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

Categorical4
Numeric4
Text3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15248/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 508 (5.1%) zerosZeros

Reproduction

Analysis started2024-05-18 00:16:16.795625
Analysis finished2024-05-18 00:16:23.916455
Duration7.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-01
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2022-01 10000
100.0%

Length

2024-05-18T09:16:24.098418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:16:24.352804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-01 10000
100.0%

대여소번호
Real number (ℝ)

Distinct2502
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2027.6736
Minimum102
Maximum4892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:16:24.641118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile237
Q1837
median1722
Q33115
95-th percentile4584
Maximum4892
Range4790
Interquartile range (IQR)2278

Descriptive statistics

Standard deviation1394.815
Coefficient of variation (CV)0.68788933
Kurtosis-0.92311977
Mean2027.6736
Median Absolute Deviation (MAD)987
Skewness0.52306069
Sum20276736
Variance1945509
MonotonicityNot monotonic
2024-05-18T09:16:25.061547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
419 12
 
0.1%
4652 12
 
0.1%
2824 11
 
0.1%
4025 11
 
0.1%
2720 11
 
0.1%
4121 11
 
0.1%
1020 10
 
0.1%
418 10
 
0.1%
1233 10
 
0.1%
1210 10
 
0.1%
Other values (2492) 9892
98.9%
ValueCountFrequency (%)
102 6
0.1%
103 5
0.1%
104 4
< 0.1%
105 2
 
< 0.1%
106 5
0.1%
107 7
0.1%
108 3
< 0.1%
109 1
 
< 0.1%
111 5
0.1%
112 4
< 0.1%
ValueCountFrequency (%)
4892 3
< 0.1%
4891 1
 
< 0.1%
4889 1
 
< 0.1%
4888 1
 
< 0.1%
4887 4
< 0.1%
4886 4
< 0.1%
4885 3
< 0.1%
4884 5
0.1%
4883 1
 
< 0.1%
4882 2
 
< 0.1%

대여소명
Text

Distinct2502
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:16:25.561604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.5268
Min length7

Characters and Unicode

Total characters155268
Distinct characters580
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)2.5%

Sample

1st row3544.한양대역 3번 출구
2nd row337. 창경궁 입구
3rd row1265. 문정동 근린공원
4th row1077.강동역 1번출구 앞
5th row2127. 난곡종점
ValueCountFrequency (%)
앞 2715
 
9.3%
옆 414
 
1.4%
출구 406
 
1.4%
1번출구 299
 
1.0%
입구 243
 
0.8%
교차로 236
 
0.8%
사거리 215
 
0.7%
3번출구 203
 
0.7%
2번출구 193
 
0.7%
4번출구 157
 
0.5%
Other values (4982) 24115
82.6%
2024-05-18T09:16:26.569112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19388
 
12.5%
. 10029
 
6.5%
1 7926
 
5.1%
2 6179
 
4.0%
3 4898
 
3.2%
4 4716
 
3.0%
5 3610
 
2.3%
6 3570
 
2.3%
0 3459
 
2.2%
구 3157
 
2.0%
Other values (570) 88336
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79929
51.5%
Decimal Number 42688
27.5%
Space Separator 19388
 
12.5%
Other Punctuation 10138
 
6.5%
Uppercase Letter 1244
 
0.8%
Open Punctuation 799
 
0.5%
Close Punctuation 799
 
0.5%
Lowercase Letter 175
 
0.1%
Dash Punctuation 78
 
0.1%
Math Symbol 14
 
< 0.1%
Other values (3) 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3157
 
3.9%
앞 3149
 
3.9%
역 2430
 
3.0%
동 2214
 
2.8%
번 2112
 
2.6%
출 2057
 
2.6%
교 1705
 
2.1%
파 1471
 
1.8%
아 1445
 
1.8%
트 1371
 
1.7%
Other values (506) 58818
73.6%
Uppercase Letter
ValueCountFrequency (%)
K 144
11.6%
T 137
11.0%
S 135
10.9%
C 124
10.0%
D 92
 
7.4%
A 87
 
7.0%
M 80
 
6.4%
B 67
 
5.4%
G 66
 
5.3%
P 63
 
5.1%
Other values (13) 249
20.0%
Lowercase Letter
ValueCountFrequency (%)
e 67
38.3%
s 24
 
