Overview

Dataset statistics

Number of variables11
Number of observations7179
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory645.1 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
이동거리(M) has 324 (4.5%) zerosZeros

Reproduction

Analysis started2024-05-18 04:58:15.956317
Analysis finished2024-05-18 04:58:23.690550
Duration7.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
2022-04-01
7179 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-01
2nd row2022-04-01
3rd row2022-04-01
4th row2022-04-01
5th row2022-04-01

Common Values

ValueCountFrequency (%)
2022-04-01 7179
100.0%

Length

2024-05-18T13:58:24.050830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:58:24.345524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-04-01 7179
100.0%

대여소번호
Real number (ℝ)

Distinct348
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.01727
Minimum5
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-05-18T13:58:24.710649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile117
Q1202
median287
Q3406
95-th percentile500
Maximum516
Range511
Interquartile range (IQR)204

Descriptive statistics

Standard deviation120.98478
Coefficient of variation (CV)0.40325938
Kurtosis-1.1472923
Mean300.01727
Median Absolute Deviation (MAD)102
Skewness0.13814806
Sum2153824
Variance14637.317
MonotonicityIncreasing
2024-05-18T13:58:25.230877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 50
 
0.7%
207 49
 
0.7%
247 40
 
0.6%
210 40
 
0.6%
272 38
 
0.5%
230 37
 
0.5%
249 36
 
0.5%
146 36
 
0.5%
106 35
 
0.5%
148 35
 
0.5%
Other values (338) 6783
94.5%
ValueCountFrequency (%)
5 2
 
< 0.1%
102 35
0.5%
103 24
0.3%
104 25
0.3%
105 18
0.3%
106 35
0.5%
107 18
0.3%
108 20
0.3%
109 18
0.3%
111 17
0.2%
ValueCountFrequency (%)
516 11
 
0.2%
515 15
0.2%
514 29
0.4%
513 22
0.3%
512 28
0.4%
511 25
0.3%
510 28
0.4%
509 32
0.4%
508 32
0.4%
507 21
0.3%

대여소
Text

Distinct348
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
2024-05-18T13:58:25.828166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length14.426661
Min length7

Characters and Unicode

Total characters103569
Distinct characters332
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

Unique0 ?
Unique (%)0.0%

Sample

1st row상암센터 정비실
2nd row상암센터 정비실
3rd row102. 망원역 1번출구 앞
4th row102. 망원역 1번출구 앞
5th row102. 망원역 1번출구 앞
ValueCountFrequency (%)
앞 2818
 
12.3%
옆 703
 
3.1%
1번출구 437
 
1.9%
사거리 295
 
1.3%
뒤 276
 
1.2%
2번출구 255
 
1.1%
출구 221
 
1.0%
5번출구 188
 
0.8%
3번출구 183
 
0.8%
4번출구 174
 
0.8%
Other values (742) 17309
75.7%
2024-05-18T13:58:26.962303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15778
 
15.2%
. 7177
 
6.9%
2 4257
 
4.1%
1 4217
 
4.1%
4 3328
 
3.2%
3 3300
 
3.2%
앞 2923
 
2.8%
구 2768
 
2.7%
역 2227
 
2.2%
번 2108
 
2.0%
Other values (322) 55486
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53882
52.0%
Decimal Number 24836
24.0%
Space Separator 15778
 
15.2%
Other Punctuation 7177
 
6.9%
Uppercase Letter 1316
 
1.3%
Close Punctuation 269
 
0.3%
Open Punctuation 269
 
0.3%
Dash Punctuation 42
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 2923
 
5.4%
구 2768
 
5.1%
역 2227
 
4.1%
번 2108
 
3.9%
출 2080
 
3.9%
동 1026
 
1.9%
원 946
 
1.8%
사 866
 
1.6%
교 858
 
1.6%
리 850
 
1.6%
Other values (288) 37230
69.1%
Uppercase Letter
ValueCountFrequency (%)
C 210
16.0%
K 190
14.4%
D 173
13.1%
M 139
10.6%
S 128
9.7%
B 67
 
