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

DateTime1
Numeric4
Text3
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/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
대여구분코드 is highly imbalanced (56.8%)Imbalance
이동거리(M) has 668 (6.7%) zerosZeros

Reproduction

Analysis started2024-03-13 16:25:43.082040
Analysis finished2024-03-13 16:25:45.738211
Duration2.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-05-01 00:00:00
Maximum2021-05-01 00:00:00
2024-03-14T01:25:45.771009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:45.839301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct1377
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean987.8618
Minimum102
Maximum2057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:45.923893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile176
Q1518
median965
Q31430
95-th percentile1955
Maximum2057
Range1955
Interquartile range (IQR)912

Descriptive statistics

Standard deviation560.29243
Coefficient of variation (CV)0.56717693
Kurtosis-1.0964463
Mean987.8618
Median Absolute Deviation (MAD)454
Skewness0.18025627
Sum9878618
Variance313927.6
MonotonicityNot monotonic
2024-03-14T01:25:46.030792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1906 27
 
0.3%
207 26
 
0.3%
502 24
 
0.2%
1125 22
 
0.2%
2002 21
 
0.2%
770 20
 
0.2%
1124 20
 
0.2%
2025 20
 
0.2%
1166 20
 
0.2%
272 20
 
0.2%
Other values (1367) 9780
97.8%
ValueCountFrequency (%)
102 13
0.1%
103 12
0.1%
104 8
0.1%
105 8
0.1%
106 14
0.1%
107 9
0.1%
108 7
0.1%
109 6
0.1%
111 4
 
< 0.1%
112 9
0.1%
ValueCountFrequency (%)
2057 4
 
< 0.1%
2056 6
0.1%
2054 9
0.1%
2050 13
0.1%
2048 6
0.1%
2041 2
 
< 0.1%
2040 2
 
< 0.1%
2038 2
 
< 0.1%
2037 8
0.1%
2036 3
 
< 0.1%

대여소
Text

Distinct1377
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:46.251863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length15.308
Min length7

Characters and Unicode

Total characters153080
Distinct characters496
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row1941. 오류동역 2번출구
2nd row1912. 한신휴아파트 앞
3rd row1828. 한양수자인아파트 앞
4th row1060. 천일초교 사거리
5th row1365. 선잠단지 앞
ValueCountFrequency (%)
앞 2730
 
9.0%
옆 532
 
1.8%
출구 381
 
1.3%
1번출구 354
 
1.2%
사거리 256
 
0.8%
4번출구 243
 
0.8%
3번출구 239
 
0.8%
2번출구 233
 
0.8%
교차로 225
 
0.7%
뒤 225
 
0.7%
Other values (2853) 24940
82.2%
2024-03-14T01:25:46.578361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20539
 
13.4%
. 10019
 
6.5%
1 9683
 
6.3%
2 4815
 
3.1%
3 3635
 
2.4%
구 3562
 
2.3%
4 3532
 
2.3%
5 3367
 
2.2%
6 3335
 
2.2%
앞 3313
 
2.2%
Other values (486) 87280
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79394
51.9%
Decimal Number 39968
26.1%
Space Separator 20539
 
13.4%
Other Punctuation 10085
 
6.6%
Uppercase Letter 1256
 
0.8%
Open Punctuation 842
 
0.6%
Close Punctuation 842
 
0.6%
Lowercase Letter 78
 
0.1%
Dash Punctuation 55
 
< 0.1%
Connector Punctuation 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3562
 
4.5%
앞 3313
 
4.2%
역 2828
 
3.6%
번 2600
 
3.3%
출 2523
 
3.2%
동 2094
 
2.6%
교 1609
 
2.0%
리 1450
 
1.8%
아 1359
 
1.7%
파 1284
 
1.6%
Other values (434) 56772
71.5%
Uppercase Letter
ValueCountFrequency (%)
S 172
13.7%
K 158
12.6%
C 106
8.4%
B 96
 
7.6%
T 91
 
7.2%
G 87
 
6.9%
I 79
 
6.3%
A 76
 
6.1%
L 75
 
6.0%
P 56
 
4.5%
Other values (12) 260
20.7%
Decimal Number
ValueCountFrequency (%)
1 9683
24.2%
2 4815
12.0%
3 3635
 