13.7%
k 22
 
12.6%
t 9
 
5.1%
l 8
 
4.6%
n 8
 
4.6%
y 4
 
2.3%
r 4
 
2.3%
f 4
 
2.3%
h 4
 
2.3%
Other values (6) 21
 
12.0%
Decimal Number
ValueCountFrequency (%)
1 7926
18.6%
2 6179
14.5%
3 4898
11.5%
4 4716
11.0%
5 3610
8.5%
6 3570
8.4%
0 3459
8.1%
7 3137
 
7.3%
8 2762
 
6.5%
9 2431
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 10029
98.9%
, 70
 
0.7%
& 19
 
0.2%
· 13
 
0.1%
? 7
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 9
64.3%
+ 5
35.7%
Other Number
ValueCountFrequency (%)
① 3
75.0%
② 1
 
25.0%
Space Separator
ValueCountFrequency (%)
19388
100.0%
Open Punctuation
ValueCountFrequency (%)
( 799
100.0%
Close Punctuation
ValueCountFrequency (%)
) 799
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79932
51.5%
Common 73917
47.6%
Latin 1419
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3157
 
3.9%
앞 3149
 
3.9%
역 2430
 
3.0%
동 2214
 
2.8%
번 2112
 
2.6%
출 2057
 
2.6%
교 1705
 
2.1%
파 1471
 
1.8%
아 1445
 
1.8%
트 1371
 
1.7%
Other values (507) 58821
73.6%
Latin
ValueCountFrequency (%)
K 144
 
10.1%
T 137
 
9.7%
S 135
 
9.5%
C 124
 
8.7%
D 92
 
6.5%
A 87
 
6.1%
M 80
 
5.6%
e 67
 
4.7%
B 67
 
4.7%
G 66
 
4.7%
Other values (29) 420
29.6%
Common
ValueCountFrequency (%)
19388
26.2%
. 10029
13.6%
1 7926
10.7%
2 6179
 
8.4%
3 4898
 
6.6%
4 4716
 
6.4%
5 3610
 
4.9%
6 3570
 
4.8%
0 3459
 
4.7%
7 3137
 
4.2%
Other values (14) 7005
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79929
51.5%
ASCII 75319
48.5%
None 16
 
< 0.1%
Enclosed Alphanum 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19388
25.7%
. 10029
13.3%
1 7926
10.5%
2 6179
 
8.2%
3 4898
 
6.5%
4 4716
 
6.3%
5 3610
 
4.8%
6 3570
 
4.7%
0 3459
 
4.6%
7 3137
 
4.2%
Other values (50) 8407
11.2%
Hangul
ValueCountFrequency (%)
구 3157
 
3.9%
앞 3149
 
3.9%
역 2430
 
3.0%
동 2214
 
2.8%
번 2112
 
2.6%
출 2057
 
2.6%
교 1705
 
2.1%
파 1471
 
1.8%
아 1445
 
1.8%
트 1371
 
1.7%
Other values (506) 58818
73.6%
None
ValueCountFrequency (%)
· 13
81.2%
㈜ 3
 
18.8%
Enclosed Alphanum
ValueCountFrequency (%)
① 3
75.0%
② 1
 
25.0%

대여구분코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5734 
일일(회원)
3716 
단체
 
317
일일(비회원)
 
232
10분이용권
 
1

Length

Max length7
Median length2
Mean length3.6028
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5734
57.3%
일일(회원) 3716
37.2%
단체 317
 
3.2%
일일(비회원) 232
 
2.3%
10분이용권 1
 
< 0.1%

Length

2024-05-18T09:16:26.861480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:16:27.055714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5734
57.3%
일일(회원 3716
37.2%
단체 317
 
3.2%
일일(비회원 232
 
2.3%
10분이용권 1
 
< 0.1%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3269 
\N
2977 
F
2687 
<NA>
1066 
m
 
1

Length

Max length4
Median length1
Mean length1.6175
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3269
32.7%
\N 2977
29.8%
F 2687
26.9%
<NA> 1066
 
10.7%
m 1
 
< 0.1%

Length

2024-05-18T09:16:27.276383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:16:27.717252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3270
32.7%
n 2977
29.8%
f 2687
26.9%
na 1066
 
10.7%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
2054 
30대
1764 
40대
1504 
기타
1445 
50대
1231 
Other values (3)
2002 

