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/F/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 (54.1%)Imbalance
이동거리(M) has 1143 (11.4%) zerosZeros

Reproduction

Analysis started2024-03-13 16:25:19.590715
Analysis finished2024-03-13 16:25:22.086390
Duration2.5 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
7400 
2022-02-02
2600 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2022-02-01 7400
74.0%
2022-02-02 2600
 
26.0%

Length

2024-03-14T01:25:22.134934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:22.206149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-02-01 7400
74.0%
2022-02-02 2600
 
26.0%

대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct2239
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1563.3858
Minimum3
Maximum5301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:22.312278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile154
Q1440
median1103
Q32325
95-th percentile4499
Maximum5301
Range5298
Interquartile range (IQR)1885

Descriptive statistics

Standard deviation1386.609
Coefficient of variation (CV)0.88692692
Kurtosis-0.28718068
Mean1563.3858
Median Absolute Deviation (MAD)808
Skewness0.94864844
Sum15633858
Variance1922684.4
MonotonicityNot monotonic
2024-03-14T01:25:22.418414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
583 37
 
0.4%
207 34
 
0.3%
272 30
 
0.3%
383 27
 
0.3%
210 25
 
0.2%
419 25
 
0.2%
230 25
 
0.2%
502 25
 
0.2%
585 24
 
0.2%
565 24
 
0.2%
Other values (2229) 9724
97.2%
ValueCountFrequency (%)
3 1
 
< 0.1%
5 1
 
< 0.1%
102 16
0.2%
103 13
0.1%
104 11
0.1%
105 14
0.1%
106 18
0.2%
107 15
0.1%
108 11
0.1%
109 8
0.1%
ValueCountFrequency (%)
5301 2
< 0.1%
5075 4
< 0.1%
5074 1
 
< 0.1%
5073 1
 
< 0.1%
5072 2
< 0.1%
5070 2
< 0.1%
5067 2
< 0.1%
5066 2
< 0.1%
5065 2
< 0.1%
5064 3
< 0.1%

대여소
Text

Distinct2239
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:22.636909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length15.2171
Min length4

Characters and Unicode

Total characters152171
Distinct characters570
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

Unique455 ?
Unique (%)4.5%

Sample

1st row1280. 송파파크데일 2단지입구 앞 주차장
2nd row2058. 동작경찰서
3rd row395. 경희궁 자이 2단지
4th row2413.도곡역 1번 출구
5th row4027. 한내 행복발전소 옆
ValueCountFrequency (%)
앞 2850
 
9.5%
옆 486
 
1.6%
출구 439
 
1.5%
1번출구 437
 
1.5%
사거리 294
 
1.0%
뒤 279
 
0.9%
3번출구 271
 
0.9%
2번출구 258
 
0.9%
교차로 210
 
0.7%
입구 209
 
0.7%
Other values (4488) 24359
80.9%
2024-03-14T01:25:22.950386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20287
 
13.3%
. 10009
 
6.6%
1 7475
 
4.9%
2 6084
 
4.0%
3 4449
 
2.9%
4 4424
 
2.9%
5 3821
 
2.5%
구 3700
 
2.4%
6 3283
 
2.2%
0 3220
 
2.1%
Other values (560) 85419
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78557
51.6%
Decimal Number 40513
26.6%
Space Separator 20287
 
13.3%
Other Punctuation 10096
 
6.6%
Uppercase Letter 1093
 
0.7%
Close Punctuation 712
 
0.5%
Open Punctuation 712
 
0.5%
Lowercase Letter 104
 
0.1%
Dash Punctuation 80
 
0.1%
Math Symbol 9
 
< 0.1%
Other values (2) 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3700
 
4.7%
앞 3207
 
4.1%
역 2939
 
3.7%
번 2689
 
3.4%
출 2632
 
3.4%
동 1975
 
2.5%
교 1617
 
2.1%
리 1369
 
1.7%
아 1343
 
1.7%
파 1204
 
1.5%
Other values (497) 55882
71.1%
Uppercase Letter
ValueCountFrequency (%)
K 143
13.1%
S 127
11.6%
C 122
11.2%
T 107
9.8%
A 66
 
6.0%
D 66
 
6.0%
I 60
 
5.5%
G 57
 
5.2%
L 51
 
4.7%
M 49
 
4.5%
Other values (13) 245
22.4%
Lowercase Letter
ValueCountFrequency (%)
e 38
36.5%
t 9
 