9.1%
4 3532
 
8.8%
5 3367
 
8.4%
6 3335
 
8.3%
7 3245
 
8.1%
0 3083
 
7.7%
9 2687
 
6.7%
8 2586
 
6.5%
Lowercase Letter
ValueCountFrequency (%)
e 26
33.3%
t 14
17.9%
k 13
16.7%
n 8
 
10.3%
l 6
 
7.7%
y 4
 
5.1%
c 2
 
2.6%
m 2
 
2.6%
o 2
 
2.6%
s 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 10019
99.3%
, 58
 
0.6%
& 4
 
< 0.1%
? 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
20539
100.0%
Open Punctuation
ValueCountFrequency (%)
( 842
100.0%
Close Punctuation
ValueCountFrequency (%)
) 842
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79394
51.9%
Common 72352
47.3%
Latin 1334
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3562
 
4.5%
앞 3313
 
4.2%
역 2828
 
3.6%
번 2600
 
3.3%
출 2523
 
3.2%
동 2094
 
2.6%
교 1609
 
2.0%
리 1450
 
1.8%
아 1359
 
1.7%
파 1284
 
1.6%
Other values (434) 56772
71.5%
Latin
ValueCountFrequency (%)
S 172
12.9%
K 158
11.8%
C 106
 
7.9%
B 96
 
7.2%
T 91
 
6.8%
G 87
 
6.5%
I 79
 
5.9%
A 76
 
5.7%
L 75
 
5.6%
P 56
 
4.2%
Other values (22) 338
25.3%
Common
ValueCountFrequency (%)
20539
28.4%
. 10019
13.8%
1 9683
13.4%
2 4815
 
6.7%
3 3635
 
5.0%
4 3532
 
4.9%
5 3367
 
4.7%
6 3335
 
4.6%
7 3245
 
4.5%
0 3083
 
4.3%
Other values (10) 7099
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79394
51.9%
ASCII 73686
48.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20539
27.9%
. 10019
13.6%
1 9683
13.1%
2 4815
 
6.5%
3 3635
 
4.9%
4 3532
 
4.8%
5 3367
 
4.6%
6 3335
 
4.5%
7 3245
 
4.4%
0 3083
 
4.2%
Other values (42) 8433
11.4%
Hangul
ValueCountFrequency (%)
구 3562
 
4.5%
앞 3313
 
4.2%
역 2828
 
3.6%
번 2600
 
3.3%
출 2523
 
3.2%
동 2094
 
2.6%
교 1609
 
2.0%
리 1450
 
1.8%
아 1359
 
1.7%
파 1284
 
1.6%
Other values (434) 56772
71.5%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7221 
일일(회원)
2536 
일일(비회원)
 
126
단체
 
98
BIL_021
 
19

Length

Max length7
Median length2
Mean length3.0869
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기
2nd row정기
3rd row정기
4th row정기
5th row정기

Common Values

ValueCountFrequency (%)
정기 7221
72.2%
일일(회원) 2536
 
25.4%
일일(비회원) 126
 
1.3%
단체 98
 
1.0%
BIL_021 19
 
0.2%

Length

2024-03-14T01:25:46.684356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:46.782187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7221
72.2%
일일(회원 2536
 
25.4%
일일(비회원 126
 
1.3%
단체 98
 
1.0%
bil_021 19
 
0.2%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3634 
\N
3427 
F
2300 
<NA>
636 
f
 
2

Length

Max length4
Median length1
Mean length1.5335
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3634
36.3%
\N 3427
34.3%
F 2300
23.0%
<NA> 636
 
6.4%
f 2
 
< 0.1%
m 1
 
< 0.1%

Length

2024-03-14T01:25:46.903624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:47.009801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3635
36.4%
n 3427
34.3%
f 2302
23.0%
na 636
 
6.4%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2902 
AGE_003
2041 
AGE_004
1548 
AGE_008
1211 
AGE_005
1147 
Other values (3)
1151 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_002
2nd rowAGE_003
3rd rowAGE_003
4th rowAGE_002
5th rowAGE_008

