Overview

Dataset statistics

Number of variables11
Number of observations7166
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory643.9 KiB
Average record size in memory92.0 B

Variable types

Categorical4
Numeric4
Text3

Dataset

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

Alerts

대여일자 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

Reproduction

Analysis started2024-05-18 04:57:20.753165
Analysis finished2024-05-18 04:57:27.462377
Duration6.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
2022-06-01
7166 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06-01
2nd row2022-06-01
3rd row2022-06-01
4th row2022-06-01
5th row2022-06-01

Common Values

ValueCountFrequency (%)
2022-06-01 7166
100.0%

Length

2024-05-18T13:57:27.811936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:57:28.095453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-06-01 7166
100.0%

대여소번호
Real number (ℝ)

Distinct286
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.39701
Minimum3
Maximum442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.1 KiB
2024-05-18T13:57:28.454999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile114
Q1182
median254
Q3345
95-th percentile422
Maximum442
Range439
Interquartile range (IQR)163

Descriptive statistics

Standard deviation98.111973
Coefficient of variation (CV)0.37390659
Kurtosis-1.0893004
Mean262.39701
Median Absolute Deviation (MAD)80
Skewness0.15306987
Sum1880337
Variance9625.9592
MonotonicityIncreasing
2024-05-18T13:57:29.098270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 54
 
0.8%
186 52
 
0.7%
117 50
 
0.7%
210 49
 
0.7%
272 49
 
0.7%
418 47
 
0.7%
227 45
 
0.6%
419 43
 
0.6%
247 43
 
0.6%
194 42
 
0.6%
Other values (276) 6692
93.4%
ValueCountFrequency (%)
3 1
 
< 0.1%
102 34
0.5%
103 38
0.5%
104 26
0.4%
105 25
0.3%
106 40
0.6%
107 30
0.4%
108 24
0.3%
109 27
0.4%
111 24
0.3%
ValueCountFrequency (%)
442 2
 
< 0.1%
441 10
 
0.1%
440 30
0.4%
439 37
0.5%
438 27
0.4%
437 25
0.3%
436 20
0.3%
435 9
 
0.1%
434 13
 
0.2%
433 20
0.3%

대여소
Text

Distinct286
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
2024-05-18T13:57:29.701385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length14.325984
Min length4

Characters and Unicode

Total characters102660
Distinct characters307
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row중랑센터
2nd row102. 망원역 1번출구 앞
3rd row102. 망원역 1번출구 앞
4th row102. 망원역 1번출구 앞
5th row102. 망원역 1번출구 앞
ValueCountFrequency (%)
앞 2881
 
12.5%
옆 628
 
2.7%
사거리 379
 
1.6%
1번출구 376
 
1.6%
뒤 322
 
1.4%
2번출구 306
 
1.3%
4번출구 204
 
0.9%
3번출구 175
 
0.8%
5번출구 173
 
0.7%
건너편 143
 
0.6%
Other values (629) 17526
75.8%
2024-05-18T13:57:30.944895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16055
 
15.6%
. 7165
 
7.0%
2 4943
 
4.8%
1 4447
 
4.3%
3 3572
 
3.5%
앞 2950
 
2.9%
4 2726
 
2.7%
구 2542
 
2.5%
역 2045
 
2.0%
번 1937
 
1.9%
Other values (297) 54278
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52830
51.5%
Decimal Number 24693
24.1%
Space Separator 16055
 
15.6%
Other Punctuation 7165
 
7.0%
Uppercase Letter 1403
 
1.4%
Close Punctuation 232
 
0.2%
Open Punctuation 232
 
0.2%
Dash Punctuation 50
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 2950
 
5.6%
구 2542
 
4.8%
역 2045
 
3.9%
번 1937
 
3.7%
출 1903
 
3.6%
동 973
 
1.8%
원 937
 
1.8%
교 909
 
1.7%
사 877
 
1.7%
리 864
 
1.6%
Other values (263) 36893
69.8%
Uppercase Letter
ValueCountFrequency (%)
K 226
16.1%
C 215
15.3%
M 141
10.0%
D 141
10.0%
S 135
9.6%
B 120
8.6%
T 72
 