Length

Max length5
Median length3
Mean length2.9067
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20대
2nd row50대
3rd row60대
4th row30대
5th row40대

Common Values

ValueCountFrequency (%)
20대 2054
20.5%
30대 1764
17.6%
40대 1504
15.0%
기타 1445
14.4%
50대 1231
12.3%
10대 1031
10.3%
60대 715
 
7.1%
70대이상 256
 
2.6%

Length

2024-05-18T09:16:27.934510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:16:28.151100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 2054
20.5%
30대 1764
17.6%
40대 1504
15.0%
기타 1445
14.4%
50대 1231
12.3%
10대 1031
10.3%
60대 715
 
7.1%
70대이상 256
 
2.6%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct185
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.4053
Minimum1
Maximum308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:16:28.414654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q316
95-th percentile61
Maximum308
Range307
Interquartile range (IQR)14

Descriptive statistics

Standard deviation24.961758
Coefficient of variation (CV)1.7328176
Kurtosis25.289321
Mean14.4053
Median Absolute Deviation (MAD)4
Skewness4.1496852
Sum144053
Variance623.08934
MonotonicityNot monotonic
2024-05-18T09:16:28.773747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2031
20.3%
2 1312
13.1%
3 825
 
8.2%
4 586
 
5.9%
5 474
 
4.7%
6 385
 
3.9%
7 346
 
3.5%
8 299
 
3.0%
9 261
 
2.6%
10 209
 
2.1%
Other values (175) 3272
32.7%
ValueCountFrequency (%)
1 2031
20.3%
2 1312
13.1%
3 825
8.2%
4 586
 
5.9%
5 474
 
4.7%
6 385
 
3.9%
7 346
 
3.5%
8 299
 
3.0%
9 261
 
2.6%
10 209
 
2.1%
ValueCountFrequency (%)
308 1
< 0.1%
297 1
< 0.1%
294 1
< 0.1%
292 1
< 0.1%
291 1
< 0.1%
277 1
< 0.1%
260 1
< 0.1%
254 1
< 0.1%
249 1
< 0.1%
240 1
< 0.1%

운동량
Text

Distinct8829
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:16:29.518894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.8385
Min length2

Characters and Unicode

Total characters58385
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

Unique8268 ?
Unique (%)82.7%

Sample

1st row4277.04
2nd row877.78
3rd row300.10
4th row609.23
5th row93.56
ValueCountFrequency (%)
0.00 491
 
4.9%
n 18
 
0.2%
27.80 6
 
0.1%
53.28 5
 
< 0.1%
32.43 5
 
< 0.1%
40.93 5
 
< 0.1%
38.61 5
 
< 0.1%
51.99 5
 
< 0.1%
31.15 5
 
< 0.1%
90.60 4
 
< 0.1%
Other values (8819) 9451
94.5%
2024-05-18T09:16:30.416021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9982
17.1%
1 6610
11.3%
2 5606
9.6%
0 5428
9.3%
3 4991
8.5%
4 4738
8.1%
5 4476
7.7%
6 4271
7.3%
7 4157
7.1%
8 4084
7.0%
Other values (3) 4042
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48367
82.8%
Other Punctuation 10000
 
17.1%
Uppercase Letter 18
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6610
13.7%
2 5606
11.6%
0 5428
11.2%
3 4991
10.3%
4 4738
9.8%
5 4476
9.3%
6 4271
8.8%
7 4157
8.6%
8 4084
8.4%
9 4006
8.3%
Other Punctuation
ValueCountFrequency (%)
. 9982
99.8%
\ 18
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58367
> 99.9%
Latin 18
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9982
17.1%
1 6610
11.3%
2 5606
9.6%
0 5428
9.3%
3 4991
8.6%
4 4738
8.1%
5 4476
7.7%
6 4271
7.3%
7 4157
7.1%
8 4084
7.0%
Other values (2) 4024
6.9%
Latin
ValueCountFrequency (%)
N 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9982
17.1%
1 6610
11.3%
2 5606
9.6%
0 5428
9.3%
3 4991
8.5%
4 4738
8.1%
5 4476
7.7%
6 4271
7.3%
7 4157
7.1%
8 4084
7.0%
Other values (3) 4042
6.9%

탄소량
Text

Distinct2229
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:16:31.067598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1698
Min length2