8.7%
k 8
 
7.7%
s 8
 
7.7%
l 7
 
6.7%
m 5
 
4.8%
o 5
 
4.8%
c 5
 
4.8%
n 4
 
3.8%
v 4
 
3.8%
Other values (6) 11
 
10.6%
Decimal Number
ValueCountFrequency (%)
1 7475
18.5%
2 6084
15.0%
3 4449
11.0%
4 4424
10.9%
5 3821
9.4%
6 3283
8.1%
0 3220
7.9%
7 3023
7.5%
8 2538
 
6.3%
9 2196
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 10009
99.1%
, 66
 
0.7%
& 10
 
0.1%
? 7
 
0.1%
· 4
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 8
88.9%
+ 1
 
11.1%
Other Number
ValueCountFrequency (%)
① 2
50.0%
② 2
50.0%
Space Separator
ValueCountFrequency (%)
20287
100.0%
Close Punctuation
ValueCountFrequency (%)
) 712
100.0%
Open Punctuation
ValueCountFrequency (%)
( 712
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78557
51.6%
Common 72417
47.6%
Latin 1197
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3700
 
4.7%
앞 3207
 
4.1%
역 2939
 
3.7%
번 2689
 
3.4%
출 2632
 
3.4%
동 1975
 
2.5%
교 1617
 
2.1%
리 1369
 
1.7%
아 1343
 
1.7%
파 1204
 
1.5%
Other values (497) 55882
71.1%
Latin
ValueCountFrequency (%)
K 143
11.9%
S 127
 
10.6%
C 122
 
10.2%
T 107
 
8.9%
A 66
 
5.5%
D 66
 
5.5%
I 60
 
5.0%
G 57
 
4.8%
L 51
 
4.3%
M 49
 
4.1%
Other values (29) 349
29.2%
Common
ValueCountFrequency (%)
20287
28.0%
. 10009
13.8%
1 7475
 
10.3%
2 6084
 
8.4%
3 4449
 
6.1%
4 4424
 
6.1%
5 3821
 
5.3%
6 3283
 
4.5%
0 3220
 
4.4%
7 3023
 
4.2%
Other values (14) 6342
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78557
51.6%
ASCII 73606
48.4%
None 4
 
< 0.1%
Enclosed Alphanum 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20287
27.6%
. 10009
13.6%
1 7475
 
10.2%
2 6084
 
8.3%
3 4449
 
6.0%
4 4424
 
6.0%
5 3821
 
5.2%
6 3283
 
4.5%
0 3220
 
4.4%
7 3023
 
4.1%
Other values (50) 7531
 
10.2%
Hangul
ValueCountFrequency (%)
구 3700
 
4.7%
앞 3207
 
4.1%
역 2939
 
3.7%
번 2689
 
3.4%
출 2632
 
3.4%
동 1975
 
2.5%
교 1617
 
2.1%
리 1369
 
1.7%
아 1343
 
1.7%
파 1204
 
1.5%
Other values (497) 55882
71.1%
None
ValueCountFrequency (%)
· 4
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 2
50.0%
② 2
50.0%

대여구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7588 
일일(회원)
2210 
일일(비회원)
 
117
단체
 
85

Length

Max length7
Median length2
Mean length2.9425
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 7588
75.9%
일일(회원) 2210
 
22.1%
일일(비회원) 117
 
1.2%
단체 85
 
0.9%

Length

2024-03-14T01:25:23.051950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:23.131142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7588
75.9%
일일(회원 2210
 
22.1%
일일(비회원 117
 
1.2%
단체 85
 
0.9%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
4079 
\N
3267 
F
2184 
<NA>
467 
m
 
3

Length

Max length4
Median length1
Mean length1.4668
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 4079
40.8%
\N 3267
32.7%
F 2184
21.8%
<NA> 467
 
4.7%
m 3
 
< 0.1%

Length

2024-03-14T01:25:23.219156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:23.530646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 4082
40.8%
n 3267
32.7%
f 2184
21.8%
na 467
 
4.7%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
3284 
30대
2364 
40대
1364 
기타
1054 
50대
1027 
Other values (3)
907 

