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-15246/A/1/datasetView.do

Alerts

대여소번호 is highly overall correlated with 대여일자High correlation
이동거리(M) is highly overall correlated with 이용시간(분)High correlation
이용시간(분) is highly overall correlated with 이동거리(M)High correlation
대여일자 is highly overall correlated with 대여소번호High correlation
대여구분코드 is highly imbalanced (53.8%)Imbalance
이동거리(M) has 1177 (11.8%) zerosZeros

Reproduction

Analysis started2024-05-18 04:59:12.983451
Analysis finished2024-05-18 04:59:23.474960
Duration10.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-02-01
7378 
2022-02-02
2622 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-02-01
2nd row2022-02-01
3rd row2022-02-02
4th row2022-02-01
5th row2022-02-01

Common Values

ValueCountFrequency (%)
2022-02-01 7378
73.8%
2022-02-02 2622
 
26.2%

Length

2024-05-18T13:59:23.661557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:59:23.940983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-02-01 7378
73.8%
2022-02-02 2622
 
26.2%

대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct2226
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1557.3187
Minimum3
Maximum5301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:59:24.366207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile155
Q1439
median1074
Q32328.5
95-th percentile4509
Maximum5301
Range5298
Interquartile range (IQR)1889.5

Descriptive statistics

Standard deviation1390.7267
Coefficient of variation (CV)0.89302642
Kurtosis-0.27227408
Mean1557.3187
Median Absolute Deviation (MAD)787
Skewness0.96013084
Sum15573187
Variance1934120.9
MonotonicityNot monotonic
2024-05-18T13:59:24.817059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
583 36
 
0.4%
502 36
 
0.4%
272 33
 
0.3%
565 31
 
0.3%
207 30
 
0.3%
383 29
 
0.3%
247 28
 
0.3%
230 26
 
0.3%
117 25
 
0.2%
210 25
 
0.2%
Other values (2216) 9701
97.0%
ValueCountFrequency (%)
3 2
 
< 0.1%
5 2
 
< 0.1%
102 15
0.1%
103 15
0.1%
104 15
0.1%
105 15
0.1%
106 17
0.2%
107 12
0.1%
108 13
0.1%
109 8
0.1%
ValueCountFrequency (%)
5301 3
< 0.1%
5075 4
< 0.1%
5074 2
< 0.1%
5073 1
 
< 0.1%
5072 1
 
< 0.1%
5070 4
< 0.1%
5067 2
< 0.1%
5066 4
< 0.1%
5065 1
 
< 0.1%
5064 2
< 0.1%

대여소
Text

Distinct2226
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T13:59:25.580825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length15.2068
Min length4

Characters and Unicode

Total characters152068
Distinct characters569
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique449 ?
Unique (%)4.5%

Sample

1st row2604. 풍납토성 서성벽터A
2nd row1404. 동일로 지하차도
3rd row211. 여의도역 4번출구 옆
4th row1166. 강서구립등빛도서관
5th row1257. 가락시장역 사거리
ValueCountFrequency (%)
앞 2901
 
9.6%
옆 458
 
1.5%
1번출구 441
 
1.5%
출구 429
 
1.4%
사거리 285
 
0.9%
3번출구 273
 
0.9%
2번출구 263
 
0.9%
뒤 255
 
0.8%
교차로 231
 
0.8%
입구 195
 
0.6%
Other values (4463) 24360
81.0%
2024-05-18T13:59:26.588294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20275
 
13.3%
. 10006
 
6.6%
1 7375
 
4.8%
2 6124
 
4.0%
3 4454
 
2.9%
4 4416
 
2.9%
5 3817
 
2.5%
구 3654
 
2.4%
6 3338
 
2.2%
0 3249
 
2.1%
Other values (559) 85360
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78592
51.7%
Decimal Number 40473
26.6%
Space Separator 20275
 
13.3%
Other Punctuation 10096
 
6.6%
Uppercase Letter 1087
 
0.7%
Close Punctuation 689
 
0.5%
Open Punctuation 689
 
0.5%
Dash Punctuation 83
 
0.1%
Lowercase Letter 69
 
< 0.1%
Connector Punctuation 6
 
< 0.1%
Other values (2) 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3654
 
