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/F/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 125 (1.2%) zerosZeros

Reproduction

Analysis started2024-03-13 13:00:06.253000
Analysis finished2024-03-13 13:00:11.322803
Duration5.07 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
2022-03
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03
2nd row2022-03
3rd row2022-03
4th row2022-03
5th row2022-03

Common Values

ValueCountFrequency (%)
2022-03 10000
100.0%

Length

2024-03-13T22:00:11.406763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:11.528322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03 10000
100.0%

대여소번호
Real number (ℝ)

Distinct1962
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1444.1985
Minimum102
Maximum3553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:11.660854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile207
Q1665
median1325
Q32140
95-th percentile3121
Maximum3553
Range3451
Interquartile range (IQR)1475

Descriptive statistics

Standard deviation902.9058
Coefficient of variation (CV)0.62519509
Kurtosis-0.72454868
Mean1444.1985
Median Absolute Deviation (MAD)722
Skewness0.43694238
Sum14441985
Variance815238.89
MonotonicityNot monotonic
2024-03-13T22:00:11.843847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1636 14
 
0.1%
207 14
 
0.1%
996 13
 
0.1%
1929 13
 
0.1%
565 13
 
0.1%
262 13
 
0.1%
2262 13
 
0.1%
502 12
 
0.1%
1641 12
 
0.1%
125 12
 
0.1%
Other values (1952) 9871
98.7%
ValueCountFrequency (%)
102 2
 
< 0.1%
103 6
0.1%
104 4
 
< 0.1%
105 5
0.1%
106 10
0.1%
107 6
0.1%
108 7
0.1%
109 6
0.1%
111 6
0.1%
112 2
 
< 0.1%
ValueCountFrequency (%)
3553 2
 
< 0.1%
3552 5
0.1%
3551 2
 
< 0.1%
3550 3
< 0.1%
3549 6
0.1%
3548 5
0.1%
3547 5
0.1%
3545 3
< 0.1%
3544 6
0.1%
3543 3
< 0.1%

대여소명
Text

Distinct1962
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:12.267121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length30
Mean length15.4173
Min length7

Characters and Unicode

Total characters154173
Distinct characters546
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)0.6%

Sample

1st row3307.봉천고개 육교
2nd row2213. 고속터미널역 5번출구 앞
3rd row331. 을지로2가 사거리 북측
4th row1681. 현대6차 아파트
5th row292. 영일 어린이공원
ValueCountFrequency (%)
앞 2672
 
9.1%
옆 452
 
1.5%
출구 441
 
1.5%
1번출구 318
 
1.1%
3번출구 253
 
0.9%
교차로 243
 
0.8%
뒤 229
 
0.8%
사거리 227
 
0.8%
2번출구 220
 
0.8%
입구 218
 
0.7%
Other values (3892) 23992
82.0%
2024-03-13T22:00:12.859807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19473
 
12.6%
. 10040
 
6.5%
1 8652
 
5.6%
2 6852
 
4.4%
3 4555
 
3.0%
4 3599
 
2.3%
구 3524
 
2.3%
5 3486
 
2.3%
0 3454
 
2.2%
6 3216
 
2.1%
Other values (536) 87322
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79748
51.7%
Decimal Number 41671
27.0%
Space Separator 19473
 
12.6%
Other Punctuation 10155
 
6.6%
Uppercase Letter 1192
 
0.8%
Close Punctuation 846
 
0.5%
Open Punctuation 846
 
0.5%
Lowercase Letter 122
 
0.1%
Dash Punctuation 82
 
0.1%
Math Symbol 20
 
< 0.1%
Other values (2) 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3524
 
4.4%
앞 3130
 
3.9%
역 2687
 
3.4%
번 2444
 
3.1%
출 2380
 
3.0%
동 2014
 
2.5%
교 1676
 
2.1%
파 1359
 
1.7%
리 1315
 
1.6%
아 1307
 
1.6%
Other values (479) 57912
72.6%
Uppercase Letter
ValueCountFrequency (%)
K 146
12.2%
S 133
11.2%
C 119
10.0%
T 87
 
7.3%
M 83
 
7.0%
D 78
 
6.5%
A 77
 
6.5%
G 67
 
5.6%
B 66
 
5.5%
L 65
 
5.5%
Other values (13) 271
22.7%
Lowercase Letter
ValueCountFrequency (%)
e 41
33.6%
l 13
 
