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/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
대여구분코드 is highly imbalanced (56.3%)Imbalance
이동거리(M) has 663 (6.6%) zerosZeros

Reproduction

Analysis started2024-05-18 05:01:52.588978
Analysis finished2024-05-18 05:02:02.196272
Duration9.61 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
2024-05-18T14:02:02.421411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:02.788692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct1375
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean989.7441
Minimum102
Maximum2057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:02:03.209547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile176.95
Q1521
median974
Q31428.25
95-th percentile1953
Maximum2057
Range1955
Interquartile range (IQR)907.25

Descriptive statistics

Standard deviation558.15195
Coefficient of variation (CV)0.56393562
Kurtosis-1.0857501
Mean989.7441
Median Absolute Deviation (MAD)453.5
Skewness0.17193045
Sum9897441
Variance311533.6
MonotonicityNot monotonic
2024-05-18T14:02:03.737133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792 24
 
0.2%
284 23
 
0.2%
152 23
 
0.2%
207 23
 
0.2%
502 22
 
0.2%
1166 22
 
0.2%
247 22
 
0.2%
1153 22
 
0.2%
744 21
 
0.2%
770 20
 
0.2%
Other values (1365) 9778
97.8%
ValueCountFrequency (%)
102 11
0.1%
103 13
0.1%
104 9
0.1%
105 6
0.1%
106 10
0.1%
107 11
0.1%
108 8
0.1%
109 6
0.1%
111 7
0.1%
112 7
0.1%
ValueCountFrequency (%)
2057 6
 
0.1%
2056 8
0.1%
2054 8
0.1%
2050 15
0.1%
2048 6
 
0.1%
2041 3
 
< 0.1%
2038 3
 
< 0.1%
2037 10
0.1%
2036 3
 
< 0.1%
2034 8
0.1%

대여소
Text

Distinct1375
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:02:04.340799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length15.3058
Min length7

Characters and Unicode

Total characters153058
Distinct characters500
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

Unique77 ?
Unique (%)0.8%

Sample

1st row512. 뚝섬역 1번 출구 옆
2nd row901. 응암1동사무소
3rd row1661. 당현천근린공원
4th row433. 을지로입구역 2번출구
5th row179. 가좌역 4번출구 앞
ValueCountFrequency (%)
앞 2782
 
9.2%
옆 547
 
1.8%
출구 388
 
1.3%
1번출구 361
 
1.2%
3번출구 253
 
0.8%
사거리 239
 
0.8%
4번출구 233
 
0.8%
2번출구 228
 
0.8%
교차로 222
 
0.7%
뒤 197
 
0.6%
Other values (2849) 24940
82.1%
2024-05-18T14:02:05.432671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20572
 
13.4%
. 10021
 
6.5%
1 9724
 
6.4%
2 4803
 
3.1%
3 3651
 
2.4%
구 3537
 
2.3%
4 3513
 
2.3%
5 3373
 
2.2%
앞 3345
 
2.2%
7 3298
 
2.2%
Other values (490) 87221
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79230
51.8%
Decimal Number 40075
26.2%
Space Separator 20572
 
13.4%
Other Punctuation 10103
 
6.6%
Uppercase Letter 1284
 
0.8%
Open Punctuation 808
 
0.5%
Close Punctuation 808
 
0.5%
Lowercase Letter 101
 
0.1%
Dash Punctuation 55
 
< 0.1%
Connector Punctuation 12
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3537
 
4.5%
앞 3345
 
4.2%
역 2796
 
3.5%
번 2597
 
3.3%
출 2503
 
3.2%
동 2168
 
2.7%
교 1559
 
2.0%
리 1441
 
1.8%
아 1385
 
1.7%
트 1325
 
1.7%
Other values (437) 56574
71.4%
Uppercase Letter
ValueCountFrequency (%)
S 162
12.6%
K 155
12.1%
C 107
8.3%
G 97
 
7.6%
B 95
 
7.4%
T 91
 
7.1%
A 89
 
6.9%
L 86
 
6.7%
I 82
 
6.4%
P 54
 
4.2%
Other values (12) 266
20.7%
Decimal Number
ValueCountFrequency (%)
1 9724
24.3%
2 4803
12.0%
3 3651
 
