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

DateTime1
Numeric4
Text3
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/F/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
이동시간(분) is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
대여구분코드 is highly imbalanced (57.0%)Imbalance
이동거리(M) has 250 (2.5%) zerosZeros

Reproduction

Analysis started2024-03-13 16:25:35.151374
Analysis finished2024-03-13 16:25:37.468593
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-06-01 00:00:00
Maximum2021-06-01 00:00:00
2024-03-14T01:25:37.501624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:37.573488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct519
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396.2071
Minimum3
Maximum731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:37.685367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile124.95
Q1238
median385
Q3558
95-th percentile700
Maximum731
Range728
Interquartile range (IQR)320

Descriptive statistics

Standard deviation181.72441
Coefficient of variation (CV)0.45866016
Kurtosis-1.2151406
Mean396.2071
Median Absolute Deviation (MAD)157
Skewness0.131788
Sum3962071
Variance33023.761
MonotonicityNot monotonic
2024-03-14T01:25:38.117549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 44
 
0.4%
565 40
 
0.4%
152 39
 
0.4%
272 39
 
0.4%
210 37
 
0.4%
583 36
 
0.4%
117 34
 
0.3%
247 34
 
0.3%
207 34
 
0.3%
262 34
 
0.3%
Other values (509) 9629
96.3%
ValueCountFrequency (%)
3 1
 
< 0.1%
102 30
0.3%
103 27
0.3%
104 18
0.2%
105 20
0.2%
106 26
0.3%
107 26
0.3%
108 24
0.2%
109 21
0.2%
111 17
0.2%
ValueCountFrequency (%)
731 20
0.2%
729 29
0.3%
726 30
0.3%
725 15
0.1%
724 8
 
0.1%
723 30
0.3%
722 26
0.3%
720 13
0.1%
719 26
0.3%
716 20
0.2%

대여소
Text

Distinct519
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:38.333391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length14.5669
Min length4

Characters and Unicode

Total characters145669
Distinct characters372
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

Unique3 ?
Unique (%)< 0.1%

Sample

1st row387. 훈련원공원주차장 앞
2nd row345. 서울보증보험본사 앞
3rd row240. 문래역 4번출구 앞
4th row667.청량차도 육교 밑
5th row176. 명지대학교 도서관
ValueCountFrequency (%)
앞 3755
 
11.9%
옆 835
 
2.6%
1번출구 472
 
1.5%
사거리 462
 
1.5%
2번출구 337
 
1.1%
출구 288
 
0.9%
뒤 284
 
0.9%
4번출구 267
 
0.8%
3번출구 237
 
0.8%
5번출구 207
 
0.7%
Other values (1086) 24392
77.3%
2024-03-14T01:25:38.693908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21644
 
14.9%
. 9999
 
6.9%
2 5151
 
3.5%
1 4817
 
3.3%
3 4065
 
2.8%
앞 4009
 
2.8%
5 3894
 
2.7%
4 3865
 
2.7%
구 3431
 
2.4%
6 3231
 
2.2%
Other values (362) 81563
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77074
52.9%
Decimal Number 34026
23.4%
Space Separator 21644
 
14.9%
Other Punctuation 9999
 
6.9%
Uppercase Letter 1804
 
1.2%
Close Punctuation 542
 
0.4%
Open Punctuation 542
 
0.4%
Dash Punctuation 38
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 4009
 
5.2%
구 3431
 
4.5%
역 2806
 
3.6%
번 2625
 
3.4%
출 2611
 
3.4%
동 1655
 
2.1%
리 1612
 
2.1%
교 1578
 
2.0%
원 1277
 
1.7%
사 1225
 
1.6%
Other values (328) 54245
70.4%
Uppercase Letter
ValueCountFrequency (%)
K 296
16.4%
S 277
15.4%
C 178
9.9%
B 133
 
7.4%
D 130
 
7.2%
T 113
 
6.3%
M 102
 
5.7%
I 94
 
5.2%
E 67
 
3.7%
A 62
 
3.4%
Other values (9) 352
19.5%
Decimal Number
ValueCountFrequency (%)
2 5151
15.1%
1 4817
14.2%
3 4065
11.9%
5 3894
11.4%
4 3865
11.4%
6 3231
9.5%
7 2724
8.0%
0 2387
7.0%
8 2159
6.3%
9 1733
 