10.7%
n 12
 
9.8%
k 9
 
7.4%
s 9
 
7.4%
c 7
 
5.7%
m 7
 
5.7%
t 7
 
5.7%
o 7
 
5.7%
y 6
 
4.9%
Decimal Number
ValueCountFrequency (%)
1 8652
20.8%
2 6852
16.4%
3 4555
10.9%
4 3599
8.6%
5 3486
8.4%
0 3454
 
8.3%
6 3216
 
7.7%
7 2924
 
7.0%
9 2527
 
6.1%
8 2406
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 10040
98.9%
, 82
 
0.8%
& 17
 
0.2%
· 9
 
0.1%
? 7
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 12
60.0%
+ 8
40.0%
Space Separator
ValueCountFrequency (%)
19473
100.0%
Close Punctuation
ValueCountFrequency (%)
) 846
100.0%
Open Punctuation
ValueCountFrequency (%)
( 846
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79758
51.7%
Common 73101
47.4%
Latin 1314
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3524
 
4.4%
앞 3130
 
3.9%
역 2687
 
3.4%
번 2444
 
3.1%
출 2380
 
3.0%
동 2014
 
2.5%
교 1676
 
2.1%
파 1359
 
1.7%
리 1315
 
1.6%
아 1307
 
1.6%
Other values (480) 57922
72.6%
Latin
ValueCountFrequency (%)
K 146
 
11.1%
S 133
 
10.1%
C 119
 
9.1%
T 87
 
6.6%
M 83
 
6.3%
D 78
 
5.9%
A 77
 
5.9%
G 67
 
5.1%
B 66
 
5.0%
L 65
 
4.9%
Other values (24) 393
29.9%
Common
ValueCountFrequency (%)
19473
26.6%
. 10040
13.7%
1 8652
11.8%
2 6852
 
9.4%
3 4555
 
6.2%
4 3599
 
4.9%
5 3486
 
4.8%
0 3454
 
4.7%
6 3216
 
4.4%
7 2924
 
4.0%
Other values (12) 6850
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79748
51.7%
ASCII 74406
48.3%
None 19
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19473
26.2%
. 10040
13.5%
1 8652
11.6%
2 6852
 
9.2%
3 4555
 
6.1%
4 3599
 
4.8%
5 3486
 
4.7%
0 3454
 
4.6%
6 3216
 
4.3%
7 2924
 
3.9%
Other values (45) 8155
11.0%
Hangul
ValueCountFrequency (%)
구 3524
 
4.4%
앞 3130
 
3.9%
역 2687
 
3.4%
번 2444
 
3.1%
출 2380
 
3.0%
동 2014
 
2.5%
교 1676
 
2.1%
파 1359
 
1.7%
리 1315
 
1.6%
아 1307
 
1.6%
Other values (479) 57912
72.6%
None
ValueCountFrequency (%)
㈜ 10
52.6%
· 9
47.4%

대여구분코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5194 
일일(회원)
3873 
단체
658 
일일(비회원)
 
274
10분이용권
 
1

Length

Max length7
Median length2
Mean length3.6866
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5194
51.9%
일일(회원) 3873
38.7%
단체 658
 
6.6%
일일(비회원) 274
 
2.7%
10분이용권 1
 
< 0.1%

Length

2024-03-13T22:00:13.041572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:13.188916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5194
51.9%
일일(회원 3873
38.7%
단체 658
 
6.6%
일일(비회원 274
 
2.7%
10분이용권 1
 
< 0.1%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3076 
\N
2995 
F
2719 
<NA>
1208 
m
 
1

Length

Max length4
Median length1
Mean length1.6619
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3076
30.8%
\N 2995
29.9%
F 2719
27.2%
<NA> 1208
 
12.1%
m 1
 
< 0.1%
f 1
 
< 0.1%

Length

2024-03-13T22:00:13.333047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:13.492075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3077
30.8%
n 2995
29.9%
f 2720
27.2%
na 1208
 
12.1%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
1799 
30대
1625 
40대
1579 
기타
1522 
50대
1279 
Other values (3)
2196 