Length

Max length5
Median length3
Mean length2.9054
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20대
2nd row50대
3rd row기타
4th row20대
5th row50대

Common Values

ValueCountFrequency (%)
20대 3284
32.8%
30대 2364
23.6%
40대 1364
13.6%
기타 1054
 
10.5%
50대 1027
 
10.3%
10대 470
 
4.7%
60대 383
 
3.8%
70대이상 54
 
0.5%

Length

2024-03-14T01:25:23.617636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:23.706904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3284
32.8%
30대 2364
23.6%
40대 1364
13.6%
기타 1054
 
10.5%
50대 1027
 
10.3%
10대 470
 
4.7%
60대 383
 
3.8%
70대이상 54
 
0.5%

이용건수
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3579
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:23.794754image/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 deviation0.78349791
Coefficient of variation (CV)0.57699235
Kurtosis19.97781
Mean1.3579
Median Absolute Deviation (MAD)0
Skewness3.4387252
Sum13579
Variance0.61386898
MonotonicityNot monotonic
2024-03-14T01:25:23.874912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 7588
75.9%
2 1678
 
16.8%
3 467
 
4.7%
4 174
 
1.7%
5 54
 
0.5%
6 24
 
0.2%
7 7
 
0.1%
8 5
 
0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
1 7588
75.9%
2 1678
 
16.8%
3 467
 
4.7%
4 174
 
1.7%
5 54
 
0.5%
6 24
 
0.2%
7 7
 
0.1%
8 5
 
0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
8 5
 
0.1%
7 7
 
0.1%
6 24
 
0.2%
5 54
 
0.5%
4 174
 
1.7%
3 467
 
4.7%
2 1678
16.8%

운동량
Text

Distinct6407
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:24.187151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.0894
Min length2

Characters and Unicode

Total characters50894
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

Unique4892 ?
Unique (%)48.9%

Sample

1st row51.80
2nd row111.17
3rd row26.57
4th row14.69
5th row57.67
ValueCountFrequency (%)
0.00 1126
 
11.3%
n 35
 
0.4%
31.40 9
 
0.1%
24.45 9
 
0.1%
16.63 9
 
0.1%
21.88 9
 
0.1%
17.50 9
 
0.1%
24.95 8
 
0.1%
46.33 8
 
0.1%
14.93 8
 
0.1%
Other values (6397) 8770
87.7%
2024-03-14T01:25:24.589910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9965
19.6%
0 6566
12.9%
1 5417
10.6%
2 4569
9.0%
3 4065
8.0%
4 3757
 
7.4%
5 3614
 
7.1%
6 3391
 
6.7%
7 3267
 
6.4%
8 3130
 
6.2%
Other values (3) 3153
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40859
80.3%
Other Punctuation 10000
 
19.6%
Uppercase Letter 35
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6566
16.1%
1 5417
13.3%
2 4569
11.2%
3 4065
9.9%
4 3757
9.2%
5 3614
8.8%
6 3391
8.3%
7 3267
8.0%
8 3130
7.7%
9 3083
7.5%
Other Punctuation
ValueCountFrequency (%)
. 9965
99.7%
\ 35
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50859
99.9%
Latin 35
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9965
19.6%
0 6566
12.9%
1 5417
10.7%
2 4569
9.0%
3 4065
8.0%
4 3757
 
7.4%
5 3614
 
7.1%
6 3391
 
6.7%
7 3267
 
6.4%
8 3130
 
6.2%
Other values (2) 3118
 
6.1%
Latin
ValueCountFrequency (%)
N 35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9965
19.6%
0 6566
12.9%
1 5417
10.6%
2 4569
9.0%
3 4065
8.0%
4 3757
 
7.4%
5 3614
 
7.1%
6 3391
 
6.7%
7 3267
 
6.4%
8 3130
 
6.2%
Other values (3) 3153
 
6.2%

탄소량
Text

Distinct498
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:24.938712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9933
Min length2

Characters and Unicode

Total characters39933
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

Unique135 ?
Unique (%)1.4%

Sample

1st row0.49
2nd row0.74
3rd row0.28
4th row0.12
5th row0.54
ValueCountFrequency (%)
0.00 1133
 