5.1%
T 65
 
4.9%
E 56
 
4.3%
F 40
 
3.0%
I 40
 
3.0%
Other values (9) 208
15.8%
Decimal Number
ValueCountFrequency (%)
2 4257
17.1%
1 4217
17.0%
4 3328
13.4%
3 3300
13.3%
5 2041
8.2%
0 1790
7.2%
8 1571
 
6.3%
7 1496
 
6.0%
6 1419
 
5.7%
9 1417
 
5.7%
Space Separator
ValueCountFrequency (%)
15778
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7177
100.0%
Close Punctuation
ValueCountFrequency (%)
) 269
100.0%
Open Punctuation
ValueCountFrequency (%)
( 269
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53882
52.0%
Common 48371
46.7%
Latin 1316
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 2923
 
5.4%
구 2768
 
5.1%
역 2227
 
4.1%
번 2108
 
3.9%
출 2080
 
3.9%
동 1026
 
1.9%
원 946
 
1.8%
사 866
 
1.6%
교 858
 
1.6%
리 850
 
1.6%
Other values (288) 37230
69.1%
Latin
ValueCountFrequency (%)
C 210
16.0%
K 190
14.4%
D 173
13.1%
M 139
10.6%
S 128
9.7%
B 67
 
5.1%
T 65
 
4.9%
E 56
 
4.3%
F 40
 
3.0%
I 40
 
3.0%
Other values (9) 208
15.8%
Common
ValueCountFrequency (%)
15778
32.6%
. 7177
14.8%
2 4257
 
8.8%
1 4217
 
8.7%
4 3328
 
6.9%
3 3300
 
6.8%
5 2041
 
4.2%
0 1790
 
3.7%
8 1571
 
3.2%
7 1496
 
3.1%
Other values (5) 3416
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53882
52.0%
ASCII 49687
48.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15778
31.8%
. 7177
14.4%
2 4257
 
8.6%
1 4217
 
8.5%
4 3328
 
6.7%
3 3300
 
6.6%
5 2041
 
4.1%
0 1790
 
3.6%
8 1571
 
3.2%
7 1496
 
3.0%
Other values (24) 4732
 
9.5%
Hangul
ValueCountFrequency (%)
앞 2923
 
5.4%
구 2768
 
5.1%
역 2227
 
4.1%
번 2108
 
3.9%
출 2080
 
3.9%
동 1026
 
1.9%
원 946
 
1.8%
사 866
 
1.6%
교 858
 
1.6%
리 850
 
1.6%
Other values (288) 37230
69.1%

대여구분코드
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
정기
4948 
일일(회원)
1995 
일일(비회원)
 
155
단체
 
81

Length

Max length7
Median length2
Mean length3.2195292
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 4948
68.9%
일일(회원) 1995
27.8%
일일(비회원) 155
 
2.2%
단체 81
 
1.1%

Length

2024-05-18T13:58:27.382680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:58:27.730946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 4948
68.9%
일일(회원 1995
27.8%
일일(비회원 155
 
2.2%
단체 81
 
1.1%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
M
2537 
\N
2137 
F
1994 
<NA>
509 
m
 
2

Length

Max length4
Median length1
Mean length1.5103775
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 2537
35.3%
\N 2137
29.8%
F 1994
27.8%
<NA> 509
 
7.1%
m 2
 
< 0.1%

Length

2024-05-18T13:58:28.223974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:58:28.627326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 2539
35.4%
n 2137
29.8%
f 1994
27.8%
na 509
 
7.1%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
20대
1875 
30대
1526 
40대
1092 
기타
975 
50대
803 
Other values (3)
908 

Length

Max length5
Median length3
Mean length2.8900961
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30대
2nd row50대
3rd row기타
4th row기타
5th row10대

Common Values

ValueCountFrequency (%)
20대 1875
26.1%
30대 1526
21.3%
40대 1092
15.2%
기타 975
13.6%
50대 803
11.2%
60대 419
 
5.8%
10대 396
 
5.5%
70대이상 93
 
1.3%

Length

2024-05-18T13:58:29.115918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:58:29.482933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 1875
26.1%
30대 1526
21.3%
40대 1092
15.2%
기타 975
13.6%
50대 803
11.2%
60대 419
 
