distinct246
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8514
Minimum1
Maximum1029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:09.283053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median23
Q346
95-th percentile104
Maximum1029
Range1028
Interquartile range (IQR)35

Descriptive statistics

Standard deviation41.004093
Coefficient of variation (CV)1.1765408
Kurtosis74.225731
Mean34.8514
Median Absolute Deviation (MAD)15
Skewness5.7181471
Sum348514
Variance1681.3357
MonotonicityNot monotonic
2024-03-14T01:24:09.393981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 316
 
3.2%
6 304
 
3.0%
8 297
 
3.0%
7 292
 
2.9%
5 292
 
2.9%
4 282
 
2.8%
9 271
 
2.7%
14 251
 
2.5%
13 250
 
2.5%
12 245
 
2.5%
Other values (236) 7200
72.0%
ValueCountFrequency (%)
1 41
 
0.4%
2 124
 
1.2%
3 218
2.2%
4 282
2.8%
5 292
2.9%
6 304
3.0%
7 292
2.9%
8 297
3.0%
9 271
2.7%
10 316
3.2%
ValueCountFrequency (%)
1029 1
< 0.1%
756 1
< 0.1%
589 1
< 0.1%
587 1
< 0.1%
582 1
< 0.1%
541 1
< 0.1%
538 1
< 0.1%
530 1
< 0.1%
527 1
< 0.1%
523 1
< 0.1%

Interactions

2024-03-14T01:24:05.430083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:03.975710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.331622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.695398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.080253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.506798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.050755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.414755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.791106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.155982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.591173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.118438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.485296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.859498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.227405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.680830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.191021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.556643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.929497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.295660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.762800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.257847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:04.622067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.010894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:05.361732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:24:09.469136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여일자''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
'대여일자'1.0000.2100.1060.0790.0870.0000.0310.0310.068
'대여구분코드'0.2101.0000.0370.1780.2150.1260.1780.1780.279
'성별'0.1060.0371.0000.0720.2250.0410.0270.0270.000
'연령대코드'0.0790.1780.0721.0000.1330.0350.0450.0450.049
'이용건수'0.0870.2150.2250.1331.0000.1890.2160.2160.312
'운동량'0.0000.1260.0410.0350.1891.0000.9280.9280.615
'탄소량'0.0310.1780.0270.0450.2160.9281.0001.0000.867
'이동거리(M)'0.0310.1780.0270.0450.2160.9281.0001.0000.867
'이동시간(분)'0.0680.2790.0000.0490.3120.6150.8670.8671.000
2024-03-14T01:24:09.561903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'연령대코드''대여구분코드''성별'
'연령대코드'1.0000.1140.077
'대여구분코드'0.1141.0000.046
'성별'0.0770.0461.000
2024-03-14T01:24:09.635765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'이용건수''운동량''탄소량''이동거리(M)''이동시간(분)''대여구분코드''성별''연령대코드'
'이용건수'1.0000.5450.5380.5380.4950.0920.1730.067
'운동량'0.5451.0000.9860.9860.8560.0860.0290.021
'탄소량'0.5380.9861.0001.0000.8750.1100.0200.024
'이동거리(M)'0.5380.9861.0001.0000.8760.1100.0200.024
'이동시간(분)'0.4950.8560.8750.8761.0000.1750.0000.026
'대여구분코드'0.0920.0860.1100.1100.1751.0000.0460.114
'성별'0.1730.0290.0200.0200.0000.0461.0000.077
'연령대코드'0.0670.0210.0240.0240.0260.1140.0771.000

Missing values

2024-03-14T01:24:05.880692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:24:06.012209image/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)'
8712'2017-01-05''220'' 미성아파트 A동 앞''정기''F''40대'145.620.37160011
6761'2017-01-04''267'' 삼성화재 사옥 옆''정기''M''20대'1175.131.63702051
1787'2017-01-02''105'' 합정역 5번출구 앞''정기''F''20대'265.620.62268016
13639'2017-01-07''511'' 서울숲역 4번 출구 옆''정기''M''20대'3121.221.06460036
3422'2017-01-02''340'' 혜화동 로터리''일일(회원)''M''20대'1149.031.34579039
10377'2017-01-05''811'' 녹사평역1번출구''일일(회원)''M''60대'1398.643.21379048
17892'2017-01-09''410'' 상암중학교 옆''정기''F''20대'2105.520.98421028
4592'2017-01-03''402'' 상암월드컵파크 9단지 앞''정기''M''20대'141.180.37160010
2966'2017-01-02''800'' 목월공원 앞''정기''M''40대'144.910.44189018
3134'2017-01-02''124'' 서강대 정문 건너편''정기''M''50대'1156.241.41607034
'대여일자''대여소번호''대여소''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
15753'2017-01-08''623'' 서울시립대 정문 앞''정기''F''40대'1170.831.67719055
6206'2017-01-04''911'' 은평평화공원(역촌역4번출구)''정기''F''30대'1108.81.12482023
5201'2017-01-03''511'' 서울숲역 4번 출구 옆''정기''M''50대'1102.860.89382026
1975'2017-01-02''202'' 국민일보 앞''정기''F''30대'2104.090.92396028
17946'2017-01-09''350''KEB 하나은행(내자동 지점) 앞''정기''F''30대'18.730.093802
1048'2017-01-01''601'' 용신동주민센터''정기''M''50대'190.190.68292026
16575'2017-01-08''906'' 연신내역 5번출구150M 아래''정기''M''40대'271.310.56240013
8145'2017-01-05''513'' 뚝섬역 5번 출구 정류소 옆''정기''F''20대'287.040.93400023
10928'2017-01-06''106'' 합정역 7번출구 앞''정기''F''30대'2134.441.26542032
6818'2017-01-04''421'' 마포구청 앞''정기''M''20대'5255.761.88813041