4.6%
앞 3231
 
4.1%
역 2930
 
3.7%
번 2677
 
3.4%
출 2612
 
3.3%
동 2000
 
2.5%
교 1663
 
2.1%
아 1401
 
1.8%
리 1363
 
1.7%
파 1229
 
1.6%
Other values (499) 55832
71.0%
Uppercase Letter
ValueCountFrequency (%)
K 144
13.2%
C 126
11.6%
S 122
11.2%
T 104
9.6%
D 76
 
7.0%
A 64
 
5.9%
B 58
 
5.3%
M 58
 
5.3%
I 54
 
5.0%
L 45
 
4.1%
Other values (12) 236
21.7%
Lowercase Letter
ValueCountFrequency (%)
e 31
44.9%
s 8
 
11.6%
k 7
 
10.1%
t 4
 
5.8%
a 3
 
4.3%
g 3
 
4.3%
n 2
 
2.9%
l 2
 
2.9%
v 2
 
2.9%
m 1
 
1.4%
Other values (6) 6
 
8.7%
Decimal Number
ValueCountFrequency (%)
1 7375
18.2%
2 6124
15.1%
3 4454
11.0%
4 4416
10.9%
5 3817
9.4%
6 3338
8.2%
0 3249
8.0%
7 2978
7.4%
8 2536
 
6.3%
9 2186
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 10006
99.1%
, 74
 
0.7%
& 7
 
0.1%
? 6
 
0.1%
· 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
20275
100.0%
Close Punctuation
ValueCountFrequency (%)
) 689
100.0%
Open Punctuation
ValueCountFrequency (%)
( 689
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 6
100.0%
Other Number
ValueCountFrequency (%)
① 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78592
51.7%
Common 72320
47.6%
Latin 1156
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3654
 
4.6%
앞 3231
 
4.1%
역 2930
 
3.7%
번 2677
 
3.4%
출 2612
 
3.3%
동 2000
 
2.5%
교 1663
 
2.1%
아 1401
 
1.8%
리 1363
 
1.7%
파 1229
 
1.6%
Other values (499) 55832
71.0%
Latin
ValueCountFrequency (%)
K 144
12.5%
C 126
10.9%
S 122
 
10.6%
T 104
 
9.0%
D 76
 
6.6%
A 64
 
5.5%
B 58
 
5.0%
M 58
 
5.0%
I 54
 
4.7%
L 45
 
3.9%
Other values (28) 305
26.4%
Common
ValueCountFrequency (%)
20275
28.0%
. 10006
13.8%
1 7375
 
10.2%
2 6124
 
8.5%
3 4454
 
6.2%
4 4416
 
6.1%
5 3817
 
5.3%
6 3338
 
4.6%
0 3249
 
4.5%
7 2978
 
4.1%
Other values (12) 6288
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78592
51.7%
ASCII 73470
48.3%
None 3
 
< 0.1%
Enclosed Alphanum 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20275
27.6%
. 10006
13.6%
1 7375
 
10.0%
2 6124
 
8.3%
3 4454
 
6.1%
4 4416
 
6.0%
5 3817
 
5.2%
6 3338
 
4.5%
0 3249
 
4.4%
7 2978
 
4.1%
Other values (48) 7438
 
10.1%
Hangul
ValueCountFrequency (%)
구 3654
 
4.6%
앞 3231
 
4.1%
역 2930
 
3.7%
번 2677
 
3.4%
출 2612
 
3.3%
동 2000
 
2.5%
교 1663
 
2.1%
아 1401
 
1.8%
리 1363
 
1.7%
파 1229
 
1.6%
Other values (499) 55832
71.0%
None
ValueCountFrequency (%)
· 3
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 3
100.0%

대여구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7557 
일일(회원)
2236 
일일(비회원)
 
124
단체
 
83

Length

Max length7
Median length2
Mean length2.9564
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 7557
75.6%
일일(회원) 2236
 
22.4%
일일(비회원) 124
 
1.2%
단체 83
 
0.8%

Length

2024-05-18T13:59:27.081683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:59:27.556173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7557
75.6%
일일(회원 2236
 
22.4%
일일(비회원 124
 
1.2%
단체 83
 
0.8%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
4113 
\N
3259 
F
2145 
<NA>
480 
m
 
3

Length

Max length4
Median length1
Mean length1.4699
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 4113
41.1%
\N 3259
32.6%
F 2145
21.4%
<NA> 480
 
