Overview

Dataset statistics

Number of variables11
Number of observations3454
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory310.4 KiB
Average record size in memory92.0 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
이동거리(M) is highly overall correlated with 이용시간(분)High correlation
이용시간(분) is highly overall correlated with 이동거리(M)High correlation
대여구분코드 is highly overall correlated with 연령대코드High correlation
연령대코드 is highly overall correlated with 대여구분코드High correlation
대여구분코드 is highly imbalanced (51.9%)Imbalance
이동거리(M) has 172 (5.0%) zerosZeros

Reproduction

Analysis started2024-03-13 16:23:30.568592
Analysis finished2024-03-13 16:23:32.723270
Duration2.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
Minimum2020-01-01 00:00:00
Maximum2020-01-01 00:00:00
2024-03-14T01:23:32.761177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:32.829606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct446
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.21395
Minimum101
Maximum659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.5 KiB
2024-03-14T01:23:32.915521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile114
Q1207
median338
Q3521
95-th percentile631
Maximum659
Range558
Interquartile range (IQR)314

Descriptive statistics

Standard deviation170.97384
Coefficient of variation (CV)0.48132636
Kurtosis-1.2664638
Mean355.21395
Median Absolute Deviation (MAD)155.5
Skewness0.20712107
Sum1226909
Variance29232.054
MonotonicityIncreasing
2024-03-14T01:23:33.021219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 24
 
0.7%
602 23
 
0.7%
114 22
 
0.6%
133 20
 
0.6%
131 19
 
0.6%
284 19
 
0.6%
565 19
 
0.6%
419 18
 
0.5%
505 18
 
0.5%
274 18
 
0.5%
Other values (436) 3254
94.2%
ValueCountFrequency (%)
101 7
0.2%
102 14
0.4%
103 13
0.4%
104 16
0.5%
105 10
0.3%
106 15
0.4%
107 10
0.3%
108 12
0.3%
109 14
0.4%
110 8
0.2%
ValueCountFrequency (%)
659 9
0.3%
658 5
 
0.1%
657 9
0.3%
656 9
0.3%
654 8
0.2%
652 3
 
0.1%
651 5
 
0.1%
650 12
0.3%
648 14
0.4%
647 3
 
0.1%

대여소
Text

Distinct446
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
2024-03-14T01:23:33.204487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length14.636943
Min length8

Characters and Unicode

Total characters50556
Distinct characters356
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

Unique19 ?
Unique (%)0.6%

Sample

1st row101. (구)합정동 주민센터
2nd row101. (구)합정동 주민센터
3rd row101. (구)합정동 주민센터
4th row101. (구)합정동 주민센터
5th row101. (구)합정동 주민센터
ValueCountFrequency (%)
앞 1379
 
12.2%
옆 289
 
2.5%
1번출구 185
 
1.6%
사거리 176
 
1.6%
2번출구 166
 
1.5%
뒤 125
 
1.1%
4번출구 107
 
0.9%
출구 105
 
0.9%
3번출구 95
 
0.8%
건너편 93
 
0.8%
Other values (972) 8614
76.0%
2024-03-14T01:23:33.518122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7923
 
15.7%
. 3454
 
6.8%
1 2050
 
4.1%
2 1826
 
3.6%
3 1535
 
3.0%
5 1490
 
2.9%
앞 1435
 
2.8%
구 1371
 
2.7%
4 1331
 
2.6%
역 1118
 
2.2%
Other values (346) 27023
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26396
52.2%
Decimal Number 11933
23.6%
Space Separator 7923
 
15.7%
Other Punctuation 3454
 
6.8%
Uppercase Letter 460
 
0.9%
Close Punctuation 184
 
0.4%
Open Punctuation 184
 
0.4%
Dash Punctuation 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 1435
 
5.4%
구 1371
 
5.2%
역 1118
 
4.2%
번 1042
 
3.9%
출 1037
 
3.9%
리 567
 
2.1%
동 536
 
2.0%
교 497
 
1.9%
원 452
 
1.7%
사 428
 
1.6%
Other values (312) 17913
67.9%
Uppercase Letter
ValueCountFrequency (%)
K 81
17.6%
S 67
14.6%
C 64
13.9%
D 41
8.9%
M 41
8.9%
B 27
 
