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 127 (1.3%) zerosZeros

Reproduction

Analysis started2024-05-03 22:12:28.639516
Analysis finished2024-05-03 22:12:37.733848
Duration9.09 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

2024-05-03T22:12:37.959719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:12:38.364598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03 10000
100.0%

대여소번호
Real number (ℝ)

Distinct1968
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1462.3872
Minimum10
Maximum3553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:12:38.686052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile213
Q1704
median1340
Q32173
95-th percentile3128
Maximum3553
Range3543
Interquartile range (IQR)1469

Descriptive statistics

Standard deviation901.96879
Coefficient of variation (CV)0.61677836
Kurtosis-0.71183744
Mean1462.3872
Median Absolute Deviation (MAD)724
Skewness0.43061053
Sum14623872
Variance813547.69
MonotonicityNot monotonic
2024-05-03T22:12:39.098420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1757 15
 
0.1%
559 14
 
0.1%
1984 14
 
0.1%
627 14
 
0.1%
870 13
 
0.1%
1650 13
 
0.1%
210 13
 
0.1%
2528 13
 
0.1%
1222 12
 
0.1%
1129 12
 
0.1%
Other values (1958) 9867
98.7%
ValueCountFrequency (%)
10 1
 
< 0.1%
102 3
 
< 0.1%
103 9
0.1%
104 8
0.1%
105 3
 
< 0.1%
106 5
0.1%
107 2
 
< 0.1%
108 7
0.1%
109 2
 
< 0.1%
111 6
0.1%
ValueCountFrequency (%)
3553 1
 
< 0.1%
3552 6
0.1%
3551 4
< 0.1%
3550 7
0.1%
3549 5
0.1%
3548 7
0.1%
3547 3
< 0.1%
3545 3
< 0.1%
3544 6
0.1%
3543 4
< 0.1%

대여소명
Text

Distinct1968
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T22:12:39.651607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length30
Mean length15.3557
Min length3

Characters and Unicode

Total characters153557
Distinct characters547
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

Unique73 ?
Unique (%)0.7%

Sample

1st row1805. 서울디지털운동장 앞
2nd row855.이촌역5번출구 앞
3rd row225. 앙카라공원 앞
4th row130. 신촌역(2호선) 7번출구 앞
5th row678.장안힐스테이트(아) 앞
ValueCountFrequency (%)
앞 2615
 
9.0%
옆 421
 
1.4%
출구 415
 
1.4%
1번출구 322
 
1.1%
2번출구 245
 
0.8%
뒤 244
 
0.8%
입구 242
 
0.8%
3번출구 238
 
0.8%
사거리 230
 
0.8%
교차로 227
 
0.8%
Other values (3908) 23878
82.1%
2024-05-03T22:12:40.590632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19289
 
12.6%
. 10051
 
6.5%
1 8722
 
5.7%
2 6925
 
4.5%
3 4566
 
3.0%
5 3557
 
2.3%
4 3512
 
2.3%
구 3497
 
2.3%
0 3409
 
2.2%
6 3075
 
2.0%
Other values (537) 86954
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79306
51.6%
Decimal Number 41651
27.1%
Space Separator 19289
 
12.6%
Other Punctuation 10140
 
6.6%
Uppercase Letter 1272
 
0.8%
Open Punctuation 839
 
0.5%
Close Punctuation 839
 
0.5%
Lowercase Letter 110
 
0.1%
Dash Punctuation 72
 
< 0.1%
Math Symbol 22
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3497
 
4.4%
앞 3068
 
3.9%
역 2668
 
3.4%
번 2375
 
3.0%
출 2328
 
2.9%
동 1992
 
2.5%
교 1726
 
2.2%
리 1387
 
1.7%
파 1318
 
1.7%
아 1262
 
1.6%
Other values (480) 57685
72.7%
Uppercase Letter
ValueCountFrequency (%)
C 148
11.6%
S 145
11.4%
K 144
11.3%
T 93
 
7.3%
A 87
 
6.8%
M 82
 
6.4%
D 80
 
6.3%
B 68
 
5.3%
I 66
 
5.2%
G 55
 
4.3%
Other values (13) 304
23.9%
Lowercase Letter
ValueCountFrequency (%)
e 29
26.4%
s 18
16.4%
k 18
16.4%
n 14
12.7%
l 9
 