5.8%
10대 396
 
5.5%
70대이상 93
 
1.3%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9020755
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-05-18T13:58:29.888075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum80
Range79
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4504444
Coefficient of variation (CV)1.1889575
Kurtosis100.79894
Mean2.9020755
Median Absolute Deviation (MAD)1
Skewness7.1080297
Sum20834
Variance11.905567
MonotonicityNot monotonic
2024-05-18T13:58:30.402267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 3025
42.1%
2 1513
21.1%
3 888
 
12.4%
4 545
 
7.6%
5 323
 
4.5%
6 229
 
3.2%
7 186
 
2.6%
8 121
 
1.7%
9 94
 
1.3%
10 65
 
0.9%
Other values (30) 190
 
2.6%
ValueCountFrequency (%)
1 3025
42.1%
2 1513
21.1%
3 888
 
12.4%
4 545
 
7.6%
5 323
 
4.5%
6 229
 
3.2%
7 186
 
2.6%
8 121
 
1.7%
9 94
 
1.3%
10 65
 
0.9%
ValueCountFrequency (%)
80 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
62 1
< 0.1%
54 1
< 0.1%
50 1
< 0.1%
49 1
< 0.1%
44 1
< 0.1%
35 1
< 0.1%
33 1
< 0.1%

운동량
Text

Distinct6128
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
2024-05-18T13:58:31.278021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.5000696
Min length2

Characters and Unicode

Total characters39485
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

Unique5526 ?
Unique (%)77.0%

Sample

1st row35.79
2nd row89.39
3rd row606.00
4th row1781.66
5th row23.26
ValueCountFrequency (%)
0.00 309
 
4.3%
n 22
 
0.3%
51.22 6
 
0.1%
30.89 5
 
0.1%
6.18 5
 
0.1%
24.20 5
 
0.1%
28.83 5
 
0.1%
54.05 5
 
0.1%
48.13 4
 
0.1%
201.39 4
 
0.1%
Other values (6118) 6809
94.8%
2024-05-18T13:58:32.729891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 7157
18.1%
1 4571
11.6%
2 3779
9.6%
0 3529
8.9%
3 3369
8.5%
4 3090
7.8%
5 2974
7.5%
6 2830
 
7.2%
7 2761
 
7.0%
8 2700
 
6.8%
Other values (3) 2725
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32284
81.8%
Other Punctuation 7179
 
18.2%
Uppercase Letter 22
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4571
14.2%
2 3779
11.7%
0 3529
10.9%
3 3369
10.4%
4 3090
9.6%
5 2974
9.2%
6 2830
8.8%
7 2761
8.6%
8 2700
8.4%
9 2681
8.3%
Other Punctuation
ValueCountFrequency (%)
. 7157
99.7%
\ 22
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39463
99.9%
Latin 22
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7157
18.1%
1 4571
11.6%
2 3779
9.6%
0 3529
8.9%
3 3369
8.5%
4 3090
7.8%
5 2974
7.5%
6 2830
 
7.2%
7 2761
 
7.0%
8 2700
 
6.8%
Other values (2) 2703
 
6.8%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7157
18.1%
1 4571
11.6%
2 3779
9.6%
0 3529
8.9%
3 3369
8.5%
4 3090
7.8%
5 2974
7.5%
6 2830
 
7.2%
7 2761
 
7.0%
8 2700
 
6.8%
Other values (3) 2725
 
6.9%

탄소량
Text

Distinct893
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
2024-05-18T13:58:33.642751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0123973
Min length2

Characters and Unicode

Total characters28805
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

Unique284 ?
Unique (%)4.0%

Sample

1st row0.34
2nd row1.05
3rd row6.28
4th row16.05
5th row0.21
ValueCountFrequency (%)
0.00 311
 