5.9%
I 21
 
4.6%
E 21
 
4.6%
T 18
 
3.9%
N 17
 
3.7%
Other values (9) 62
13.5%
Decimal Number
ValueCountFrequency (%)
1 2050
17.2%
2 1826
15.3%
3 1535
12.9%
5 1490
12.5%
4 1331
11.2%
6 1018
8.5%
0 795
 
6.7%
8 740
 
6.2%
7 632
 
5.3%
9 516
 
4.3%
Space Separator
ValueCountFrequency (%)
7923
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3454
100.0%
Close Punctuation
ValueCountFrequency (%)
) 184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 184
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26396
52.2%
Common 23700
46.9%
Latin 460
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 1435
 
5.4%
구 1371
 
5.2%
역 1118
 
4.2%
번 1042
 
3.9%
출 1037
 
3.9%
리 567
 
2.1%
동 536
 
2.0%
교 497
 
1.9%
원 452
 
1.7%
사 428
 
1.6%
Other values (312) 17913
67.9%
Latin
ValueCountFrequency (%)
K 81
17.6%
S 67
14.6%
C 64
13.9%
D 41
8.9%
M 41
8.9%
B 27
 
5.9%
I 21
 
4.6%
E 21
 
4.6%
T 18
 
3.9%
N 17
 
3.7%
Other values (9) 62
13.5%
Common
ValueCountFrequency (%)
7923
33.4%
. 3454
14.6%
1 2050
 
8.6%
2 1826
 
7.7%
3 1535
 
6.5%
5 1490
 
6.3%
4 1331
 
5.6%
6 1018
 
4.3%
0 795
 
3.4%
8 740
 
3.1%
Other values (5) 1538
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26396
52.2%
ASCII 24160
47.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7923
32.8%
. 3454
14.3%
1 2050
 
8.5%
2 1826
 
7.6%
3 1535
 
6.4%
5 1490
 
6.2%
4 1331
 
5.5%
6 1018
 
4.2%
0 795
 
3.3%
8 740
 
3.1%
Other values (24) 1998
 
8.3%
Hangul
ValueCountFrequency (%)
앞 1435
 
5.4%
구 1371
 
5.2%
역 1118
 
4.2%
번 1042
 
3.9%
출 1037
 
3.9%
리 567
 
2.1%
동 536
 
2.0%
교 497
 
1.9%
원 452
 
1.7%
사 428
 
1.6%
Other values (312) 17913
67.9%

대여구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
정기
2614 
일일(회원)
733 
일일(비회원)
 
72
단체
 
35

Length

Max length7
Median length2
Mean length2.9530979
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 2614
75.7%
일일(회원) 733
 
21.2%
일일(비회원) 72
 
2.1%
단체 35
 
1.0%

Length

2024-03-14T01:23:33.642344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:23:33.721798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 2614
75.7%
일일(회원 733
 
21.2%
일일(비회원 72
 
2.1%
단체 35
 
1.0%

성별
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
\N
1291 
M
1204 
F
636 
<NA>
319 
m
 
3

Length

Max length4
Median length1
Mean length1.6508396
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
\N 1291
37.4%
M 1204
34.9%
F 636
18.4%
<NA> 319
 
9.2%
m 3
 
0.1%
f 1
 
< 0.1%

Length

2024-03-14T01:23:33.835219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:23:33.942621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 1291
37.4%
m 1207
34.9%
f 637
18.4%
na 319
 
9.2%

연령대코드
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
AGE_002
1300 
AGE_003
825 
AGE_004
510 
AGE_005
362 
AGE_001
186 
Other values (3)
271 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
AGE_002 1300
37.6%
AGE_003 825
23.9%
AGE_004 510
 