11.3%
0.19 158
 
1.6%
0.18 141
 
1.4%
0.16 140
 
1.4%
0.20 139
 
1.4%
0.23 135
 
1.4%
0.26 134
 
1.3%
0.25 131
 
1.3%
0.29 128
 
1.3%
0.24 125
 
1.2%
Other values (488) 7636
76.4%
2024-03-14T01:25:25.360057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11704
29.3%
. 9965
25.0%
1 3642
 
9.1%
2 2847
 
7.1%
3 2333
 
5.8%
4 1891
 
4.7%
5 1758
 
4.4%
6 1591
 
4.0%
7 1396
 
3.5%
9 1369
 
3.4%
Other values (3) 1437
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29898
74.9%
Other Punctuation 10000
 
25.0%
Uppercase Letter 35
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11704
39.1%
1 3642
 
12.2%
2 2847
 
9.5%
3 2333
 
7.8%
4 1891
 
6.3%
5 1758
 
5.9%
6 1591
 
5.3%
7 1396
 
4.7%
9 1369
 
4.6%
8 1367
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 9965
99.7%
\ 35
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39898
99.9%
Latin 35
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11704
29.3%
. 9965
25.0%
1 3642
 
9.1%
2 2847
 
7.1%
3 2333
 
5.8%
4 1891
 
4.7%
5 1758
 
4.4%
6 1591
 
4.0%
7 1396
 
3.5%
9 1369
 
3.4%
Other values (2) 1402
 
3.5%
Latin
ValueCountFrequency (%)
N 35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11704
29.3%
. 9965
25.0%
1 3642
 
9.1%
2 2847
 
7.1%
3 2333
 
5.8%
4 1891
 
4.7%
5 1758
 
4.4%
6 1591
 
4.0%
7 1396
 
3.5%
9 1369
 
3.4%
Other values (3) 1437
 
3.6%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5549
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3062.517
Minimum0
Maximum62522.74
Zeros1143
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:25.475540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1799.635
median1740
Q33728.2375
95-th percentile10704.727
Maximum62522.74
Range62522.74
Interquartile range (IQR)2928.6025

Descriptive statistics

Standard deviation4052.9861
Coefficient of variation (CV)1.3234167
Kurtosis20.16318
Mean3062.517
Median Absolute Deviation (MAD)1200
Skewness3.499667
Sum30625170
Variance16426696
MonotonicityNot monotonic
2024-03-14T01:25:25.575038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1143
 
11.4%
1220.0 27
 
0.3%
780.0 25
 
0.2%
1010.0 23
 
0.2%
1030.0 23
 
0.2%
830.0 22
 
0.2%
1170.0 21
 
0.2%
790.0 21
 
0.2%
1060.0 21
 
0.2%
750.0 21
 
0.2%
Other values (5539) 8653
86.5%
ValueCountFrequency (%)
0.0 1143
11.4%
0.1 14
 
0.1%
0.13 3
 
< 0.1%
0.2 2
 
< 0.1%
0.3 1
 
< 0.1%
0.4 1
 
< 0.1%
10.0 2
 
< 0.1%
20.0 2
 
< 0.1%
30.0 5
 
0.1%
39.03 1
 
< 0.1%
ValueCountFrequency (%)
62522.74 1
< 0.1%
47671.16 1
< 0.1%
44895.93 1
< 0.1%
40785.51 1
< 0.1%
38460.0 1
< 0.1%
38227.58 1
< 0.1%
37810.0 1
< 0.1%
36832.19 1
< 0.1%
36799.2 1
< 0.1%
36633.01 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.9915
Minimum0
Maximum623
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:25.683570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median19
Q343
95-th percentile109
Maximum623
Range623
Interquartile range (IQR)35

Descriptive statistics

Standard deviation39.975736
Coefficient of variation (CV)1.211698
Kurtosis22.883119
Mean32.9915
Median Absolute Deviation (MAD)13
Skewness3.4805939
Sum329915
Variance1598.0594
MonotonicityNot monotonic
2024-03-14T01:25:25.796825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 415
 