8.2%
y 7
 
6.4%
v 7
 
6.4%
c 2
 
1.8%
m 2
 
1.8%
o 2
 
1.8%
Decimal Number
ValueCountFrequency (%)
1 8722
20.9%
2 6925
16.6%
3 4566
11.0%
5 3557
8.5%
4 3512
8.4%
0 3409
 
8.2%
6 3075
 
7.4%
7 2932
 
7.0%
9 2510
 
6.0%
8 2443
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 10051
99.1%
, 60
 
0.6%
? 12
 
0.1%
& 10
 
0.1%
· 7
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 17
77.3%
+ 5
 
22.7%
Space Separator
ValueCountFrequency (%)
19289
100.0%
Open Punctuation
ValueCountFrequency (%)
( 839
100.0%
Close Punctuation
ValueCountFrequency (%)
) 839
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79315
51.7%
Common 72860
47.4%
Latin 1382
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3497
 
4.4%
앞 3068
 
3.9%
역 2668
 
3.4%
번 2375
 
3.0%
출 2328
 
2.9%
동 1992
 
2.5%
교 1726
 
2.2%
리 1387
 
1.7%
파 1318
 
1.7%
아 1262
 
1.6%
Other values (481) 57694
72.7%
Latin
ValueCountFrequency (%)
C 148
 
10.7%
S 145
 
10.5%
K 144
 
10.4%
T 93
 
6.7%
A 87
 
6.3%
M 82
 
5.9%
D 80
 
5.8%
B 68
 
4.9%
I 66
 
4.8%
G 55
 
4.0%
Other values (24) 414
30.0%
Common
ValueCountFrequency (%)
19289
26.5%
. 10051
13.8%
1 8722
12.0%
2 6925
 
9.5%
3 4566
 
6.3%
5 3557
 
4.9%
4 3512
 
4.8%
0 3409
 
4.7%
6 3075
 
4.2%
7 2932
 
4.0%
Other values (12) 6822
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79306
51.6%
ASCII 74235
48.3%
None 16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19289
26.0%
. 10051
13.5%
1 8722
11.7%
2 6925
 
9.3%
3 4566
 
6.2%
5 3557
 
4.8%
4 3512
 
4.7%
0 3409
 
4.6%
6 3075
 
4.1%
7 2932
 
3.9%
Other values (45) 8197
11.0%
Hangul
ValueCountFrequency (%)
구 3497
 
4.4%
앞 3068
 
3.9%
역 2668
 
3.4%
번 2375
 
3.0%
출 2328
 
2.9%
동 1992
 
2.5%
교 1726
 
2.2%
리 1387
 
1.7%
파 1318
 
1.7%
아 1262
 
1.6%
Other values (480) 57685
72.7%
None
ValueCountFrequency (%)
㈜ 9
56.2%
· 7
43.8%

대여구분코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5246 
일일(회원)
3866 
단체
653 
일일(비회원)
 
233
10분이용권
 
2

Length

Max length7
Median length2
Mean length3.6637
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5246
52.5%
일일(회원) 3866
38.7%
단체 653
 
6.5%
일일(비회원) 233
 
2.3%
10분이용권 2
 
< 0.1%

Length

2024-05-03T22:12:40.974412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:12:41.320462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5246
52.5%
일일(회원 3866
38.7%
단체 653
 
6.5%
일일(비회원 233
 
2.3%
10분이용권 2
 
< 0.1%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3116 
\N
2909 
F
2742 
<NA>
1230 
f
 
2

Length

Max length4
Median length1
Mean length1.6599
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3116
31.2%
\N 2909
29.1%
F 2742
27.4%
<NA> 1230
 
12.3%
f 2
 
< 0.1%
m 1
 
< 0.1%

Length

2024-05-03T22:12:41.727406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:12:42.088439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3117
31.2%
n 2909
29.1%
f 2744
27.4%
na 1230
 
12.3%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
1803 
30대
1611 
40대
1566 
기타
1461 
50대
1324 
Other values (3)
2235 

Length

Max length5
Median length3
Mean length2.9107
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20대 1803
18.0%
30대 1611
16.1%
40대 1566
15.7%
기타 1461
14.6%
50대 1324
13.2%
10대 1154
11.5%
60대 797
8.0%
70대이상 284
 