5.1%
E 71
 
5.1%
I 49
 
3.5%
F 49
 
3.5%
Other values (9) 184
13.1%
Decimal Number
ValueCountFrequency (%)
2 4943
20.0%
1 4447
18.0%
3 3572
14.5%
4 2726
11.0%
0 1710
 
6.9%
8 1485
 
6.0%
7 1479
 
6.0%
6 1475
 
6.0%
9 1441
 
5.8%
5 1415
 
5.7%
Space Separator
ValueCountFrequency (%)
16055
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7165
100.0%
Close Punctuation
ValueCountFrequency (%)
) 232
100.0%
Open Punctuation
ValueCountFrequency (%)
( 232
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52830
51.5%
Common 48427
47.2%
Latin 1403
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 2950
 
5.6%
구 2542
 
4.8%
역 2045
 
3.9%
번 1937
 
3.7%
출 1903
 
3.6%
동 973
 
1.8%
원 937
 
1.8%
교 909
 
1.7%
사 877
 
1.7%
리 864
 
1.6%
Other values (263) 36893
69.8%
Latin
ValueCountFrequency (%)
K 226
16.1%
C 215
15.3%
M 141
10.0%
D 141
10.0%
S 135
9.6%
B 120
8.6%
T 72
 
5.1%
E 71
 
5.1%
I 49
 
3.5%
F 49
 
3.5%
Other values (9) 184
13.1%
Common
ValueCountFrequency (%)
16055
33.2%
. 7165
14.8%
2 4943
 
10.2%
1 4447
 
9.2%
3 3572
 
7.4%
4 2726
 
5.6%
0 1710
 
3.5%
8 1485
 
3.1%
7 1479
 
3.1%
6 1475
 
3.0%
Other values (5) 3370
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52830
51.5%
ASCII 49830
48.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16055
32.2%
. 7165
14.4%
2 4943
 
9.9%
1 4447
 
8.9%
3 3572
 
7.2%
4 2726
 
5.5%
0 1710
 
3.4%
8 1485
 
3.0%
7 1479
 
3.0%
6 1475
 
3.0%
Other values (24) 4773
 
9.6%
Hangul
ValueCountFrequency (%)
앞 2950
 
5.6%
구 2542
 
4.8%
역 2045
 
3.9%
번 1937
 
3.7%
출 1903
 
3.6%
동 973
 
1.8%
원 937
 
1.8%
교 909
 
1.7%
사 877
 
1.7%
리 864
 
1.6%
Other values (263) 36893
69.8%

대여구분코드
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
정기권
4147 
일일권
2550 
단체권
 
251
일일권(비회원)
 
218

Length

Max length8
Median length3
Mean length3.1521072
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기권
2nd row단체권
3rd row일일권(비회원)
4th row일일권
5th row일일권

Common Values

ValueCountFrequency (%)
정기권 4147
57.9%
일일권 2550
35.6%
단체권 251
 
3.5%
일일권(비회원) 218
 
3.0%

Length

2024-05-18T13:57:31.475480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:57:31.859609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기권 4147
57.9%
일일권 2550
35.6%
단체권 251
 
3.5%
일일권(비회원 218
 
3.0%

성별
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
M
2568 
\N
2119 
F
1974 
<NA>
505 

Length

Max length4
Median length1
Mean length1.5071169
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 2568
35.8%
\N 2119
29.6%
F 1974
27.5%
<NA> 505
 
7.0%

Length

2024-05-18T13:57:32.243004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:57:32.647708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 2568
35.8%
n 2119
29.6%
f 1974
27.5%
na 505
 
7.0%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
20대
1792 
30대
1514 
40대
1078 
기타
1030 
50대
771 
Other values (3)
981 

Length

Max length5
Median length3
Mean length2.8710578
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50대
2nd row20대
3rd row기타
4th row20대
5th row30대

Common Values

ValueCountFrequency (%)
20대 1792
25.0%
30대 1514
21.1%
40대 1078
15.0%
기타 1030
14.4%
50대 771
10.8%
10대 593
 