4.3%
0.35 56
 
0.8%
0.32 56
 
0.8%
0.29 55
 
0.8%
0.18 53
 
0.7%
0.25 52
 
0.7%
0.22 49
 
0.7%
0.36 49
 
0.7%
0.24 48
 
0.7%
0.19 48
 
0.7%
Other values (883) 6402
89.2%
2024-05-18T13:58:35.221866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 7157
24.8%
0 5411
18.8%
1 3141
10.9%
2 2358
 
8.2%
3 2018
 
7.0%
4 1764
 
6.1%
5 1597
 
5.5%
6 1430
 
5.0%
7 1354
 
4.7%
8 1290
 
4.5%
Other values (3) 1285
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21604
75.0%
Other Punctuation 7179
 
24.9%
Uppercase Letter 22
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5411
25.0%
1 3141
14.5%
2 2358
10.9%
3 2018
 
9.3%
4 1764
 
8.2%
5 1597
 
7.4%
6 1430
 
6.6%
7 1354
 
6.3%
8 1290
 
6.0%
9 1241
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 7157
99.7%
\ 22
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28783
99.9%
Latin 22
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7157
24.9%
0 5411
18.8%
1 3141
10.9%
2 2358
 
8.2%
3 2018
 
7.0%
4 1764
 
6.1%
5 1597
 
5.5%
6 1430
 
5.0%
7 1354
 
4.7%
8 1290
 
4.5%
Other values (2) 1263
 
4.4%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7157
24.8%
0 5411
18.8%
1 3141
10.9%
2 2358
 
8.2%
3 2018
 
7.0%
4 1764
 
6.1%
5 1597
 
5.5%
6 1430
 
5.0%
7 1354
 
4.7%
8 1290
 
4.5%
Other values (3) 1285
 
4.5%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5659
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8471.1518
Minimum0
Maximum416434.93
Zeros324
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-05-18T13:58:36.063813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile190
Q11739.975
median4520
Q310156.315
95-th percentile27875.608
Maximum416434.93
Range416434.93
Interquartile range (IQR)8416.34

Descriptive statistics

Standard deviation15275.535
Coefficient of variation (CV)1.8032418
Kurtosis226.4569
Mean8471.1518
Median Absolute Deviation (MAD)3315.95
Skewness11.482895
Sum60814399
Variance2.3334196 × 108
MonotonicityNot monotonic
2024-05-18T13:58:36.690872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 324
 
4.5%
1070.0 11
 
0.2%
1030.0 10
 
0.1%
940.0 10
 
0.1%
840.0 10
 
0.1%
480.0 9
 
0.1%
1050.0 9
 
0.1%
690.0 9
 
0.1%
1270.0 9
 
0.1%
1520.0 9
 
0.1%
Other values (5649) 6769
94.3%
ValueCountFrequency (%)
0.0 324
4.5%
0.1 3
 
< 0.1%
10.0 3
 
< 0.1%
10.67 1
 
< 0.1%
20.0 2
 
< 0.1%
30.0 3
 
< 0.1%
40.0 1
 
< 0.1%
70.0 3
 
< 0.1%
88.13 2
 
< 0.1%
111.2 1
 
< 0.1%
ValueCountFrequency (%)
416434.93 1
< 0.1%
404541.24 1
< 0.1%
316744.5 1
< 0.1%
312238.71 1
< 0.1%
279742.48 1
< 0.1%
264014.33 1
< 0.1%
249803.31 1
< 0.1%
227270.79 1
< 0.1%
183519.74 1
< 0.1%
164100.57 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct452
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.914891
Minimum0
Maximum4096
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.2 KiB
2024-05-18T13:58:37.684095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median44
Q393
95-th percentile245
Maximum4096
Range4096
Interquartile range (IQR)77

Descriptive statistics

Standard deviation144.10479
Coefficient of variation (CV)1.8735616
Kurtosis288.59124
Mean76.914891
Median Absolute Deviation (MAD)32
Skewness13.479244
Sum552172
Variance20766.19
MonotonicityNot monotonic
2024-05-18T13:58:38.301259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 153
 
2.1%
9 149
 
2.1%
8 139
 
1.9%
10 136
 
1.9%
5 135
 
1.9%
13 132
 
1.8%
7 129
 
1.8%
12 119
 
1.7%
4 119
 
1.7%
11 114
 
1.6%
Other values (442) 5854
81.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 24
 