14.8%
AGE_005 362
 
10.5%
AGE_001 186
 
5.4%
AGE_006 130
 
3.8%
AGE_008 106
 
3.1%
AGE_007 35
 
1.0%

Length

2024-03-14T01:23:34.034307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:23:34.122540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 1300
37.6%
age_003 825
23.9%
age_004 510
 
14.8%
age_005 362
 
10.5%
age_001 186
 
5.4%
age_006 130
 
3.8%
age_008 106
 
3.1%
age_007 35
 
1.0%

이용건수
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.589172
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.5 KiB
2024-03-14T01:23:34.216377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0662985
Coefficient of variation (CV)0.67097741
Kurtosis9.8380946
Mean1.589172
Median Absolute Deviation (MAD)0
Skewness2.7001771
Sum5489
Variance1.1369925
MonotonicityNot monotonic
2024-03-14T01:23:34.293496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 2262
65.5%
2 721
 
20.9%
3 284
 
8.2%
4 86
 
2.5%
5 54
 
1.6%
6 25
 
0.7%
7 14
 
0.4%
9 3
 
0.1%
8 3
 
0.1%
10 2
 
0.1%
ValueCountFrequency (%)
1 2262
65.5%
2 721
 
20.9%
3 284
 
8.2%
4 86
 
2.5%
5 54
 
1.6%
6 25
 
0.7%
7 14
 
0.4%
8 3
 
0.1%
9 3
 
0.1%
10 2
 
0.1%
ValueCountFrequency (%)
10 2
 
0.1%
9 3
 
0.1%
8 3
 
0.1%
7 14
 
0.4%
6 25
 
0.7%
5 54
 
1.6%
4 86
 
2.5%
3 284
 
8.2%
2 721
 
20.9%
1 2262
65.5%

운동량
Text

Distinct2741
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
2024-03-14T01:23:34.580019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.3462652
Min length2

Characters and Unicode

Total characters18466
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

Unique2346 ?
Unique (%)67.9%

Sample

1st row0.00
2nd row80.54
3rd row72.07
4th row157.56
5th row39.99
ValueCountFrequency (%)
0.00 163
 
4.7%
n 9
 
0.3%
38.61 7
 
0.2%
35.01 6
 
0.2%
19.31 6
 
0.2%
43.24 6
 
0.2%
35.78 6
 
0.2%
18.53 5
 
0.1%
54.05 5
 
0.1%
44.53 5
 
0.1%
Other values (2731) 3236
93.7%
2024-03-14T01:23:35.025116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3445
18.7%
1 2118
11.5%
2 1716
9.3%
0 1638
8.9%
3 1605
8.7%
4 1429
7.7%
5 1413
7.7%
6 1357
 
7.3%
7 1289
 
7.0%
8 1275
 
6.9%
Other values (3) 1181
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15003
81.2%
Other Punctuation 3454
 
18.7%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2118
14.1%
2 1716
11.4%
0 1638
10.9%
3 1605
10.7%
4 1429
9.5%
5 1413
9.4%
6 1357
9.0%
7 1289
8.6%
8 1275
8.5%
9 1163
7.8%
Other Punctuation
ValueCountFrequency (%)
. 3445
99.7%
\ 9
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18457
> 99.9%
Latin 9
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3445
18.7%
1 2118
11.5%
2 1716
9.3%
0 1638
8.9%
3 1605
8.7%
4 1429
7.7%
5 1413
7.7%
6 1357
 
7.4%
7 1289
 
7.0%
8 1275
 
6.9%
Other values (2) 1172
 
6.3%
Latin
ValueCountFrequency (%)
N 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3445
18.7%
1 2118
11.5%
2 1716
9.3%
0 1638
8.9%
3 1605
8.7%
4 1429
7.7%
5 1413
7.7%
6 1357
 
7.3%
7 1289
 
7.0%
8 1275
 
6.9%
Other values (3) 1181
 
6.4%

탄소량
Text

Distinct559
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
2024-03-14T01:23:35.364650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0199768
Min length2

Characters and Unicode

Total characters13885
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

Unique260 ?
Unique (%)7.5%

Sample

1st row0.00
2nd row0.75
3rd row0.75
4th row1.59
5th row0.23
ValueCountFrequency (%)
0.00 163
 