Length

Max length5
Median length3
Mean length2.8978
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50대
2nd row20대
3rd row10대
4th row50대
5th row10대

Common Values

ValueCountFrequency (%)
20대 1799
18.0%
30대 1625
16.2%
40대 1579
15.8%
기타 1522
15.2%
50대 1279
12.8%
10대 1174
11.7%
60대 772
7.7%
70대이상 250
 
2.5%

Length

2024-03-13T22:00:13.658399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:13.805992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 1799
18.0%
30대 1625
16.2%
40대 1579
15.8%
기타 1522
15.2%
50대 1279
12.8%
10대 1174
11.7%
60대 772
7.7%
70대이상 250
 
2.5%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.4666
Minimum1
Maximum758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:13.988814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q324
95-th percentile88
Maximum758
Range757
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.906095
Coefficient of variation (CV)1.8124014
Kurtosis43.607792
Mean21.4666
Median Absolute Deviation (MAD)6
Skewness5.1045132
Sum214666
Variance1513.6843
MonotonicityNot monotonic
2024-03-13T22:00:14.166580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1424
 
14.2%
2 1200
 
12.0%
3 685
 
6.9%
4 577
 
5.8%
5 454
 
4.5%
6 356
 
3.6%
7 330
 
3.3%
8 316
 
3.2%
9 259
 
2.6%
11 200
 
2.0%
Other values (246) 4199
42.0%
ValueCountFrequency (%)
1 1424
14.2%
2 1200
12.0%
3 685
6.9%
4 577
5.8%
5 454
 
4.5%
6 356
 
3.6%
7 330
 
3.3%
8 316
 
3.2%
9 259
 
2.6%
10 197
 
2.0%
ValueCountFrequency (%)
758 1
< 0.1%
540 1
< 0.1%
498 1
< 0.1%
482 1
< 0.1%
468 1
< 0.1%
456 1
< 0.1%
438 1
< 0.1%
431 1
< 0.1%
429 1
< 0.1%
382 2
< 0.1%

운동량
Text

Distinct9515
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:14.686727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.1927
Min length2

Characters and Unicode

Total characters61927
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

Unique9190 ?
Unique (%)91.9%

Sample

1st row346.02
2nd row85.97
3rd row25.32
4th row2676.03
5th row29.06
ValueCountFrequency (%)
0.00 118
 
1.2%
n 11
 
0.1%
42.21 4
 
< 0.1%
44.02 4
 
< 0.1%
23.17 4
 
< 0.1%
77.73 4
 
< 0.1%
47.92 3
 
< 0.1%
47.10 3
 
< 0.1%
54.57 3
 
< 0.1%
80.31 3
 
< 0.1%
Other values (9505) 9843
98.4%
2024-03-13T22:00:15.327763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9989
16.1%
1 7215
11.7%
2 6024
9.7%
3 5570
9.0%
4 5056
8.2%
5 4879
7.9%
0 4763
7.7%
7 4750
7.7%
6 4715
7.6%
8 4528
7.3%
Other values (3) 4438
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51916
83.8%
Other Punctuation 10000
 
16.1%
Uppercase Letter 11
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7215
13.9%
2 6024
11.6%
3 5570
10.7%
4 5056
9.7%
5 4879
9.4%
0 4763
9.2%
7 4750
9.1%
6 4715
9.1%
8 4528
8.7%
9 4416
8.5%
Other Punctuation
ValueCountFrequency (%)
. 9989
99.9%
\ 11
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61916
> 99.9%
Latin 11
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9989
16.1%
1 7215
11.7%
2 6024
9.7%
3 5570
9.0%
4 5056
8.2%
5 4879
7.9%
0 4763
7.7%
7 4750
7.7%
6 4715
7.6%
8 4528
7.3%
Other values (2) 4427
7.2%
Latin
ValueCountFrequency (%)
N 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9989
16.1%
1 7215
11.7%
2 6024
9.7%
3 5570
9.0%
4 5056
8.2%
5 4879
7.9%
0 4763
7.7%
7 4750
7.7%
6 4715
7.6%
8 4528
7.3%
Other values (3) 4438
7.2%

탄소량
Text

Distinct3287
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:15.839979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.3487
Min length2