5.1%
Space Separator
ValueCountFrequency (%)
21644
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9999
100.0%
Close Punctuation
ValueCountFrequency (%)
) 542
100.0%
Open Punctuation
ValueCountFrequency (%)
( 542
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77074
52.9%
Common 66791
45.9%
Latin 1804
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 4009
 
5.2%
구 3431
 
4.5%
역 2806
 
3.6%
번 2625
 
3.4%
출 2611
 
3.4%
동 1655
 
2.1%
리 1612
 
2.1%
교 1578
 
2.0%
원 1277
 
1.7%
사 1225
 
1.6%
Other values (328) 54245
70.4%
Latin
ValueCountFrequency (%)
K 296
16.4%
S 277
15.4%
C 178
9.9%
B 133
 
7.4%
D 130
 
7.2%
T 113
 
6.3%
M 102
 
5.7%
I 94
 
5.2%
E 67
 
3.7%
A 62
 
3.4%
Other values (9) 352
19.5%
Common
ValueCountFrequency (%)
21644
32.4%
. 9999
15.0%
2 5151
 
7.7%
1 4817
 
7.2%
3 4065
 
6.1%
5 3894
 
5.8%
4 3865
 
5.8%
6 3231
 
4.8%
7 2724
 
4.1%
0 2387
 
3.6%
Other values (5) 5014
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77074
52.9%
ASCII 68595
47.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21644
31.6%
. 9999
14.6%
2 5151
 
7.5%
1 4817
 
7.0%
3 4065
 
5.9%
5 3894
 
5.7%
4 3865
 
5.6%
6 3231
 
4.7%
7 2724
 
4.0%
0 2387
 
3.5%
Other values (24) 6818
 
9.9%
Hangul
ValueCountFrequency (%)
앞 4009
 
5.2%
구 3431
 
4.5%
역 2806
 
3.6%
번 2625
 
3.4%
출 2611
 
3.4%
동 1655
 
2.1%
리 1612
 
2.1%
교 1578
 
2.0%
원 1277
 
1.7%
사 1225
 
1.6%
Other values (328) 54245
70.4%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7221 
일일(회원)
2546 
일일(비회원)
 
148
단체
 
60
BIL_021
 
25

Length

Max length7
Median length2
Mean length3.1049
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기
2nd row정기
3rd row정기
4th row정기
5th row일일(회원)

Common Values

ValueCountFrequency (%)
정기 7221
72.2%
일일(회원) 2546
 
25.5%
일일(비회원) 148
 
1.5%
단체 60
 
0.6%
BIL_021 25
 
0.2%

Length

2024-03-14T01:25:38.810977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:38.890736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7221
72.2%
일일(회원 2546
 
25.5%
일일(비회원 148
 
1.5%
단체 60
 
0.6%
bil_021 25
 
0.2%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3408 
\N
3073 
F
2668 
<NA>
848 
m
 
2

Length

Max length4
Median length1
Mean length1.5617
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3408
34.1%
\N 3073
30.7%
F 2668
26.7%
<NA> 848
 
8.5%
m 2
 
< 0.1%
f 1
 
< 0.1%

Length

2024-03-14T01:25:38.999672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:39.094771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3410
34.1%
n 3073
30.7%
f 2669
26.7%
na 848
 
8.5%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2650 
AGE_003
2026 
AGE_004
1542 
AGE_008
1499 
AGE_005
1151 
Other values (3)
1132 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_008
2nd rowAGE_004
3rd rowAGE_003
4th rowAGE_004
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 2650
26.5%
AGE_003 2026
20.3%
AGE_004 1542
15.4%
AGE_008 1499
15.0%
AGE_005 1151
11.5%
AGE_001 575
 
5.8%
AGE_006 475
 
4.8%
AGE_007 82
 
0.8%

Length

2024-03-14T01:25:39.204573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:39.293650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2650
26.5%
age_003 2026
20.3%
age_004 1542
15.4%
age_008 1499
15.0%
age_005 1151
11.5%
age_001 575
 