Common Values

ValueCountFrequency (%)
AGE_002 2902
29.0%
AGE_003 2041
20.4%
AGE_004 1548
15.5%
AGE_008 1211
12.1%
AGE_005 1147
 
11.5%
AGE_001 661
 
6.6%
AGE_006 418
 
4.2%
AGE_007 72
 
0.7%

Length

2024-03-14T01:25:47.101741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:47.190724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2902
29.0%
age_003 2041
20.4%
age_004 1548
15.5%
age_008 1211
12.1%
age_005 1147
 
11.5%
age_001 661
 
6.6%
age_006 418
 
4.2%
age_007 72
 
0.7%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7335
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:47.288600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum21
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.375815
Coefficient of variation (CV)0.79366313
Kurtosis21.209307
Mean1.7335
Median Absolute Deviation (MAD)0
Skewness3.6013021
Sum17335
Variance1.892867
MonotonicityNot monotonic
2024-03-14T01:25:47.378306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 6244
62.4%
2 2075
 
20.8%
3 868
 
8.7%
4 401
 
4.0%
5 165
 
1.7%
6 94
 
0.9%
7 58
 
0.6%
8 41
 
0.4%
9 18
 
0.2%
10 8
 
0.1%
Other values (7) 28
 
0.3%
ValueCountFrequency (%)
1 6244
62.4%
2 2075
 
20.8%
3 868
 
8.7%
4 401
 
4.0%
5 165
 
1.7%
6 94
 
0.9%
7 58
 
0.6%
8 41
 
0.4%
9 18
 
0.2%
10 8
 
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
16 3
 
< 0.1%
15 2
 
< 0.1%
14 3
 
< 0.1%
13 6
 
0.1%
12 6
 
0.1%
11 7
 
0.1%
10 8
 
0.1%
9 18
0.2%
8 41
0.4%

운동량
Text

Distinct7513
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:47.666654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.9821
Min length1

Characters and Unicode

Total characters49821
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

Unique6136 ?
Unique (%)61.4%

Sample

1st row21.51
2nd row80.24
3rd row31.1
4th row227.54
5th row0
ValueCountFrequency (%)
0 623
 
6.2%
n 48
 
0.5%
44.79 7
 
0.1%
2.27 7
 
0.1%
46.85 7
 
0.1%
27.28 7
 
0.1%
30.89 6
 
0.1%
18.83 6
 
0.1%
19.31 6
 
0.1%
57.02 6
 
0.1%
Other values (7503) 9277
92.8%
2024-03-14T01:25:48.049332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9235
18.5%
1 5995
12.0%
2 5108
10.3%
3 4393
8.8%
4 4062
8.2%
5 3860
7.7%
6 3696
7.4%
7 3569
 
7.2%
8 3545
 
7.1%
9 3338
 
6.7%
Other values (3) 3020
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40490
81.3%
Other Punctuation 9283
 
18.6%
Uppercase Letter 48
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5995
14.8%
2 5108
12.6%
3 4393
10.8%
4 4062
10.0%
5 3860
9.5%
6 3696
9.1%
7 3569
8.8%
8 3545
8.8%
9 3338
8.2%
0 2924
7.2%
Other Punctuation
ValueCountFrequency (%)
. 9235
99.5%
\ 48
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49773
99.9%
Latin 48
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9235
18.6%
1 5995
12.0%
2 5108
10.3%
3 4393
8.8%
4 4062
8.2%
5 3860
7.8%
6 3696
7.4%
7 3569
 
7.2%
8 3545
 
7.1%
9 3338
 
6.7%
Other values (2) 2972
 
6.0%
Latin
ValueCountFrequency (%)
N 48
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9235
18.5%
1 5995
12.0%
2 5108
10.3%
3 4393
8.8%
4 4062
8.2%
5 3860
7.7%
6 3696
7.4%
7 3569
 
7.2%
8 3545
 
7.1%
9 3338
 
6.7%
Other values (3) 3020
 
6.1%

탄소량
Text

Distinct638
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:48.369343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7025
Min length1

Characters and Unicode

Total characters37025
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

Unique158 ?
Unique (%)1.6%

Sample

1st row0.22
2nd row0.59
3rd row0.26
4th row2.3
5th row0
ValueCountFrequency (%)
0 627
 