9.1%
4 3513
 
8.8%
5 3373
 
8.4%
7 3298
 
8.2%
6 3281
 
8.2%
0 3128
 
7.8%
9 2712
 
6.8%
8 2592
 
6.5%
Lowercase Letter
ValueCountFrequency (%)
e 29
28.7%
t 17
16.8%
k 14
13.9%
n 12
11.9%
l 10
 
9.9%
y 6
 
5.9%
m 4
 
4.0%
o 4
 
4.0%
c 4
 
4.0%
s 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 10021
99.2%
, 69
 
0.7%
? 7
 
0.1%
& 6
 
0.1%
Space Separator
ValueCountFrequency (%)
20572
100.0%
Open Punctuation
ValueCountFrequency (%)
( 808
100.0%
Close Punctuation
ValueCountFrequency (%)
) 808
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79231
51.8%
Common 72442
47.3%
Latin 1385
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3537
 
4.5%
앞 3345
 
4.2%
역 2796
 
3.5%
번 2597
 
3.3%
출 2503
 
3.2%
동 2168
 
2.7%
교 1559
 
2.0%
리 1441
 
1.8%
아 1385
 
1.7%
트 1325
 
1.7%
Other values (438) 56575
71.4%
Latin
ValueCountFrequency (%)
S 162
11.7%
K 155
11.2%
C 107
 
7.7%
G 97
 
7.0%
B 95
 
6.9%
T 91
 
6.6%
A 89
 
6.4%
L 86
 
6.2%
I 82
 
5.9%
P 54
 
3.9%
Other values (22) 367
26.5%
Common
ValueCountFrequency (%)
20572
28.4%
. 10021
13.8%
1 9724
13.4%
2 4803
 
6.6%
3 3651
 
5.0%
4 3513
 
4.8%
5 3373
 
4.7%
7 3298
 
4.6%
6 3281
 
4.5%
0 3128
 
4.3%
Other values (10) 7078
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79230
51.8%
ASCII 73827
48.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20572
27.9%
. 10021
13.6%
1 9724
13.2%
2 4803
 
6.5%
3 3651
 
4.9%
4 3513
 
4.8%
5 3373
 
4.6%
7 3298
 
4.5%
6 3281
 
4.4%
0 3128
 
4.2%
Other values (42) 8463
11.5%
Hangul
ValueCountFrequency (%)
구 3537
 
4.5%
앞 3345
 
4.2%
역 2796
 
3.5%
번 2597
 
3.3%
출 2503
 
3.2%
동 2168
 
2.7%
교 1559
 
2.0%
리 1441
 
1.8%
아 1385
 
1.7%
트 1325
 
1.7%
Other values (437) 56574
71.4%
None
ValueCountFrequency (%)
㈜ 1
100.0%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7152 
일일(회원)
2601 
일일(비회원)
 
118
단체
 
111
BIL_021
 
18

Length

Max length7
Median length2
Mean length3.1084
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 7152
71.5%
일일(회원) 2601
 
26.0%
일일(비회원) 118
 
1.2%
단체 111
 
1.1%
BIL_021 18
 
0.2%

Length

2024-05-18T14:02:05.818785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:02:06.153118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7152
71.5%
일일(회원 2601
 
26.0%
일일(비회원 118
 
1.2%
단체 111
 
1.1%
bil_021 18
 
0.2%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3625 
\N
3349 
F
2411 
<NA>
611 
m
 
2

Length

Max length4
Median length1
Mean length1.5182
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 3625
36.2%
\N 3349
33.5%
F 2411
24.1%
<NA> 611
 
6.1%
m 2
 
< 0.1%
f 2
 
< 0.1%

Length

2024-05-18T14:02:06.504225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:02:06.815034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3627
36.3%
n 3349
33.5%
f 2413
24.1%
na 611
 
6.1%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2906 
AGE_003
1977 
AGE_004
1568 
AGE_008
1275 
AGE_005
1128 
Other values (3)
1146 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_001
2nd rowAGE_008
3rd rowAGE_002
4th rowAGE_008
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 2906
29.1%
AGE_003 1977
19.8%
AGE_004 1568
15.7%
AGE_008 1275
12.8%
AGE_005 1128
 