4.8%
m 3
 
< 0.1%

Length

2024-05-18T13:59:28.038659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:59:28.519340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 4116
41.2%
n 3259
32.6%
f 2145
21.4%
na 480
 
4.8%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
3269 
30대
2358 
40대
1401 
기타
1062 
50대
999 
Other values (3)
911 

Length

Max length5
Median length3
Mean length2.9046
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20대 3269
32.7%
30대 2358
23.6%
40대 1401
14.0%
기타 1062
 
10.6%
50대 999
 
10.0%
10대 479
 
4.8%
60대 378
 
3.8%
70대이상 54
 
0.5%

Length

2024-05-18T13:59:29.095078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:59:29.615070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3269
32.7%
30대 2358
23.6%
40대 1401
14.0%
기타 1062
 
10.6%
50대 999
 
10.0%
10대 479
 
4.8%
60대 378
 
3.8%
70대이상 54
 
0.5%

이용건수
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3574
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:59:30.104363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76454149
Coefficient of variation (CV)0.56323964
Kurtosis17.160113
Mean1.3574
Median Absolute Deviation (MAD)0
Skewness3.2059464
Sum13574
Variance0.58452369
MonotonicityNot monotonic
2024-05-18T13:59:30.584095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 7558
75.6%
2 1702
 
17.0%
3 485
 
4.9%
4 171
 
1.7%
5 58
 
0.6%
6 13
 
0.1%
7 7
 
0.1%
8 3
 
< 0.1%
10 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
1 7558
75.6%
2 1702
 
17.0%
3 485
 
4.9%
4 171
 
1.7%
5 58
 
0.6%
6 13
 
0.1%
7 7
 
0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 3
 
< 0.1%
7 7
 
0.1%
6 13
 
0.1%
5 58
 
0.6%
4 171
 
1.7%
3 485
 
4.9%
2 1702
17.0%

운동량
Text

Distinct6411
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T13:59:31.510026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.0954
Min length2

Characters and Unicode

Total characters50954
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

Unique4919 ?
Unique (%)49.2%

Sample

1st row239.89
2nd row13.69
3rd row194.13
4th row53.28
5th row44.00
ValueCountFrequency (%)
0.00 1168
 
11.7%
n 33
 
0.3%
28.83 10
 
0.1%
36.04 10
 
0.1%
18.02 9
 
0.1%
20.79 9
 
0.1%
24.95 9
 
0.1%
20.85 8
 
0.1%
19.56 8
 
0.1%
17.50 8
 
0.1%
Other values (6401) 8728
87.3%
2024-05-18T13:59:32.839383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9967
19.6%
0 6640
13.0%
1 5388
10.6%
2 4631
9.1%
3 4047
7.9%
4 3706
 
7.3%
5 3610
 
7.1%
6 3403
 
6.7%
7 3256
 
6.4%
8 3133
 
6.1%
Other values (3) 3173
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40921
80.3%
Other Punctuation 10000
 
19.6%
Uppercase Letter 33
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6640
16.2%
1 5388
13.2%
2 4631
11.3%
3 4047
9.9%
4 3706
9.1%
5 3610
8.8%
6 3403
8.3%
7 3256
8.0%
8 3133
7.7%
9 3107
7.6%
Other Punctuation
ValueCountFrequency (%)
. 9967
99.7%
\ 33
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50921
99.9%
Latin 33
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9967
19.6%
0 6640
13.0%
1 5388
10.6%
2 4631
9.1%
3 4047
7.9%
4 3706
 
7.3%
5 3610
 
7.1%
6 3403
 
6.7%
7 3256
 
6.4%
8 3133
 
6.2%
Other values (2) 3140
 
6.2%
Latin
ValueCountFrequency (%)
N 33
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50954
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9967
19.6%
0 6640
13.0%
1 5388
10.6%
2 4631
9.1%
3 4047
7.9%
4 3706
 
7.3%
5 3610
 
7.1%
6 3403
 
6.7%
7 3256
 
6.4%
8 3133
 
6.1%
Other values (3) 3173
 
6.2%

탄소량
Text

Distinct507
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T13:59:33.953766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9936
Min length2

Characters and Unicode

Total characters39936
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

Unique136 ?
Unique (%)1.4%

Sample

1st row2.16
2nd row0.11
3rd row1.64
4th row0.44
5th row0.47
ValueCountFrequency (%)
0.00 1171
 