Characters and Unicode

Total characters41698
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

Unique1033 ?
Unique (%)10.3%

Sample

1st row34.79
2nd row7.54
3rd row2.71
4th row5.04
5th row0.73
ValueCountFrequency (%)
0.00 494
 
4.9%
0.29 55
 
0.5%
0.25 48
 
0.5%
0.24 47
 
0.5%
0.48 45
 
0.4%
0.26 45
 
0.4%
0.58 40
 
0.4%
0.19 39
 
0.4%
0.30 38
 
0.4%
0.38 38
 
0.4%
Other values (2219) 9111
91.1%
2024-05-18T09:16:32.149757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9982
23.9%
0 6238
15.0%
1 4756
11.4%
2 3579
 
8.6%
3 3020
 
7.2%
4 2734
 
6.6%
5 2459
 
5.9%
6 2337
 
5.6%
7 2255
 
5.4%
8 2169
 
5.2%
Other values (3) 2169
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31680
76.0%
Other Punctuation 10000
 
24.0%
Uppercase Letter 18
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6238
19.7%
1 4756
15.0%
2 3579
11.3%
3 3020
9.5%
4 2734
8.6%
5 2459
 
7.8%
6 2337
 
7.4%
7 2255
 
7.1%
8 2169
 
6.8%
9 2133
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 9982
99.8%
\ 18
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41680
> 99.9%
Latin 18
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9982
23.9%
0 6238
15.0%
1 4756
11.4%
2 3579
 
8.6%
3 3020
 
7.2%
4 2734
 
6.6%
5 2459
 
5.9%
6 2337
 
5.6%
7 2255
 
5.4%
8 2169
 
5.2%
Other values (2) 2151
 
5.2%
Latin
ValueCountFrequency (%)
N 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9982
23.9%
0 6238
15.0%
1 4756
11.4%
2 3579
 
8.6%
3 3020
 
7.2%
4 2734
 
6.6%
5 2459
 
5.9%
6 2337
 
5.6%
7 2255
 
5.4%
8 2169
 
5.2%
Other values (3) 2169
 
5.2%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8432
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25818.293
Minimum0
Maximum858629.33
Zeros508
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:16:32.577441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13048.1325
median9920.85
Q328573.815
95-th percentile107205.11
Maximum858629.33
Range858629.33
Interquartile range (IQR)25525.683

Descriptive statistics

Standard deviation44305.591
Coefficient of variation (CV)1.7160542
Kurtosis39.443477
Mean25818.293
Median Absolute Deviation (MAD)8358.765
Skewness4.6575349
Sum2.5818293 × 108
Variance1.9629854 × 109
MonotonicityNot monotonic
2024-05-18T09:16:32.997967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 508
 
5.1%
2360.0 11
 
0.1%
1230.0 10
 
0.1%
830.0 9
 
0.1%
1080.0 9
 
0.1%
1180.0 9
 
0.1%
720.0 8
 
0.1%
400.0 8
 
0.1%
2040.0 8
 
0.1%
660.0 8
 
0.1%
Other values (8422) 9412
94.1%
ValueCountFrequency (%)
0.0 508
5.1%
0.2 1
 
< 0.1%
10.0 2
 
< 0.1%
20.0 1
 
< 0.1%
88.13 1
 
< 0.1%
110.0 1
 
< 0.1%
111.2 1
 
< 0.1%
141.88 1
 
< 0.1%
141.95 1
 
< 0.1%
170.0 2
 
< 0.1%
ValueCountFrequency (%)
858629.33 1
< 0.1%
716492.29 1
< 0.1%
644166.25 1
< 0.1%
548078.51 1
< 0.1%
487898.54 1
< 0.1%
480199.57 1
< 0.1%
455706.12 1
< 0.1%
419772.58 1
< 0.1%
401942.63 1
< 0.1%
398000.42 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct1454
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.9688
Minimum0
Maximum8219
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:16:33.414326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q139
median115
Q3325
95-th percentile1183
Maximum8219
Range8219
Interquartile range (IQR)286

Descriptive statistics

Standard deviation491.13146
Coefficient of variation (CV)1.6763951
Kurtosis34.093669
Mean292.9688
Median Absolute Deviation (MAD)94
Skewness4.4126584
Sum2929688
Variance241210.11
MonotonicityNot monotonic
2024-05-18T09:16:33.913374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 103
 