4.7%
0.26 57
 
1.7%
0.23 47
 
1.4%
0.19 42
 
1.2%
0.29 42
 
1.2%
0.17 41
 
1.2%
0.32 41
 
1.2%
0.24 38
 
1.1%
0.39 38
 
1.1%
0.31 38
 
1.1%
Other values (549) 2907
84.2%
2024-03-14T01:23:35.847092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3445
24.8%
0 3123
22.5%
1 1448
10.4%
2 1079
 
7.8%
3 875
 
6.3%
4 788
 
5.7%
5 752
 
5.4%
6 632
 
4.6%
9 595
 
4.3%
7 589
 
4.2%
Other values (3) 559
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10422
75.1%
Other Punctuation 3454
 
24.9%
Uppercase Letter 9
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3123
30.0%
1 1448
13.9%
2 1079
 
10.4%
3 875
 
8.4%
4 788
 
7.6%
5 752
 
7.2%
6 632
 
6.1%
9 595
 
5.7%
7 589
 
5.7%
8 541
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 3445
99.7%
\ 9
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13876
99.9%
Latin 9
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3445
24.8%
0 3123
22.5%
1 1448
10.4%
2 1079
 
7.8%
3 875
 
6.3%
4 788
 
5.7%
5 752
 
5.4%
6 632
 
4.6%
9 595
 
4.3%
7 589
 
4.2%
Other values (2) 550
 
4.0%
Latin
ValueCountFrequency (%)
N 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3445
24.8%
0 3123
22.5%
1 1448
10.4%
2 1079
 
7.8%
3 875
 
6.3%
4 788
 
5.7%
5 752
 
5.4%
6 632
 
4.6%
9 595
 
4.3%
7 589
 
4.2%
Other values (3) 559
 
4.0%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1213
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6598.949
Minimum0
Maximum285620
Zeros172
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size30.5 KiB
2024-03-14T01:23:35.969730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69.5
Q11280
median2685
Q35830
95-th percentile22071
Maximum285620
Range285620
Interquartile range (IQR)4550

Descriptive statistics

Standard deviation16298.625
Coefficient of variation (CV)2.469882
Kurtosis102.61548
Mean6598.949
Median Absolute Deviation (MAD)1705
Skewness8.6863837
Sum22792770
Variance2.6564519 × 108
MonotonicityNot monotonic
2024-03-14T01:23:36.079919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172
 
5.0%
1250 15
 
0.4%
740 15
 
0.4%
980 14
 
0.4%
1110 13
 
0.4%
1000 13
 
0.4%
1470 13
 
0.4%
1280 12
 
0.3%
1390 12
 
0.3%
1100 12
 
0.3%
Other values (1203) 3163
91.6%
ValueCountFrequency (%)
0 172
5.0%
50 1
 
< 0.1%
80 1
 
< 0.1%
100 1
 
< 0.1%
150 1
 
< 0.1%
180 1
 
< 0.1%
190 1
 
< 0.1%
210 1
 
< 0.1%
230 2
 
0.1%
250 3
 
0.1%
ValueCountFrequency (%)
285620 1
< 0.1%
250700 1
< 0.1%
246770 1
< 0.1%
241970 1
< 0.1%
217860 1
< 0.1%
187880 1
< 0.1%
185550 1
< 0.1%
181890 1
< 0.1%
179400 1
< 0.1%
176390 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.877244
Minimum0
Maximum628
Zeros7
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size30.5 KiB
2024-03-14T01:23:36.193294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median20
Q344
95-th percentile119
Maximum628
Range628
Interquartile range (IQR)35

Descriptive statistics

Standard deviation45.446843
Coefficient of variation (CV)1.2667317
Kurtosis28.580455
Mean35.877244
Median Absolute Deviation (MAD)14
Skewness3.9362394
Sum123920
Variance2065.4156
MonotonicityNot monotonic
2024-03-14T01:23:36.300528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 137
 