Characters and Unicode

Total characters43487
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

Unique1705 ?
Unique (%)17.1%

Sample

1st row3.40
2nd row0.77
3rd row0.29
4th row19.29
5th row0.42
ValueCountFrequency (%)
0.00 117
 
1.2%
0.21 28
 
0.3%
0.29 27
 
0.3%
0.52 26
 
0.3%
0.38 26
 
0.3%
0.51 26
 
0.3%
0.49 26
 
0.3%
0.42 25
 
0.2%
0.24 25
 
0.2%
0.85 25
 
0.2%
Other values (3277) 9649
96.5%
2024-03-13T22:00:16.516096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9989
23.0%
1 5198
12.0%
0 4419
10.2%
2 4133
9.5%
3 3504
 
8.1%
4 3108
 
7.1%
5 2944
 
6.8%
6 2749
 
6.3%
7 2574
 
5.9%
8 2437
 
5.6%
Other values (3) 2432
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33476
77.0%
Other Punctuation 10000
 
23.0%
Uppercase Letter 11
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5198
15.5%
0 4419
13.2%
2 4133
12.3%
3 3504
10.5%
4 3108
9.3%
5 2944
8.8%
6 2749
8.2%
7 2574
7.7%
8 2437
7.3%
9 2410
7.2%
Other Punctuation
ValueCountFrequency (%)
. 9989
99.9%
\ 11
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43476
> 99.9%
Latin 11
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9989
23.0%
1 5198
12.0%
0 4419
10.2%
2 4133
9.5%
3 3504
 
8.1%
4 3108
 
7.1%
5 2944
 
6.8%
6 2749
 
6.3%
7 2574
 
5.9%
8 2437
 
5.6%
Other values (2) 2421
 
5.6%
Latin
ValueCountFrequency (%)
N 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43487
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9989
23.0%
1 5198
12.0%
0 4419
10.2%
2 4133
9.5%
3 3504
 
8.1%
4 3108
 
7.1%
5 2944
 
6.8%
6 2749
 
6.3%
7 2574
 
5.9%
8 2437
 
5.6%
Other values (3) 2432
 
5.6%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9414
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56407.322
Minimum0
Maximum4733215.8
Zeros125
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:16.735503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1200
Q16759.55
median20941.08
Q364699.973
95-th percentile226184.3
Maximum4733215.8
Range4733215.8
Interquartile range (IQR)57940.423

Descriptive statistics

Standard deviation106495.11
Coefficient of variation (CV)1.887966
Kurtosis390.91678
Mean56407.322
Median Absolute Deviation (MAD)17804.74
Skewness11.658761
Sum5.6407322 × 108
Variance1.1341207 × 1010
MonotonicityNot monotonic
2024-03-13T22:00:16.973712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 125
 
1.2%
1710.0 7
 
0.1%
930.0 6
 
0.1%
1690.0 6
 
0.1%
3390.0 6
 
0.1%
1640.0 5
 
0.1%
3810.0 5
 
0.1%
750.0 5
 
0.1%
3020.0 5
 
0.1%
1230.0 5
 
0.1%
Other values (9404) 9825
98.2%
ValueCountFrequency (%)
0.0 125
1.2%
0.1 1
 
< 0.1%
0.29 1
 
< 0.1%
16.5 1
 
< 0.1%
30.0 1
 
< 0.1%
40.0 1
 
< 0.1%
50.0 1
 
< 0.1%
111.2 2
 
< 0.1%
111.59 1
 
< 0.1%
120.0 3
 
< 0.1%
ValueCountFrequency (%)
4733215.85 1
< 0.1%
1373293.48 1
< 0.1%
1370364.61 1
< 0.1%
1216149.7 1
< 0.1%
1138314.06 1
< 0.1%
1100538.94 1
< 0.1%
1047443.67 1
< 0.1%
1012867.87 1
< 0.1%
998471.4 1
< 0.1%
929558.41 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct1982
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean506.0313
Minimum0
Maximum41823
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:17.171406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q166
median199
Q3574
95-th percentile2019.05
Maximum41823
Range41823
Interquartile range (IQR)508

Descriptive statistics

Standard deviation928.55498
Coefficient of variation (CV)1.8349754
Kurtosis408.88183
Mean506.0313
Median Absolute Deviation (MAD)164
Skewness11.843131
Sum5060313
Variance862214.36
MonotonicityNot monotonic
2024-03-13T22:00:17.865295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 65
 