6.3%
0.21 127
 
1.3%
0.23 108
 
1.1%
0.27 105
 
1.1%
0.19 104
 
1.0%
0.28 103
 
1.0%
0.22 102
 
1.0%
0.32 99
 
1.0%
0.14 99
 
1.0%
0.16 98
 
1.0%
Other values (628) 8428
84.3%
2024-03-14T01:25:48.796148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9266
25.0%
0 7273
19.6%
1 4177
11.3%
2 3104
 
8.4%
3 2528
 
6.8%
4 2210
 
6.0%
5 2016
 
5.4%
6 1687
 
4.6%
7 1661
 
4.5%
8 1538
 
4.2%
Other values (3) 1565
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27663
74.7%
Other Punctuation 9314
 
25.2%
Uppercase Letter 48
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7273
26.3%
1 4177
15.1%
2 3104
11.2%
3 2528
 
9.1%
4 2210
 
8.0%
5 2016
 
7.3%
6 1687
 
6.1%
7 1661
 
6.0%
8 1538
 
5.6%
9 1469
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 9266
99.5%
\ 48
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36977
99.9%
Latin 48
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9266
25.1%
0 7273
19.7%
1 4177
11.3%
2 3104
 
8.4%
3 2528
 
6.8%
4 2210
 
6.0%
5 2016
 
5.5%
6 1687
 
4.6%
7 1661
 
4.5%
8 1538
 
4.2%
Other values (2) 1517
 
4.1%
Latin
ValueCountFrequency (%)
N 48
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9266
25.0%
0 7273
19.6%
1 4177
11.3%
2 3104
 
8.4%
3 2528
 
6.8%
4 2210
 
6.0%
5 2016
 
5.4%
6 1687
 
4.6%
7 1661
 
4.5%
8 1538
 
4.2%
Other values (3) 1565
 
4.2%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8220
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4555.3617
Minimum0
Maximum129483.61
Zeros668
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:48.918130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11151.55
median2579.97
Q35730.445
95-th percentile15532.765
Maximum129483.61
Range129483.61
Interquartile range (IQR)4578.895

Descriptive statistics

Standard deviation5825.8189
Coefficient of variation (CV)1.2788927
Kurtosis39.476679
Mean4555.3617
Median Absolute Deviation (MAD)1776.22
Skewness4.1160271
Sum45553617
Variance33940166
MonotonicityNot monotonic
2024-03-14T01:25:49.091913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 668
 
6.7%
800.0 13
 
0.1%
1300.0 12
 
0.1%
790.0 12
 
0.1%
910.0 11
 
0.1%
750.0 11
 
0.1%
1200.0 11
 
0.1%
1720.0 10
 
0.1%
111.2 10
 
0.1%
900.0 10
 
0.1%
Other values (8210) 9232
92.3%
ValueCountFrequency (%)
0.0 668
6.7%
0.1 3
 
< 0.1%
0.2 1
 
< 0.1%
0.26 1
 
< 0.1%
0.39 1
 
< 0.1%
0.93 1
 
< 0.1%
30.0 1
 
< 0.1%
40.0 2
 
< 0.1%
60.0 1
 
< 0.1%
80.0 1
 
< 0.1%
ValueCountFrequency (%)
129483.61 1
< 0.1%
88806.74 1
< 0.1%
77786.95 1
< 0.1%
66815.73 1
< 0.1%
61391.31 1
< 0.1%
57967.7 1
< 0.1%
54784.52 1
< 0.1%
53830.85 1
< 0.1%
51003.55 1
< 0.1%
50702.24 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.9029
Minimum0
Maximum881
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:49.212869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q111
median25
Q356
95-th percentile134
Maximum881
Range881
Interquartile range (IQR)45

Descriptive statistics

Standard deviation52.114816
Coefficient of variation (CV)1.2147155
Kurtosis32.415054
Mean42.9029
Median Absolute Deviation (MAD)17
Skewness3.9805868
Sum429029
Variance2715.9541
MonotonicityNot monotonic
2024-03-14T01:25:49.322214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 329
 