5.8%
age_006 475
 
4.8%
age_007 82
 
0.8%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8505
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:39.400864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum44
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0829045
Coefficient of variation (CV)1.0815311
Kurtosis20.831956
Mean2.8505
Median Absolute Deviation (MAD)1
Skewness3.5593978
Sum28505
Variance9.5043002
MonotonicityNot monotonic
2024-03-14T01:25:39.500934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 4413
44.1%
2 2058
20.6%
3 1162
 
11.6%
4 704
 
7.0%
5 451
 
4.5%
6 302
 
3.0%
7 217
 
2.2%
8 160
 
1.6%
9 130
 
1.3%
10 81
 
0.8%
Other values (23) 322
 
3.2%
ValueCountFrequency (%)
1 4413
44.1%
2 2058
20.6%
3 1162
 
11.6%
4 704
 
7.0%
5 451
 
4.5%
6 302
 
3.0%
7 217
 
2.2%
8 160
 
1.6%
9 130
 
1.3%
10 81
 
0.8%
ValueCountFrequency (%)
44 1
 
< 0.1%
42 1
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
30 1
 
< 0.1%
29 3
< 0.1%
28 3
< 0.1%
26 2
< 0.1%
25 2
< 0.1%
24 3
< 0.1%

운동량
Text

Distinct8526
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:39.778039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length5.3758
Min length1

Characters and Unicode

Total characters53758
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

Unique7487 ?
Unique (%)74.9%

Sample

1st row27.7
2nd row180.72
3rd row17.15
4th row141.54
5th row248.28
ValueCountFrequency (%)
0 220
 
2.2%
n 30
 
0.3%
28.51 5
 
< 0.1%
19.56 5
 
< 0.1%
26.61 5
 
< 0.1%
52.51 4
 
< 0.1%
38.36 4
 
< 0.1%
49.3 4
 
< 0.1%
20.08 4
 
< 0.1%
32.19 4
 
< 0.1%
Other values (8516) 9715
97.2%
2024-03-14T01:25:40.188078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9671
18.0%
1 6468
12.0%
2 5282
9.8%
3 4749
8.8%
4 4499
8.4%
5 4379
8.1%
6 4096
7.6%
8 3924
7.3%
7 3916
7.3%
9 3878
7.2%
Other values (3) 2896
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44027
81.9%
Other Punctuation 9701
 
18.0%
Uppercase Letter 30
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6468
14.7%
2 5282
12.0%
3 4749
10.8%
4 4499
10.2%
5 4379
9.9%
6 4096
9.3%
8 3924
8.9%
7 3916
8.9%
9 3878
8.8%
0 2836
6.4%
Other Punctuation
ValueCountFrequency (%)
. 9671
99.7%
\ 30
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53728
99.9%
Latin 30
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9671
18.0%
1 6468
12.0%
2 5282
9.8%
3 4749
8.8%
4 4499
8.4%
5 4379
8.2%
6 4096
7.6%
8 3924
7.3%
7 3916
7.3%
9 3878
7.2%
Other values (2) 2866
 
5.3%
Latin
ValueCountFrequency (%)
N 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9671
18.0%
1 6468
12.0%
2 5282
9.8%
3 4749
8.8%
4 4499
8.4%
5 4379
8.1%
6 4096
7.6%
8 3924
7.3%
7 3916
7.3%
9 3878
7.2%
Other values (3) 2896
 
5.4%

탄소량
Text

Distinct1065
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:40.542933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.8365
Min length1

Characters and Unicode

Total characters38365
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

Unique352 ?
Unique (%)3.5%

Sample

1st row0.25
2nd row1.65
3rd row0.14
4th row1.18
5th row1.55
ValueCountFrequency (%)
0 224
 
2.2%
0.22 78
 
0.8%
0.23 77
 
0.8%
0.3 76
 
0.8%
0.32 75
 
0.8%
0.26 74
 
0.7%
0.16 74
 
0.7%
0.19 71
 
0.7%
0.25 69
 
0.7%
0.37 69
 
0.7%
Other values (1055) 9113
91.1%
2024-03-14T01:25:41.024802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9679
25.2%
0 5457
14.2%
1 4456
11.6%
2 3446
 