11.3%
AGE_001 636
 
6.4%
AGE_006 439
 
4.4%
AGE_007 71
 
0.7%

Length

2024-05-18T14:02:07.130433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:02:07.571336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2906
29.1%
age_003 1977
19.8%
age_004 1568
15.7%
age_008 1275
12.8%
age_005 1128
 
11.3%
age_001 636
 
6.4%
age_006 439
 
4.4%
age_007 71
 
0.7%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7485
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:02:08.021321image/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 deviation1.4207919
Coefficient of variation (CV)0.81257758
Kurtosis22.952938
Mean1.7485
Median Absolute Deviation (MAD)0
Skewness3.7484129
Sum17485
Variance2.0186496
MonotonicityNot monotonic
2024-05-18T14:02:08.548532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 6211
62.1%
2 2105
 
21.1%
3 871
 
8.7%
4 372
 
3.7%
5 165
 
1.7%
6 106
 
1.1%
7 69
 
0.7%
8 38
 
0.4%
9 22
 
0.2%
10 9
 
0.1%
Other values (9) 32
 
0.3%
ValueCountFrequency (%)
1 6211
62.1%
2 2105
 
21.1%
3 871
 
8.7%
4 372
 
3.7%
5 165
 
1.7%
6 106
 
1.1%
7 69
 
0.7%
8 38
 
0.4%
9 22
 
0.2%
10 9
 
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 3
 
< 0.1%
15 2
 
< 0.1%
14 3
 
< 0.1%
13 6
0.1%
12 6
0.1%
11 9
0.1%
10 9
0.1%

운동량
Text

Distinct7550
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:02:09.587625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.9896
Min length1

Characters and Unicode

Total characters49896
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

Unique6165 ?
Unique (%)61.7%

Sample

1st row60.92
2nd row18.4
3rd row276.41
4th row19.31
5th row21.04
ValueCountFrequency (%)
0 615
 
6.2%
n 52
 
0.5%
28.83 7
 
0.1%
18.83 6
 
0.1%
17.76 6
 
0.1%
23.42 6
 
0.1%
27.28 6
 
0.1%
46.85 6
 
0.1%
49.16 6
 
0.1%
8.43 5
 
< 0.1%
Other values (7540) 9285
92.8%
2024-05-18T14:02:10.963428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9242
18.5%
1 6054
12.1%
2 4996
10.0%
3 4446
8.9%
4 4113
8.2%
5 3864
7.7%
6 3690
 
7.4%
7 3587
 
7.2%
8 3514
 
7.0%
9 3358
 
6.7%
Other values (3) 3032
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40550
81.3%
Other Punctuation 9294
 
18.6%
Uppercase Letter 52
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6054
14.9%
2 4996
12.3%
3 4446
11.0%
4 4113
10.1%
5 3864
9.5%
6 3690
9.1%
7 3587
8.8%
8 3514
8.7%
9 3358
8.3%
0 2928
7.2%
Other Punctuation
ValueCountFrequency (%)
. 9242
99.4%
\ 52
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
99.9%
Latin 52
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9242
18.5%
1 6054
12.1%
2 4996
10.0%
3 4446
8.9%
4 4113
8.3%
5 3864
7.8%
6 3690
 
7.4%
7 3587
 
7.2%
8 3514
 
7.0%
9 3358
 
6.7%
Other values (2) 2980
 
6.0%
Latin
ValueCountFrequency (%)
N 52
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9242
18.5%
1 6054
12.1%
2 4996
10.0%
3 4446
8.9%
4 4113
8.2%
5 3864
7.7%
6 3690
 
7.4%
7 3587
 
7.2%
8 3514
 
7.0%
9 3358
 
6.7%
Other values (3) 3032
 
6.1%

탄소량
Text

Distinct653
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:02:12.042580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7021
Min length1

Characters and Unicode

Total characters37021
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

Unique188 ?
Unique (%)1.9%

Sample

1st row0.7
2nd row0.19
3rd row1.97
4th row0.17
5th row0.19
ValueCountFrequency (%)
0 620
 