8.3%
60대 335
 
4.7%
70대이상 53
 
0.7%

Length

2024-05-18T13:57:33.298923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:57:33.733932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 1792
25.0%
30대 1514
21.1%
40대 1078
15.0%
기타 1030
14.4%
50대 771
10.8%
10대 593
 
8.3%
60대 335
 
4.7%
70대이상 53
 
0.7%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3399386
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.1 KiB
2024-05-18T13:57:34.257473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum119
Range118
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.3716704
Coefficient of variation (CV)1.3089074
Kurtosis154.12796
Mean3.3399386
Median Absolute Deviation (MAD)1
Skewness8.4531918
Sum23934
Variance19.111502
MonotonicityNot monotonic
2024-05-18T13:57:34.725018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 2756
38.5%
2 1539
21.5%
3 843
 
11.8%
4 538
 
7.5%
5 356
 
5.0%
6 279
 
3.9%
7 185
 
2.6%
8 151
 
2.1%
9 96
 
1.3%
10 88
 
1.2%
Other values (30) 335
 
4.7%
ValueCountFrequency (%)
1 2756
38.5%
2 1539
21.5%
3 843
 
11.8%
4 538
 
7.5%
5 356
 
5.0%
6 279
 
3.9%
7 185
 
2.6%
8 151
 
2.1%
9 96
 
1.3%
10 88
 
1.2%
ValueCountFrequency (%)
119 1
 
< 0.1%
99 1
 
< 0.1%
97 1
 
< 0.1%
83 1
 
< 0.1%
44 1
 
< 0.1%
42 1
 
< 0.1%
41 2
< 0.1%
37 1
 
< 0.1%
36 1
 
< 0.1%
34 4
0.1%

운동량
Text

Distinct6461
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
2024-05-18T13:57:35.718065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.6528049
Min length2

Characters and Unicode

Total characters40508
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

Unique5925 ?
Unique (%)82.7%

Sample

1st row54.69
2nd row246.76
3rd row689.20
4th row679.04
5th row292.92
ValueCountFrequency (%)
0.00 48
 
0.7%
25.23 7
 
0.1%
n 7
 
0.1%
11.33 6
 
0.1%
23.17 5
 
0.1%
22.81 5
 
0.1%
55.60 5
 
0.1%
25.74 5
 
0.1%
32.18 4
 
0.1%
52.51 4
 
0.1%
Other values (6451) 7070
98.7%
2024-05-18T13:57:37.157412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 7159
17.7%
1 4813
11.9%
2 3889
9.6%
3 3621
8.9%
4 3283
8.1%
5 3218
7.9%
6 3050
7.5%
0 2964
7.3%
7 2850
 
7.0%
8 2824
 
7.0%
Other values (3) 2837
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33335
82.3%
Other Punctuation 7166
 
17.7%
Uppercase Letter 7
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4813
14.4%
2 3889
11.7%
3 3621
10.9%
4 3283
9.8%
5 3218
9.7%
6 3050
9.1%
0 2964
8.9%
7 2850
8.5%
8 2824
8.5%
9 2823
8.5%
Other Punctuation
ValueCountFrequency (%)
. 7159
99.9%
\ 7
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40501
> 99.9%
Latin 7
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7159
17.7%
1 4813
11.9%
2 3889
9.6%
3 3621
8.9%
4 3283
8.1%
5 3218
7.9%
6 3050
7.5%
0 2964
7.3%
7 2850
 
7.0%
8 2824
 
7.0%
Other values (2) 2830
 
7.0%
Latin
ValueCountFrequency (%)
N 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7159
17.7%
1 4813
11.9%
2 3889
9.6%
3 3621
8.9%
4 3283
8.1%
5 3218
7.9%
6 3050
7.5%
0 2964
7.3%
7 2850
 
7.0%
8 2824
 
7.0%
Other values (3) 2837
 
7.0%

탄소량
Text

Distinct1143
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size56.1 KiB
2024-05-18T13:57:37.984506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.043818
Min length2

Characters and Unicode

Total characters28978
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

Unique425 ?
Unique (%)5.9%

Sample

1st row0.43
2nd row2.63
3rd row6.21
4th row6.09
5th row2.61
ValueCountFrequency (%)
0.23 64
 