0.3%
2 67
0.9%
3 99
1.4%
4 119
1.7%
5 135
1.9%
6 153
2.1%
7 129
1.8%
8 139
1.9%
9 149
2.1%
ValueCountFrequency (%)
4096 1
< 0.1%
3729 1
< 0.1%
3679 1
< 0.1%
3054 1
< 0.1%
2927 1
< 0.1%
2706 1
< 0.1%
2701 1
< 0.1%
2346 1
< 0.1%
1690 1
< 0.1%
1469 1
< 0.1%

Interactions

2024-05-18T13:58:21.408821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:17.833390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:19.015227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:20.296110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:21.673877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:18.126392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:19.347948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:20.574881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:22.211738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:18.467901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:19.674517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:20.891484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:22.476351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:18.722369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:19.966001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:58:21.145014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T13:58:38.700011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0710.0000.0000.0800.0850.095
대여구분코드0.0711.0000.3060.5340.1210.1010.116
성별0.0000.3061.0000.1310.0290.0000.030
연령대코드0.0000.5340.1311.0000.1040.0340.053
이용건수0.0800.1210.0290.1041.0000.8960.959
이동거리(M)0.0850.1010.0000.0340.8961.0000.913
이용시간(분)0.0950.1160.0300.0530.9590.9131.000
2024-05-18T13:58:39.025646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0590.261
성별0.0591.0000.124
대여구분코드0.2610.1241.000
2024-05-18T13:58:39.309011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.046-0.100-0.0900.0420.0000.000
이용건수-0.0461.0000.6840.6990.0730.0170.050
이동거리(M)-0.1000.6841.0000.8830.0640.0000.016
이용시간(분)-0.0900.6990.8831.0000.0690.0180.025
대여구분코드0.0420.0730.0640.0691.0000.1240.261
성별0.0000.0170.0000.0180.1241.0000.059
연령대코드0.0000.0500.0160.0250.2610.0591.000

Missing values

2024-05-18T13:58:22.907555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T13:58:23.423782image/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)
02022-04-015상암센터 정비실정기F30대135.790.341481.6411
12022-04-015상암센터 정비실정기M50대189.391.054514.8725
22022-04-01102102. 망원역 1번출구 앞단체M기타7606.006.2827056.32211
32022-04-01102102. 망원역 1번출구 앞일일(비회원)\N기타61781.6616.0569216.96455
42022-04-01102102. 망원역 1번출구 앞일일(회원)\N10대123.260.21890.04
52022-04-01102102. 망원역 1번출구 앞일일(회원)\N20대4535.525.2222486.39143
62022-04-01102102. 망원역 1번출구 앞일일(회원)\N기타1308.643.0112990.078
72022-04-01102102. 망원역 1번출구 앞일일(회원)<NA>30대1180.101.626996.9247
82022-04-01102102. 망원역 1번출구 앞일일(회원)F20대111119.1310.7246259.29499
92022-04-01102102. 망원역 1번출구 앞일일(회원)F30대3453.184.3118564.78181
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
71692022-04-01516516. 광진메디칼 앞일일(회원)\N30대1194.951.275470.043
71702022-04-01516516. 광진메디칼 앞일일(회원)F20대3236.252.3410077.65108
71712022-04-01516516. 광진메디칼 앞일일(회원)F30대154.050.492100.015
71722022-04-01516516. 광진메디칼 앞일일(회원)F기타1190.991.245358.75152
71732022-04-01516516. 광진메디칼 앞일일(회원)M20대2107.720.833583.8430
71742022-04-01516516. 광진메디칼 앞정기\N20대10292.664.5419603.55113
71752022-04-01516516. 광진메디칼 앞정기\N30대143.340.251094.419
71762022-04-01516516. 광진메디칼 앞정기\N40대117.000.12530.02
71772022-04-01516516. 광진메디칼 앞정기\N50대3227.132.088961.9775
71782022-04-01516516. 광진메디칼 앞정기\N기타10.000.000.09