3.3%
6 300
 
3.0%
4 288
 
2.9%
8 287
 
2.9%
9 281
 
2.8%
7 280
 
2.8%
11 266
 
2.7%
10 265
 
2.6%
13 249
 
2.5%
3 234
 
2.3%
Other values (301) 7221
72.2%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 23
 
0.2%
2 126
 
1.3%
3 234
2.3%
4 288
2.9%
5 329
3.3%
6 300
3.0%
7 280
2.8%
8 287
2.9%
9 281
2.8%
ValueCountFrequency (%)
881 1
< 0.1%
834 1
< 0.1%
761 1
< 0.1%
725 1
< 0.1%
640 1
< 0.1%
607 1
< 0.1%
599 1
< 0.1%
541 1
< 0.1%
527 1
< 0.1%
495 1
< 0.1%

Interactions

2024-03-14T01:25:44.975652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:43.956873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.260987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.590497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:45.069654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.033876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.340037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.696413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:45.156612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.115528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.418209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.783484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:45.229930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.191007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.494977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:44.873297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:25:49.389830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0790.0590.0620.0540.0640.098
대여구분코드0.0791.0000.1840.3060.1160.0620.129
성별0.0590.1841.0000.1170.0750.0000.000
연령대코드0.0620.3060.1171.0000.1680.0610.050
이용건수0.0540.1160.0750.1681.0000.4980.501
이동거리(M)0.0640.0620.0000.0610.4981.0000.692
이용시간(분)0.0980.1290.0000.0500.5010.6921.000
2024-03-14T01:25:49.475792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.0700.193
성별0.0701.0000.072
연령대코드0.1930.0721.000
2024-03-14T01:25:49.547860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.015-0.016-0.0380.0330.0250.029
이용건수-0.0151.0000.5200.5430.0560.0400.070
이동거리(M)-0.0160.5201.0000.8000.0380.0000.021
이용시간(분)-0.0380.5430.8001.0000.0540.0000.024
대여구분코드0.0330.0560.0380.0541.0000.0700.193
성별0.0250.0400.0000.0000.0701.0000.072
연령대코드0.0290.0700.0210.0240.1930.0721.000

Missing values

2024-03-14T01:25:45.320378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:25:45.677449image/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)
152482021-05-0119411941. 오류동역 2번출구정기\NAGE_002121.510.22952.8110
150472021-05-0119121912. 한신휴아파트 앞정기MAGE_003180.240.592532.8714
146542021-05-0118281828. 한양수자인아파트 앞정기\NAGE_003131.10.261106.076
87512021-05-0110601060. 천일초교 사거리정기\NAGE_0022227.542.39906.9293
116652021-05-0113651365. 선잠단지 앞정기FAGE_0081000.010
119612021-05-0114051405. 망우역 1번출구정기\NAGE_0024121.071.175026.740
47152021-05-01589589. 성수역3번출구일일(회원)MAGE_003114.220.13561.17
49512021-05-01622622. 전농사거리 교통섬정기\NAGE_001382.970.93857.045
33082021-05-01426426. 서울신용보증재단일일(회원)FAGE_003154.270.572447.1520
6512021-05-01157157. 애오개역 4번출구 앞일일(회원)\NAGE_002357.950.642780.019
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
14922021-05-01232232. 양평우림 이비즈센타 앞정기MAGE_0061329.432.4410530.4647
58282021-05-01723723. SBS방송국일일(회원)MAGE_001145.190.411755.4616
14202021-05-01229229. 양평1 보행육교 앞정기\NAGE_0031233.522.3610167.0463
142742021-05-0117421742. 북한산 코오롱 하늘채정기\NAGE_0034215.481.978468.5949
26812021-05-01338338. 세운스퀘어 앞정기MAGE_0041000.03
122122021-05-0114401440. 하나은행 면목지점정기MAGE_003127.280.251060.06
71602021-05-01831831. 이태원관광특구입구일일(회원)FAGE_002145.080.492108.29
60422021-05-01739739. 신월사거리정기MAGE_0061360.321398.448
106672021-05-0112331233. 잠실3거리(갤러리아팰리스)일일(회원)\NAGE_004195.980.62606.1933
153272021-05-0119551955. 디지털입구 교차로일일(회원)MAGE_002127.220.21892.637