2.8%

Length

2024-05-03T22:12:42.516963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T22:12:43.012926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 1803
18.0%
30대 1611
16.1%
40대 1566
15.7%
기타 1461
14.6%
50대 1324
13.2%
10대 1154
11.5%
60대 797
8.0%
70대이상 284
 
2.8%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct259
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.464
Minimum1
Maximum815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:12:43.590566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q323
95-th percentile89
Maximum815
Range814
Interquartile range (IQR)21

Descriptive statistics

Standard deviation39.568683
Coefficient of variation (CV)1.8434906
Kurtosis48.008342
Mean21.464
Median Absolute Deviation (MAD)6
Skewness5.2344149
Sum214640
Variance1565.6807
MonotonicityNot monotonic
2024-05-03T22:12:44.069717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1500
 
15.0%
2 1154
 
11.5%
3 713
 
7.1%
4 584
 
5.8%
5 465
 
4.7%
6 373
 
3.7%
7 312
 
3.1%
8 290
 
2.9%
9 245
 
2.5%
10 232
 
2.3%
Other values (249) 4132
41.3%
ValueCountFrequency (%)
1 1500
15.0%
2 1154
11.5%
3 713
7.1%
4 584
 
5.8%
5 465
 
4.7%
6 373
 
3.7%
7 312
 
3.1%
8 290
 
2.9%
9 245
 
2.5%
10 232
 
2.3%
ValueCountFrequency (%)
815 1
< 0.1%
589 1
< 0.1%
542 1
< 0.1%
540 1
< 0.1%
481 1
< 0.1%
473 1
< 0.1%
453 1
< 0.1%
447 1
< 0.1%
423 1
< 0.1%
402 1
< 0.1%

운동량
Text

Distinct9495
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T22:12:45.155417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.1782
Min length2

Characters and Unicode

Total characters61782
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

Unique9159 ?
Unique (%)91.6%

Sample

1st row5209.46
2nd row640.95
3rd row2281.17
4th row2554.60
5th row409.64
ValueCountFrequency (%)
0.00 122
 
1.2%
n 11
 
0.1%
23.17 5
 
< 0.1%
45.30 4
 
< 0.1%
25.74 4
 
< 0.1%
44.02 4
 
< 0.1%
53.54 4
 
< 0.1%
68.47 4
 
< 0.1%
26.25 4
 
< 0.1%
48.65 3
 
< 0.1%
Other values (9485) 9835
98.4%
2024-05-03T22:12:46.805939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9989
16.2%
1 7174
11.6%
2 6053
9.8%
3 5478
8.9%
4 5019
8.1%
5 4884
7.9%
6 4821
7.8%
0 4763
7.7%
7 4722
7.6%
8 4472
7.2%
Other values (3) 4407
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51771
83.8%
Other Punctuation 10000
 
16.2%
Uppercase Letter 11
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7174
13.9%
2 6053
11.7%
3 5478
10.6%
4 5019
9.7%
5 4884
9.4%
6 4821
9.3%
0 4763
9.2%
7 4722
9.1%
8 4472
8.6%
9 4385
8.5%
Other Punctuation
ValueCountFrequency (%)
. 9989
99.9%
\ 11
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61771
> 99.9%
Latin 11
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9989
16.2%
1 7174
11.6%
2 6053
9.8%
3 5478
8.9%
4 5019
8.1%
5 4884
7.9%
6 4821
7.8%
0 4763
7.7%
7 4722
7.6%
8 4472
7.2%
Other values (2) 4396
7.1%
Latin
ValueCountFrequency (%)
N 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9989
16.2%
1 7174
11.6%
2 6053
9.8%
3 5478
8.9%
4 5019
8.1%
5 4884
7.9%
6 4821
7.8%
0 4763
7.7%
7 4722
7.6%
8 4472
7.2%
Other values (3) 4407
7.1%

탄소량
Text

Distinct3260
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T22:12:47.790710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.3352
Min length2

Characters and Unicode

Total characters43352
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

Unique1719 ?
Unique (%)17.2%

Sample

1st row41.76
2nd row5.22
3rd row23.28
4th row20.15
5th row3.30
ValueCountFrequency (%)
0.00 123
 