0.9%
0.38 57
 
0.8%
0.19 55
 
0.8%
0.29 49
 
0.7%
0.00 48
 
0.7%
0.45 46
 
0.6%
0.25 45
 
0.6%
0.30 45
 
0.6%
0.20 45
 
0.6%
0.26 45
 
0.6%
Other values (1133) 6667
93.0%
2024-05-18T13:57:39.423354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 7159
24.7%
0 4455
15.4%
1 3339
11.5%
2 2495
 
8.6%
3 2131
 
7.4%
4 1847
 
6.4%
5 1703
 
5.9%
6 1595
 
5.5%
7 1519
 
5.2%
8 1395
 
4.8%
Other values (3) 1340
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21805
75.2%
Other Punctuation 7166
 
24.7%
Uppercase Letter 7
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4455
20.4%
1 3339
15.3%
2 2495
11.4%
3 2131
9.8%
4 1847
8.5%
5 1703
 
7.8%
6 1595
 
7.3%
7 1519
 
7.0%
8 1395
 
6.4%
9 1326
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 7159
99.9%
\ 7
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28971
> 99.9%
Latin 7
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7159
24.7%
0 4455
15.4%
1 3339
11.5%
2 2495
 
8.6%
3 2131
 
7.4%
4 1847
 
6.4%
5 1703
 
5.9%
6 1595
 
5.5%
7 1519
 
5.2%
8 1395
 
4.8%
Other values (2) 1333
 
4.6%
Latin
ValueCountFrequency (%)
N 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7159
24.7%
0 4455
15.4%
1 3339
11.5%
2 2495
 
8.6%
3 2131
 
7.4%
4 1847
 
6.4%
5 1703
 
5.9%
6 1595
 
5.5%
7 1519
 
5.2%
8 1395
 
4.8%
Other values (3) 1340
 
4.6%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION 

Distinct6078
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11772.632
Minimum0
Maximum658221.07
Zeros48
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size63.1 KiB
2024-05-18T13:57:39.935258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile703.1175
Q12310
median5822.42
Q314039.735
95-th percentile40266.975
Maximum658221.07
Range658221.07
Interquartile range (IQR)11729.735

Descriptive statistics

Standard deviation21327.409
Coefficient of variation (CV)1.8116093
Kurtosis280.57692
Mean11772.632
Median Absolute Deviation (MAD)4301.315
Skewness12.177319
Sum84362678
Variance4.5485839 × 108
MonotonicityNot monotonic
2024-05-18T13:57:40.451757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 48
 
0.7%
1650.0 9
 
0.1%
1010.0 9
 
0.1%
1630.0 9
 
0.1%
1430.0 8
 
0.1%
2110.0 8
 
0.1%
1910.0 8
 
0.1%
2040.0 8
 
0.1%
970.0 8
 
0.1%
1590.0 8
 
0.1%
Other values (6068) 7043
98.3%
ValueCountFrequency (%)
0.0 48
0.7%
0.1 1
 
< 0.1%
10.0 4
 
0.1%
18.67 1
 
< 0.1%
20.0 1
 
< 0.1%
30.0 1
 
< 0.1%
38.53 1
 
< 0.1%
53.37 1
 
< 0.1%
60.0 1
 
< 0.1%
70.0 2
 
< 0.1%
ValueCountFrequency (%)
658221.07 1
< 0.1%
572617.37 1
< 0.1%
551554.4 1
< 0.1%
461786.77 1
< 0.1%
264428.4 1
< 0.1%
234469.51 1
< 0.1%
190467.87 1
< 0.1%
184837.74 1
< 0.1%
182941.56 1
< 0.1%
172944.44 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct616
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.63048
Minimum0
Maximum6797
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.1 KiB
2024-05-18T13:57:40.890930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q121
median55
Q3124
95-th percentile360
Maximum6797
Range6797
Interquartile range (IQR)103

Descriptive statistics

Standard deviation200.73734
Coefficient of variation (CV)1.8825513
Kurtosis374.29943
Mean106.63048
Median Absolute Deviation (MAD)41
Skewness14.301906
Sum764114
Variance40295.479
MonotonicityNot monotonic
2024-05-18T13:57:41.420656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 116
 