0.7%
17 59
 
0.6%
12 55
 
0.5%
13 54
 
0.5%
10 54
 
0.5%
7 53
 
0.5%
11 50
 
0.5%
8 50
 
0.5%
9 49
 
0.5%
21 49
 
0.5%
Other values (1972) 9462
94.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 8
 
0.1%
2 30
0.3%
3 30
0.3%
4 38
0.4%
5 41
0.4%
6 47
0.5%
7 53
0.5%
8 50
0.5%
9 49
0.5%
ValueCountFrequency (%)
41823 1
< 0.1%
10963 1
< 0.1%
10734 1
< 0.1%
9628 1
< 0.1%
9370 1
< 0.1%
9351 1
< 0.1%
9126 1
< 0.1%
8892 1
< 0.1%
8809 1
< 0.1%
8799 1
< 0.1%

Interactions

2024-03-13T22:00:10.358365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:08.513515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:09.149299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:09.652026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:10.516347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:08.685100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:09.282197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:09.866427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:10.643139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:08.870750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:09.411594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:10.080018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:10.816896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:09.040336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:09.531852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:10.222513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:00:18.042239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0000.0000.0000.0840.0270.025
대여구분코드0.0001.0000.2850.3320.1820.0370.034
성별0.0000.2851.0000.0530.0420.0250.025
연령대코드0.0000.3320.0531.0000.1820.1470.139
이용건수0.0840.1820.0420.1821.0000.8300.860
이동거리(M)0.0270.0370.0250.1470.8301.0000.983
이용시간(분)0.0250.0340.0250.1390.8600.9831.000
2024-03-13T22:00:18.243342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.1100.211
성별0.1101.0000.032
연령대코드0.2110.0321.000
2024-03-13T22:00:18.407418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.054-0.052-0.0620.0000.0000.000
이용건수-0.0541.0000.9070.9190.1060.0240.090
이동거리(M)-0.0520.9071.0000.9680.0300.0210.066
이용시간(분)-0.0620.9190.9681.0000.0280.0210.063
대여구분코드0.0000.1060.0300.0281.0000.1100.211
성별0.0000.0240.0210.0210.1101.0000.032
연령대코드0.0000.0900.0660.0630.2110.0321.000

Missing values

2024-03-13T22:00:10.985780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:00:11.233217image/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)
870982022-0333073307.봉천고개 육교정기F50대6346.023.4014588.8115
691742022-0322132213. 고속터미널역 5번출구 앞일일(비회원)\N20대185.970.773340.048
95062022-03331331. 을지로2가 사거리 북측정기\N10대125.320.291229.4510
555982022-0316811681. 현대6차 아파트정기M50대282676.0319.2983144.24867
82422022-03292292. 영일 어린이공원단체F10대129.060.421790.032
119172022-03398398. 을지로3가역 3번출구일일(회원)M기타4338.792.9312632.03110
267602022-03796796.목동아파트 14단지 B상가 앞정기F60대7241.042.3610180.073
303662022-03919919. 서울혁신파크정기\N기타139.420.421810.012
23722022-03156156. 서울서부지방법원 앞정기M50대423183.4425.43109488.27654
402982022-0311981198. LG 사이언스파크일일(회원)M30대5151.351.285504.1536
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
797432022-0326312631.배명고등학교일일(회원)M20대2371.433.3514430.0129
882712022-0334303430.구기치안센터 앞일일(회원)M50대187.520.793400.092
38302022-03194194. 증산교 앞정기M10대3260.062.048780.1356
797732022-0326322632.석촌고분역 2번 출구단체M50대2451.644.0717546.5117
700572022-0322412241.양재전화국 사거리일일(비회원)\N기타395.300.863702.7262
518692022-0315381538. 솔밭공원역일일(회원)\N40대1157.121.084667.9935
779372022-0325352535.신반포역 3번출구 뒤정기<NA>20대2324.192.6011190.0191
7882022-03117117. 홍은사거리정기M20대11410582.7691.43394156.32718
48812022-03217217. NH농협은행 앞일일(회원)F60대1201.801.827840.084
662242022-0320942094.숭실대학교(중문)일일(회원)F10대219.900.21907.56436