1.0%
7 95
 
0.9%
5 95
 
0.9%
12 92
 
0.9%
16 90
 
0.9%
9 90
 
0.9%
17 87
 
0.9%
6 85
 
0.9%
14 82
 
0.8%
15 79
 
0.8%
Other values (1444) 9102
91.0%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 15
 
0.1%
2 48
0.5%
3 56
0.6%
4 70
0.7%
5 95
0.9%
6 85
0.9%
7 95
0.9%
8 103
1.0%
9 90
0.9%
ValueCountFrequency (%)
8219 1
< 0.1%
8215 1
< 0.1%
7322 1
< 0.1%
5888 1
< 0.1%
5817 1
< 0.1%
5005 1
< 0.1%
4854 1
< 0.1%
4721 1
< 0.1%
4451 1
< 0.1%
4375 1
< 0.1%

Interactions

2024-05-18T09:16:21.890830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:18.826251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:19.773108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:20.815093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:22.150531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:19.119238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:19.948673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:21.083650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:22.422899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:19.390217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:20.220726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:21.364462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:22.699483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:19.608959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:20.483985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:21.630290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T09:16:34.173997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0000.0000.0310.0850.0760.078
대여구분코드0.0001.0000.1550.3370.3380.2330.262
성별0.0000.1551.0000.1050.0760.0710.062
연령대코드0.0310.3370.1051.0000.1830.1280.137
이용건수0.0850.3380.0760.1831.0000.7970.815
이동거리(M)0.0760.2330.0710.1280.7971.0000.940
이용시간(분)0.0780.2620.0620.1370.8150.9401.000
2024-05-18T09:16:34.429320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0470.214
성별0.0471.0000.127
대여구분코드0.2140.1271.000
2024-05-18T09:16:34.595569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.069-0.071-0.0840.0000.0000.015
이용건수-0.0691.0000.8770.8910.1470.0450.088
이동거리(M)-0.0710.8771.0000.9320.0990.0430.061
이용시간(분)-0.0840.8910.9321.0000.1120.0370.065
대여구분코드0.0000.1470.0990.1121.0000.1270.214
성별0.0000.0450.0430.0370.1271.0000.047
연령대코드0.0150.0880.0610.0650.2140.0471.000

Missing values

2024-05-18T09:16:23.054555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T09:16:23.710043image/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

대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
759002022-0135443544.한양대역 3번 출구일일(회원)M20대244277.0434.79149965.961100
83072022-01337337. 창경궁 입구정기M50대21877.787.5432521.15425
365952022-0112651265. 문정동 근린공원정기\N60대4300.102.7111712.2679
303702022-0110771077.강동역 1번출구 앞일일(회원)M30대10609.235.0421715.54300
566412022-0121272127. 난곡종점정기<NA>40대193.560.733150.016
190642022-01672672.대광고등학교단체M기타326.640.261140.08
465632022-0116651665. 양지근린공원앞일일(회원)M30대3327.652.159270.0872
545742022-0120322032. 이수역 11번출구쪽일일(회원)M30대7632.185.0321733.17288
552892022-0120672067. 보라매역?동작세무서 버스정류장(강남중학교방면)정기F20대281335.3813.5258329.54639
535612022-0119851985. 구로도서관일일(회원)M10대5271.972.319936.3365
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
58802022-01262262. 영문초등학교 사거리정기<NA>40대181002.069.3740404.15310
275832022-01992992.북한산입구 정류장정기F30대8553.985.6824459.99144
502622022-0118261826. 한신코아 앞정기F60대1322.262.9012520.053
717172022-0130133013.서울지방고용노동청 앞정기M50대361842.0214.4662358.27885
921382022-0146534653. 불광역 9번 출구 맞은편일일(회원)M20대41296.899.7642054.43292
540682022-0120022002. 노들역 1번출구정기F60대5151.361.556717.28201
569162022-0121392139. 서울신성초등학교정기F30대3156.591.757534.658
886052022-0144814481. 올림픽공원 북2문 2일일(회원)F60대20.000.000.083
559402022-0120952095.경문고등학교 앞정기\N40대301184.619.9342906.01544
570122022-0121432143. 서울산업정보학교일일(회원)F30대3465.485.2522605.03119