1.2%
0.45 31
 
0.3%
0.23 29
 
0.3%
0.25 29
 
0.3%
0.48 28
 
0.3%
0.41 28
 
0.3%
0.57 26
 
0.3%
0.59 26
 
0.3%
0.31 25
 
0.2%
0.33 25
 
0.2%
Other values (3250) 9630
96.3%
2024-05-03T22:12:49.342668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9989
23.0%
1 5059
11.7%
0 4450
10.3%
2 4068
9.4%
3 3414
 
7.9%
4 3168
 
7.3%
5 3016
 
7.0%
6 2711
 
6.3%
7 2565
 
5.9%
8 2518
 
5.8%
Other values (3) 2394
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33341
76.9%
Other Punctuation 10000
 
23.1%
Uppercase Letter 11
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5059
15.2%
0 4450
13.3%
2 4068
12.2%
3 3414
10.2%
4 3168
9.5%
5 3016
9.0%
6 2711
8.1%
7 2565
7.7%
8 2518
7.6%
9 2372
7.1%
Other Punctuation
ValueCountFrequency (%)
. 9989
99.9%
\ 11
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43341
> 99.9%
Latin 11
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9989
23.0%
1 5059
11.7%
0 4450
10.3%
2 4068
9.4%
3 3414
 
7.9%
4 3168
 
7.3%
5 3016
 
7.0%
6 2711
 
6.3%
7 2565
 
5.9%
8 2518
 
5.8%
Other values (2) 2383
 
5.5%
Latin
ValueCountFrequency (%)
N 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9989
23.0%
1 5059
11.7%
0 4450
10.3%
2 4068
9.4%
3 3414
 
7.9%
4 3168
 
7.3%
5 3016
 
7.0%
6 2711
 
6.3%
7 2565
 
5.9%
8 2518
 
5.8%
Other values (3) 2394
 
5.5%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9374
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55339.302
Minimum0
Maximum4953131.5
Zeros127
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:12:50.168047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1135.2905
Q16435.315
median20138.075
Q362592.225
95-th percentile219705.45
Maximum4953131.5
Range4953131.5
Interquartile range (IQR)56156.91

Descriptive statistics

Standard deviation107254.87
Coefficient of variation (CV)1.9381319
Kurtosis460.43547
Mean55339.302
Median Absolute Deviation (MAD)17088.075
Skewness12.933608
Sum5.5339302 × 108
Variance1.1503607 × 1010
MonotonicityNot monotonic
2024-05-03T22:12:50.720147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 127
 
1.3%
1810.0 8
 
0.1%
2090.0 8
 
0.1%
920.0 7
 
0.1%
4520.0 7
 
0.1%
1280.0 6
 
0.1%
900.0 6
 
0.1%
1760.0 6
 
0.1%
1710.0 6
 
0.1%
1540.0 6
 
0.1%
Other values (9364) 9813
98.1%
ValueCountFrequency (%)
0.0 127
1.3%
0.1 2
 
< 0.1%
0.2 1
 
< 0.1%
0.26 1
 
< 0.1%
0.67 1
 
< 0.1%
10.0 2
 
< 0.1%
30.0 1
 
< 0.1%
50.0 1
 
< 0.1%
80.0 1
 
< 0.1%
88.22 2
 
< 0.1%
ValueCountFrequency (%)
4953131.52 1
< 0.1%
1939132.03 1
< 0.1%
1483680.94 1
< 0.1%
1175113.39 1
< 0.1%
1140766.2 1
< 0.1%
1117819.95 1
< 0.1%
1012867.87 1
< 0.1%
995967.58 1
< 0.1%
959778.07 1
< 0.1%
956281.54 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct1983
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.8749
Minimum0
Maximum45417
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T22:12:51.164633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q163
median191
Q3569.25
95-th percentile1963.25
Maximum45417
Range45417
Interquartile range (IQR)506.25

Descriptive statistics

Standard deviation955.01104
Coefficient of variation (CV)1.9066858
Kurtosis509.2229
Mean500.8749
Median Absolute Deviation (MAD)157
Skewness13.517999
Sum5008749
Variance912046.09
MonotonicityNot monotonic
2024-05-03T22:12:51.588626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 62
 