9.0%
3 2951
 
7.7%
4 2435
 
6.3%
5 2235
 
5.8%
6 2054
 
5.4%
7 1953
 
5.1%
8 1896
 
4.9%
Other values (3) 1803
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28626
74.6%
Other Punctuation 9709
 
25.3%
Uppercase Letter 30
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5457
19.1%
1 4456
15.6%
2 3446
12.0%
3 2951
10.3%
4 2435
8.5%
5 2235
7.8%
6 2054
 
7.2%
7 1953
 
6.8%
8 1896
 
6.6%
9 1743
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 9679
99.7%
\ 30
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38335
99.9%
Latin 30
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9679
25.2%
0 5457
14.2%
1 4456
11.6%
2 3446
 
9.0%
3 2951
 
7.7%
4 2435
 
6.4%
5 2235
 
5.8%
6 2054
 
5.4%
7 1953
 
5.1%
8 1896
 
4.9%
Other values (2) 1773
 
4.6%
Latin
ValueCountFrequency (%)
N 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9679
25.2%
0 5457
14.2%
1 4456
11.6%
2 3446
 
9.0%
3 2951
 
7.7%
4 2435
 
6.3%
5 2235
 
5.8%
6 2054
 
5.4%
7 1953
 
5.1%
8 1896
 
4.9%
Other values (3) 1803
 
4.7%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8857
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9002.7844
Minimum0
Maximum236617.33
Zeros250
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:41.148829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile495.8745
Q11910
median4860.88
Q311222.392
95-th percentile30512.028
Maximum236617.33
Range236617.33
Interquartile range (IQR)9312.3925

Descriptive statistics

Standard deviation12646.371
Coefficient of variation (CV)1.4047177
Kurtosis40.384456
Mean9002.7844
Median Absolute Deviation (MAD)3567.21
Skewness4.705404
Sum90027844
Variance1.5993069 × 108
MonotonicityNot monotonic
2024-03-14T01:25:41.258247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 250
 
2.5%
111.2 10
 
0.1%
1670.0 10
 
0.1%
1600.0 9
 
0.1%
970.0 8
 
0.1%
1320.0 8
 
0.1%
1360.0 8
 
0.1%
1130.0 8
 
0.1%
810.0 8
 
0.1%
2340.0 7
 
0.1%
Other values (8847) 9674
96.7%
ValueCountFrequency (%)
0.0 250
2.5%
0.29 1
 
< 0.1%
10.0 2
 
< 0.1%
20.0 1
 
< 0.1%
70.0 1
 
< 0.1%
80.0 1
 
< 0.1%
88.12 1
 
< 0.1%
88.13 3
 
< 0.1%
88.16 1
 
< 0.1%
88.17 1
 
< 0.1%
ValueCountFrequency (%)
236617.33 1
< 0.1%
177841.7 1
< 0.1%
169711.84 1
< 0.1%
165355.53 1
< 0.1%
158953.56 1
< 0.1%
153954.03 1
< 0.1%
151651.09 1
< 0.1%
145280.36 1
< 0.1%
137367.83 1
< 0.1%
135663.27 1
< 0.1%

이동시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct498
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.2013
Minimum0
Maximum1747
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:41.356571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q118
median46
Q398
95-th percentile247
Maximum1747
Range1747
Interquartile range (IQR)80

Descriptive statistics

Standard deviation98.714121
Coefficient of variation (CV)1.2954388
Kurtosis40.566034
Mean76.2013
Median Absolute Deviation (MAD)33
Skewness4.5258637
Sum762013
Variance9744.4776
MonotonicityNot monotonic
2024-03-14T01:25:41.468454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 191
 
1.9%
10 190
 
1.9%
7 186
 
1.9%
9 180
 
1.8%
12 174
 
1.7%
11 174
 
1.7%
6 165
 
1.7%
5 158
 
1.6%
4 147
 
1.5%
13 147
 
1.5%
Other values (488) 8288
82.9%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 20
 