4.2%
5 413
 
4.1%
7 384
 
3.8%
8 374
 
3.7%
4 350
 
3.5%
3 334
 
3.3%
9 333
 
3.3%
10 325
 
3.2%
11 297
 
3.0%
13 269
 
2.7%
Other values (246) 6506
65.1%
ValueCountFrequency (%)
0 5
 
0.1%
1 60
 
0.6%
2 166
 
1.7%
3 334
3.3%
4 350
3.5%
5 413
4.1%
6 415
4.2%
7 384
3.8%
8 374
3.7%
9 333
3.3%
ValueCountFrequency (%)
623 1
< 0.1%
597 1
< 0.1%
495 1
< 0.1%
452 1
< 0.1%
414 1
< 0.1%
412 1
< 0.1%
403 1
< 0.1%
385 1
< 0.1%
378 1
< 0.1%
375 1
< 0.1%

Interactions

2024-03-14T01:25:21.547137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:20.587444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:20.902875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.220657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.624291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:20.653407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:20.991020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.298410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.696358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:20.723392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.073490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.376196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.771171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:20.809456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.145304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:21.464033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:25:25.894238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.8160.0660.0190.0620.1320.0530.067
대여소번호0.8161.0000.0500.0190.0550.0510.0510.055
대여구분코드0.0660.0501.0000.2160.4550.1670.0970.129
성별0.0190.0190.2161.0000.1780.0640.0000.000
연령대코드0.0620.0550.4550.1781.0000.1540.0000.027
이용건수0.1320.0510.1670.0640.1541.0000.3480.614
이동거리(M)0.0530.0510.0970.0000.0000.3481.0000.736
이용시간(분)0.0670.0550.1290.0000.0270.6140.7361.000
2024-03-14T01:25:25.991115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자성별대여구분코드연령대코드
대여일자1.0000.0130.0440.046
성별0.0131.0000.0860.081
대여구분코드0.0440.0861.0000.217
연령대코드0.0460.0810.2171.000
2024-03-14T01:25:26.096836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여일자대여구분코드성별연령대코드
대여소번호1.000-0.093-0.021-0.0750.6490.0300.0110.026
이용건수-0.0931.0000.3800.4280.1020.0750.0290.052
이동거리(M)-0.0210.3801.0000.7240.0530.0620.0000.000
이용시간(분)-0.0750.4280.7241.0000.0670.0830.0000.013
대여일자0.6490.1020.0530.0671.0000.0440.0130.046
대여구분코드0.0300.0750.0620.0830.0441.0000.0860.217
성별0.0110.0290.0000.0000.0130.0861.0000.081
연령대코드0.0260.0520.0000.0130.0460.2170.0811.000

Missing values

2024-03-14T01:25:21.877807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:25:22.028430image/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)
44252022-02-0112801280. 송파파크데일 2단지입구 앞 주차장정기\N20대151.800.492110.015
64492022-02-0120582058. 동작경찰서정기M50대1111.170.743190.045
11552022-02-01395395. 경희궁 자이 2단지정기M기타126.570.281220.05
74372022-02-0124132413.도곡역 1번 출구일일(회원)M20대114.690.12530.065
96352022-02-0140274027. 한내 행복발전소 옆정기F50대257.670.542320.024
113342022-02-02146146. 마포역 2번출구 뒤정기M30대143.830.371580.9916
83562022-02-0131073107. 연희초등학교 앞정기M20대146.070.421790.014
93862022-02-0138643864. 광진소방서 앞정기F50대10.000.000.05
82242022-02-0129022902.공릉풍림아파트 108동정기\N20대336.960.331430.063
137582022-02-02512512. 뚝섬역 1번 출구 옆일일(회원)M기타10.000.000.017
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
43372022-02-0112591259. 방이역 1번출구일일(회원)M30대1181.841.486377.5377
47272022-02-0114011401. 극동늘푸른아파트정기\N50대165.220.492111.6326
92062022-02-0137623762. 마곡9단지 907동앞정기F기타147.010.522240.090
137732022-02-02513513. 뚝섬역 5번 출구 정류소 옆정기M50대2159.531.315660.041
122272022-02-02255255. 도림사거리정기M기타10.000.000.04
63642022-02-0120152015. 신대방삼거리역 6번출구쪽정기F40대159.660.652790.028
112912022-02-02142142. 아현역 4번출구 앞정기M20대128.330.23980.07
12152022-02-01418418. 월드컵경기장역 3번출구 옆일일(회원)M10대138.570.431837.8124
8492022-02-01289289. 대림3동사거리일일(회원)\N40대262.010.682900.026
142782022-02-02583583. 청계천 생태교실 앞단체\N기타2113.261.024400.058