0.6%
8 58
 
0.6%
9 58
 
0.6%
21 55
 
0.5%
7 55
 
0.5%
11 54
 
0.5%
13 54
 
0.5%
15 54
 
0.5%
16 52
 
0.5%
18 49
 
0.5%
Other values (1973) 9449
94.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 6
 
0.1%
2 26
0.3%
3 35
0.4%
4 43
0.4%
5 45
0.4%
6 45
0.4%
7 55
0.5%
8 58
0.6%
9 58
0.6%
ValueCountFrequency (%)
45417 1
< 0.1%
15040 1
< 0.1%
11101 1
< 0.1%
10734 1
< 0.1%
10256 1
< 0.1%
10148 1
< 0.1%
9718 1
< 0.1%
9280 1
< 0.1%
9180 1
< 0.1%
8166 1
< 0.1%

Interactions

2024-05-03T22:12:35.729285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:32.077255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:33.452640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:34.634650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:36.000216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:32.486368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:33.763161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:34.911370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:36.303681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:32.854502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:34.085262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:35.214492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:36.569951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:33.155291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:34.368324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T22:12:35.479491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T22:12:51.979774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0290.0000.0050.0570.0000.013
대여구분코드0.0291.0000.2790.3190.1760.0570.063
성별0.0000.2791.0000.0660.0430.0160.028
연령대코드0.0050.3190.0661.0000.1600.0800.069
이용건수0.0570.1760.0430.1601.0000.7770.839
이동거리(M)0.0000.0570.0160.0800.7771.0000.978
이용시간(분)0.0130.0630.0280.0690.8390.9781.000
2024-05-03T22:12:52.321767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별연령대코드대여구분코드
성별1.0000.0400.107
연령대코드0.0401.0000.202
대여구분코드0.1070.2021.000
2024-05-03T22:12:52.624710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.049-0.042-0.0500.0120.0000.003
이용건수-0.0491.0000.9070.9190.1020.0240.079
이동거리(M)-0.0420.9071.0000.9670.0210.0060.049
이용시간(분)-0.0500.9190.9671.0000.0240.0100.042
대여구분코드0.0120.1020.0210.0241.0000.1070.202
성별0.0000.0240.0060.0100.1071.0000.040
연령대코드0.0030.0790.0490.0420.2020.0401.000

Missing values

2024-05-03T22:12:36.939055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T22:12:37.489090image/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)
589392022-0318051805. 서울디지털운동장 앞정기M30대725209.4641.76179911.971258
289932022-03855855.이촌역5번출구 앞정기<NA>20대1640.955.2222480.090
52462022-03225225. 앙카라공원 앞일일(회원)F20대352281.1723.28100428.581322
13762022-03130130. 신촌역(2호선) 7번출구 앞정기M기타372554.6020.1586848.05851
227422022-03678678.장안힐스테이트(아) 앞일일(회원)\N기타4409.643.3014202.3592
497392022-0314581458. 상봉터미널2일일(비회원)\N기타12390.893.5215186.17450
6562022-03115115. 마스타 빌딩 앞일일(회원)M20대202388.9119.2583027.52700
485172022-0314281428. 원묵고등학교정기M60대3465.623.6615796.3398
760672022-0324152415.한티역 롯데백화점 앞정기\N10대212184.4220.7889627.92612
699072022-0322332233. 서초신동아1차아파트 옆일일(회원)F30대5214.452.3310016.68133
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
69662022-03263263. 근로자회관 사거리일일(회원)M20대291774.2715.3266045.33584
23092022-03155155. 가좌역1 번출구 앞정기\N기타10.000.000.06
240532022-03733733. 신정이펜하우스314동일일(회원)\N20대111051.109.5140968.36339
374712022-0311271127. 화곡역 버스정류장일일(회원)\N50대140.410.361570.018
664872022-0321052105. 미성동 신림체육센터일일(회원)F10대6424.455.2222463.74201
839622022-0329022902.공릉풍림아파트 108동정기F기타5192.871.988536.76100
618472022-0319311931. 개봉역2번출구 A정기F10대231.640.251090.666
660572022-0320892089.사당역10번출구(금강빌딩)일일(회원)M30대5325.472.7211719.4195
433452022-0312741274. 영파여고 앞정기M기타17542.453.8816755.46157
13012022-03129129. 신촌역(2호선) 6번출구 옆정기\N30대463305.7030.06129618.931488