11.7%
0.26 146
 
1.5%
0.16 144
 
1.4%
0.19 138
 
1.4%
0.32 128
 
1.3%
0.29 127
 
1.3%
0.18 126
 
1.3%
0.20 126
 
1.3%
0.25 126
 
1.3%
0.23 125
 
1.2%
Other values (497) 7643
76.4%
2024-05-18T13:59:35.550989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11724
29.4%
. 9967
25.0%
1 3574
 
8.9%
2 2804
 
7.0%
3 2297
 
5.8%
4 1912
 
4.8%
5 1773
 
4.4%
6 1567
 
3.9%
7 1472
 
3.7%
9 1403
 
3.5%
Other values (3) 1443
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29903
74.9%
Other Punctuation 10000
 
25.0%
Uppercase Letter 33
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11724
39.2%
1 3574
 
12.0%
2 2804
 
9.4%
3 2297
 
7.7%
4 1912
 
6.4%
5 1773
 
5.9%
6 1567
 
5.2%
7 1472
 
4.9%
9 1403
 
4.7%
8 1377
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 9967
99.7%
\ 33
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39903
99.9%
Latin 33
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11724
29.4%
. 9967
25.0%
1 3574
 
9.0%
2 2804
 
7.0%
3 2297
 
5.8%
4 1912
 
4.8%
5 1773
 
4.4%
6 1567
 
3.9%
7 1472
 
3.7%
9 1403
 
3.5%
Other values (2) 1410
 
3.5%
Latin
ValueCountFrequency (%)
N 33
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11724
29.4%
. 9967
25.0%
1 3574
 
8.9%
2 2804
 
7.0%
3 2297
 
5.8%
4 1912
 
4.8%
5 1773
 
4.4%
6 1567
 
3.9%
7 1472
 
3.7%
9 1403
 
3.5%
Other values (3) 1443
 
3.6%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5514
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3139.4054
Minimum0
Maximum47671.16
Zeros1177
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:59:36.154557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1791.3825
median1780
Q33839.0025
95-th percentile11065.459
Maximum47671.16
Range47671.16
Interquartile range (IQR)3047.62

Descriptive statistics

Standard deviation4140.324
Coefficient of variation (CV)1.3188242
Kurtosis15.566273
Mean3139.4054
Median Absolute Deviation (MAD)1240
Skewness3.2369446
Sum31394054
Variance17142283
MonotonicityNot monotonic
2024-05-18T13:59:36.629310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1177
 
11.8%
1010.0 28
 
0.3%
1220.0 23
 
0.2%
1530.0 23
 
0.2%
790.0 23
 
0.2%
990.0 21
 
0.2%
830.0 21
 
0.2%
750.0 21
 
0.2%
1280.0 20
 
0.2%
930.0 20
 
0.2%
Other values (5504) 8623
86.2%
ValueCountFrequency (%)
0.0 1177
11.8%
0.1 18
 
0.2%
0.13 4
 
< 0.1%
0.2 1
 
< 0.1%
0.4 1
 
< 0.1%
0.67 1
 
< 0.1%
10.0 2
 
< 0.1%
30.0 4
 
< 0.1%
50.0 5
 
0.1%
52.76 1
 
< 0.1%
ValueCountFrequency (%)
47671.16 1
< 0.1%
44895.93 1
< 0.1%
42230.0 1
< 0.1%
40785.51 1
< 0.1%
38460.0 1
< 0.1%
38227.58 1
< 0.1%
37810.0 1
< 0.1%
36633.01 1
< 0.1%
36360.0 1
< 0.1%
36324.09 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct244
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.4576
Minimum0
Maximum623
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:59:37.197871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median19
Q344
95-th percentile111
Maximum623
Range623
Interquartile range (IQR)35

Descriptive statistics

Standard deviation39.71966
Coefficient of variation (CV)1.187164
Kurtosis19.511858
Mean33.4576
Median Absolute Deviation (MAD)13
Skewness3.1618333
Sum334576
Variance1577.6514
MonotonicityNot monotonic
2024-05-18T13:59:37.724335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 416
 