1.6%
9 114
 
1.6%
5 111
 
1.5%
8 108
 
1.5%
14 104
 
1.5%
10 101
 
1.4%
11 99
 
1.4%
4 98
 
1.4%
6 95
 
1.3%
17 95
 
1.3%
Other values (606) 6125
85.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 19
 
0.3%
2 46
 
0.6%
3 79
1.1%
4 98
1.4%
5 111
1.5%
6 95
1.3%
7 116
1.6%
8 108
1.5%
9 114
1.6%
ValueCountFrequency (%)
6797 1
< 0.1%
6159 1
< 0.1%
5118 1
< 0.1%
4048 1
< 0.1%
2678 1
< 0.1%
2511 1
< 0.1%
1976 1
< 0.1%
1567 1
< 0.1%
1563 1
< 0.1%
1505 1
< 0.1%

Interactions

2024-05-18T13:57:25.430896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:22.308931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:23.388302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:24.361949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:25.688570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:22.537796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:23.643167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:24.615922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:26.019602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:22.849656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:23.820307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:24.887627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:26.292326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:23.102092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:24.077703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:57:25.151794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T13:57:41.733565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0770.0000.0320.0800.0800.082
대여구분코드0.0771.0000.1880.5620.0470.1050.067
성별0.0000.1881.0000.1210.0000.0000.000
연령대코드0.0320.5620.1211.0000.1070.0880.033
이용건수0.0800.0470.0000.1071.0000.9150.956
이동거리(M)0.0800.1050.0000.0880.9151.0000.916
이용시간(분)0.0820.0670.0000.0330.9560.9161.000
2024-05-18T13:57:42.024826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0760.279
성별0.0761.0000.178
대여구분코드0.2790.1781.000
2024-05-18T13:57:42.363950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.078-0.127-0.1140.0490.0000.016
이용건수-0.0781.0000.7600.7600.0320.0000.057
이동거리(M)-0.1270.7601.0000.9070.0470.0000.030
이용시간(분)-0.1140.7600.9071.0000.0460.0000.018
대여구분코드0.0490.0320.0470.0461.0000.1780.279
성별0.0000.0000.0000.0000.1781.0000.076
연령대코드0.0160.0570.0300.0180.2790.0761.000

Missing values

2024-05-18T13:57:26.721724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T13:57:27.241583image/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)
02022-06-013중랑센터정기권M50대154.690.431841.511
12022-06-01102102. 망원역 1번출구 앞단체권\N20대2246.762.6311329.76132
22022-06-01102102. 망원역 1번출구 앞일일권(비회원)\N기타6689.206.2126775.46205
32022-06-01102102. 망원역 1번출구 앞일일권\N20대12679.046.0926277.8337
42022-06-01102102. 망원역 1번출구 앞일일권\N30대3292.922.6111260.096
52022-06-01102102. 망원역 1번출구 앞일일권\N40대2400.333.5815430.091
62022-06-01102102. 망원역 1번출구 앞일일권<NA>20대297.200.773334.6925
72022-06-01102102. 망원역 1번출구 앞일일권F20대12755.468.4436344.45455
82022-06-01102102. 망원역 1번출구 앞일일권F30대3354.923.2413928.96170
92022-06-01102102. 망원역 1번출구 앞일일권F40대15.460.07287.54
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
71562022-06-01441441.염천교사거리일일권M10대1162.681.476320.030
71572022-06-01441441.염천교사거리정기권\N30대128.530.291242.039
71582022-06-01441441.염천교사거리정기권<NA>20대15.770.06260.02
71592022-06-01441441.염천교사거리정기권F40대124.180.22939.538
71602022-06-01441441.염천교사거리정기권M20대160.790.472020.012
71612022-06-01441441.염천교사거리정기권M30대239.080.321371.739
71622022-06-01441441.염천교사거리정기권M40대260.480.552350.014
71632022-06-01441441.염천교사거리정기권M60대162.920.532270.016
71642022-06-01442442.서울역 서부일일권(비회원)\N기타112.610.11490.05
71652022-06-01442442.서울역 서부일일권F20대164.350.582500.016