0.2%
2 73
 
0.7%
3 123
1.2%
4 147
1.5%
5 158
1.6%
6 165
1.7%
7 186
1.9%
8 191
1.9%
9 180
1.8%
ValueCountFrequency (%)
1747 1
< 0.1%
1603 1
< 0.1%
1572 1
< 0.1%
1511 1
< 0.1%
1336 1
< 0.1%
1241 1
< 0.1%
1146 1
< 0.1%
1143 1
< 0.1%
1072 1
< 0.1%
908 1
< 0.1%

Interactions

2024-03-14T01:25:36.957556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.017807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.349813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.634765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:37.034680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.106493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.419651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.714391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:37.108150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.195829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.485160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.803366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:37.177312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.272583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.561166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:36.874933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:25:41.544964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이동시간(분)
대여소번호1.0000.0170.0000.0670.0620.1040.123
대여구분코드0.0171.0000.2340.3080.1530.0440.129
성별0.0000.2341.0000.1160.0730.0450.056
연령대코드0.0670.3080.1161.0000.1880.1110.109
이용건수0.0620.1530.0730.1881.0000.8620.740
이동거리(M)0.1040.0440.0450.1110.8621.0000.867
이동시간(분)0.1230.1290.0560.1090.7400.8671.000
2024-03-14T01:25:41.632676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.0890.195
성별0.0891.0000.071
연령대코드0.1950.0711.000
2024-03-14T01:25:41.704851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이동시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.051-0.088-0.0980.0070.0000.032
이용건수-0.0511.0000.6940.7190.0880.0420.093
이동거리(M)-0.0880.6941.0000.8720.0250.0260.054
이동시간(분)-0.0980.7190.8721.0000.0540.0230.052
대여구분코드0.0070.0880.0250.0541.0000.0890.195
성별0.0000.0420.0260.0230.0891.0000.071
연령대코드0.0320.0930.0540.0520.1950.0711.000

Missing values

2024-03-14T01:25:37.281018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:25:37.410778image/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)이동시간(분)
50902021-06-01387387. 훈련원공원주차장 앞정기FAGE_008127.70.251076.2510
44732021-06-01345345. 서울보증보험본사 앞정기FAGE_0045180.721.657156.85237
25672021-06-01240240. 문래역 4번출구 앞정기<NA>AGE_003217.150.14610.08
93422021-06-01667667.청량차도 육교 밑정기FAGE_0041141.541.185105.89118
13022021-06-01176176. 명지대학교 도서관일일(회원)FAGE_0021248.281.556670.059
74252021-06-01548548. 자양나들목정기MAGE_0012104.510.93847.1220
97082021-06-01703703. 오목교역 7번출구 앞정기MAGE_0048617.724.8220775.96187
90452021-06-01646646. 장한평역 1번출구 (국민은행앞)정기FAGE_0035391.653.3214310.8494
65032021-06-01490490.가온문화공원일일(회원)\NAGE_0041000.02
64092021-06-01481481.신당역 10번 출구정기\NAGE_007146.510.421807.0812
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이동시간(분)
60622021-06-01451451. 청와대앞길정기MAGE_004254.570.532272.7511
1642021-06-01108108. 서교동 사거리정기FAGE_0035333.32.6811589.7124
47062021-06-01361361. 동묘앞역 1번출구 뒤정기\NAGE_0039664.745.6524344.6421
87172021-06-01626626. 군자교 서측 녹지대정기MAGE_0043201.051.566723.73126
11742021-06-01165165. 중앙근린공원정기MAGE_0047490.44.2618377.46179
73462021-06-01543543. 구의공원(테크노마트 앞)정기\NAGE_003338.260.251106.7313
322021-06-01103103. 망원역 2번출구 앞일일(회원)\NAGE_0012390.143.5215156.7770
100682021-06-01726726. 목동3단지 시내버스정류장정기\NAGE_002211215.2610.6345802.52321
85592021-06-01615615. 용두동 래미안허브리츠아파트 앞일일(회원)<NA>AGE_0021000.050
59062021-06-01438438. 성산2-1 공영주차장정기MAGE_006143.30.361540.011