4.0%
8 130
 
3.8%
4 125
 
3.6%
6 125
 
3.6%
9 117
 
3.4%
13 99
 
2.9%
10 98
 
2.8%
7 93
 
2.7%
3 92
 
2.7%
11 85
 
2.5%
Other values (209) 2353
68.1%
ValueCountFrequency (%)
0 7
 
0.2%
1 13
 
0.4%
2 64
1.9%
3 92
2.7%
4 125
3.6%
5 137
4.0%
6 125
3.6%
7 93
2.7%
8 130
3.8%
9 117
3.4%
ValueCountFrequency (%)
628 1
< 0.1%
616 1
< 0.1%
510 1
< 0.1%
471 1
< 0.1%
373 1
< 0.1%
343 1
< 0.1%
334 1
< 0.1%
327 1
< 0.1%
318 1
< 0.1%
316 1
< 0.1%

Interactions

2024-03-14T01:23:32.199916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.131012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.406185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.682528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:32.268566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.197187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.474329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.764758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:32.334605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.262510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.542902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.830477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:32.405281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.334057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:31.609066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:23:32.125070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:23:36.373637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0000.0000.0450.0680.1140.098
대여구분코드0.0001.0000.1290.8350.1610.0410.203
성별0.0000.1291.0000.2300.1560.0000.000
연령대코드0.0450.8350.2301.0000.1650.0000.111
이용건수0.0680.1610.1560.1651.0000.2980.414
이동거리(M)0.1140.0410.0000.0000.2981.0000.147
이용시간(분)0.0980.2030.0000.1110.4140.1471.000
2024-03-14T01:23:36.453784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.1050.506
성별0.1051.0000.142
연령대코드0.5060.1421.000
2024-03-14T01:23:36.527109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.046-0.065-0.0880.0000.0000.021
이용건수-0.0461.0000.4900.4980.0970.0650.079
이동거리(M)-0.0650.4901.0000.7680.0240.0000.000
이용시간(분)-0.0880.4980.7681.0000.1310.0000.055
대여구분코드0.0000.0970.0240.1311.0000.1050.506
성별0.0000.0650.0000.0000.1051.0000.142
연령대코드0.0210.0790.0000.0550.5060.1421.000

Missing values

2024-03-14T01:23:32.501003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:23:32.648654image/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)
02020-01-01101101. (구)합정동 주민센터일일(회원)\NAGE_00210.000.00029
12020-01-01101101. (구)합정동 주민센터정기\NAGE_002280.540.75324052
22020-01-01101101. (구)합정동 주민센터정기FAGE_002172.070.75325021
32020-01-01101101. (구)합정동 주민센터정기FAGE_0041157.561.59686046
42020-01-01101101. (구)합정동 주민센터정기MAGE_002139.990.239905
52020-01-01101101. (구)합정동 주민센터정기MAGE_005125.480.239905
62020-01-01101101. (구)합정동 주민센터정기MAGE_008117.630.146102
72020-01-01102102. 망원역 1번출구 앞일일(회원)\NAGE_002249.960.35152065
82020-01-01102102. 망원역 1번출구 앞일일(회원)MAGE_0032215.701.91823040
92020-01-01102102. 망원역 1번출구 앞일일(회원)MAGE_0041157.601.38594052
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
34442020-01-01658658. 촬영소 사거리정기MAGE_003182.050.60259023
34452020-01-01659659. 제기역1번출구일일(회원)\NAGE_002120.770.187604
34462020-01-01659659. 제기역1번출구일일(회원)<NA>AGE_001129.510.2510805
34472020-01-01659659. 제기역1번출구정기\NAGE_0024390.883.5715350113
34482020-01-01659659. 제기역1번출구정기\NAGE_003148.230.4017408
34492020-01-01659659. 제기역1번출구정기\NAGE_004267.610.63271024
34502020-01-01659659. 제기역1번출구정기\NAGE_005154.140.34147024
34512020-01-01659659. 제기역1번출구정기\NAGE_008126.670.2191013
34522020-01-01659659. 제기역1번출구정기<NA>AGE_002139.380.3515307
34532020-01-01659659. 제기역1번출구정기FAGE_003117.690.187705