4.2%
5 393
 
3.9%
8 375
 
3.8%
7 368
 
3.7%
4 352
 
3.5%
10 345
 
3.5%
9 337
 
3.4%
3 330
 
3.3%
11 310
 
3.1%
12 270
 
2.7%
Other values (234) 6504
65.0%
ValueCountFrequency (%)
0 7
 
0.1%
1 64
 
0.6%
2 164
 
1.6%
3 330
3.3%
4 352
3.5%
5 393
3.9%
6 416
4.2%
7 368
3.7%
8 375
3.8%
9 337
3.4%
ValueCountFrequency (%)
623 1
< 0.1%
597 1
< 0.1%
452 1
< 0.1%
406 1
< 0.1%
403 1
< 0.1%
391 1
< 0.1%
375 1
< 0.1%
368 1
< 0.1%
346 1
< 0.1%
345 1
< 0.1%

Interactions

2024-05-18T13:59:21.147547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:16.918429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:18.736120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:19.843502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:21.481449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:17.403095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:19.023234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:20.210530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:21.835410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:17.927509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:19.300693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:20.510963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:22.164427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:18.341418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:19.569784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:59:20.787023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T13:59:38.171349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.8150.0570.0560.0780.0950.0800.061
대여소번호0.8151.0000.0460.0310.0530.0370.0970.068
대여구분코드0.0570.0461.0000.2280.4660.1140.1370.132
성별0.0560.0310.2281.0000.1740.0380.0000.000
연령대코드0.0780.0530.4660.1741.0000.0960.0390.030
이용건수0.0950.0370.1140.0380.0961.0000.3480.748
이동거리(M)0.0800.0970.1370.0000.0390.3481.0000.572
이용시간(분)0.0610.0680.1320.0000.0300.7480.5721.000
2024-05-18T13:59:38.504026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여일자대여구분코드
연령대코드1.0000.0790.0580.223
성별0.0791.0000.0370.091
대여일자0.0580.0371.0000.037
대여구분코드0.2230.0910.0371.000
2024-05-18T13:59:38.831778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여일자대여구분코드성별연령대코드
대여소번호1.000-0.086-0.019-0.0720.6480.0280.0190.025
이용건수-0.0861.0000.3770.4310.0950.0730.0240.047
이동거리(M)-0.0190.3771.0000.7270.0610.0820.0000.018
이용시간(분)-0.0720.4310.7271.0000.0610.0850.0000.015
대여일자0.6480.0950.0610.0611.0000.0370.0370.058
대여구분코드0.0280.0730.0820.0850.0371.0000.0910.223
성별0.0190.0240.0000.0000.0370.0911.0000.079
연령대코드0.0250.0470.0180.0150.0580.2230.0791.000

Missing values

2024-05-18T13:59:22.590505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T13:59:23.263569image/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)
76392022-02-0126042604. 풍납토성 서성벽터A정기\N20대1239.892.169319.8966
47482022-02-0114041404. 동일로 지하차도일일(회원)<NA>30대113.690.11480.02
118212022-02-02211211. 여의도역 4번출구 옆정기\N20대4194.131.647052.470
39052022-02-0111661166. 강서구립등빛도서관정기M60대153.280.441894.9512
43282022-02-0112571257. 가락시장역 사거리정기F30대144.000.472020.014
37852022-02-0111481148. 볏골공원정기F40대131.630.381629.9117
95242022-02-0139723972. 산기슭공원 앞정기M20대143.170.371580.09
5322022-02-01229229. 양평1 보행육교 앞일일(회원)M40대1201.001.446190.037
70132022-02-0122232223. 방배래미안 정문 앞정기F40대1103.100.934005.2992
127022022-02-02320320. 을지로입구역 4번출구 앞정기M30대10.000.000.0103
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
9702022-02-01339339. 종로4가 사거리일일(회원)F40대129.540.251065.8113
139412022-02-02542542. 강변역 4번출구 뒤정기M20대5181.441.476332.05198
94522022-02-0139053905. 희훈타워빌 앞정기M30대1143.631.295580.031
67992022-02-0121732173. 당곡사거리일일(회원)M기타138.320.301307.7954
114262022-02-02159159. 이대역 4번 출구정기M40대20.000.000.07
111272022-02-02119119. 서강나루 공원정기F40대1137.731.727400.045
66162022-02-0121082108. 은천치안센터정기\N20대1211.611.616940.056
85002022-02-0134023402.이화공영주차장정기M30대229.910.271180.07
28212022-02-01835835. 남영역 건너편정기M40대150.730.421830.07
129602022-02-02371371. 동대입구역 6번출구 뒤정기<NA>20대2247.191.727420.057