6.2%
0.19 114
 
1.1%
0.16 110
 
1.1%
0.21 110
 
1.1%
0.22 101
 
1.0%
0.28 99
 
1.0%
0.23 98
 
1.0%
0.31 96
 
1.0%
0.14 96
 
1.0%
0.26 94
 
0.9%
Other values (643) 8462
84.6%
2024-05-18T14:02:13.241054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9264
25.0%
0 7257
19.6%
1 4234
11.4%
2 3070
 
8.3%
3 2487
 
6.7%
4 2184
 
5.9%
5 2020
 
5.5%
6 1759
 
4.8%
7 1644
 
4.4%
8 1537
 
4.2%
Other values (3) 1565
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27653
74.7%
Other Punctuation 9316
 
25.2%
Uppercase Letter 52
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7257
26.2%
1 4234
15.3%
2 3070
11.1%
3 2487
 
9.0%
4 2184
 
7.9%
5 2020
 
7.3%
6 1759
 
6.4%
7 1644
 
5.9%
8 1537
 
5.6%
9 1461
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 9264
99.4%
\ 52
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36969
99.9%
Latin 52
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9264
25.1%
0 7257
19.6%
1 4234
11.5%
2 3070
 
8.3%
3 2487
 
6.7%
4 2184
 
5.9%
5 2020
 
5.5%
6 1759
 
4.8%
7 1644
 
4.4%
8 1537
 
4.2%
Other values (2) 1513
 
4.1%
Latin
ValueCountFrequency (%)
N 52
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37021
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9264
25.0%
0 7257
19.6%
1 4234
11.4%
2 3070
 
8.3%
3 2487
 
6.7%
4 2184
 
5.9%
5 2020
 
5.5%
6 1759
 
4.8%
7 1644
 
4.4%
8 1537
 
4.2%
Other values (3) 1565
 
4.2%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8212
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4614.2623
Minimum0
Maximum129483.61
Zeros663
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:02:13.757566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11175.8675
median2632.14
Q35751.765
95-th percentile15588.199
Maximum129483.61
Range129483.61
Interquartile range (IQR)4575.8975

Descriptive statistics

Standard deviation5993.045
Coefficient of variation (CV)1.2988089
Kurtosis38.997939
Mean4614.2623
Median Absolute Deviation (MAD)1812.66
Skewness4.2363125
Sum46142623
Variance35916588
MonotonicityNot monotonic
2024-05-18T14:02:14.200009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 663
 
6.6%
800.0 14
 
0.1%
111.2 11
 
0.1%
750.0 11
 
0.1%
830.0 11
 
0.1%
820.0 9
 
0.1%
900.0 9
 
0.1%
840.0 9
 
0.1%
690.0 9
 
0.1%
1140.0 9
 
0.1%
Other values (8202) 9245
92.5%
ValueCountFrequency (%)
0.0 663
6.6%
0.1 4
 
< 0.1%
0.2 1
 
< 0.1%
0.26 1
 
< 0.1%
0.39 1
 
< 0.1%
0.48 1
 
< 0.1%
0.93 1
 
< 0.1%
30.0 1
 
< 0.1%
40.0 2
 
< 0.1%
50.0 2
 
< 0.1%
ValueCountFrequency (%)
129483.61 1
< 0.1%
88806.74 1
< 0.1%
77786.95 1
< 0.1%
73651.88 1
< 0.1%
66815.73 1
< 0.1%
61391.31 1
< 0.1%
58548.43 1
< 0.1%
57967.7 1
< 0.1%
54784.52 1
< 0.1%
54240.0 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct316
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.2048
Minimum0
Maximum881
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:02:14.629703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q111
median26
Q356
95-th percentile136
Maximum881
Range881
Interquartile range (IQR)45

Descriptive statistics

Standard deviation52.331561
Coefficient of variation (CV)1.2112442
Kurtosis29.923987
Mean43.2048
Median Absolute Deviation (MAD)18
Skewness3.8859885
Sum432048
Variance2738.5923
MonotonicityNot monotonic
2024-05-18T14:02:15.071456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 313
 
3.1%
4 282
 
2.8%
10 280
 
2.8%
6 277
 
2.8%
7 276
 
2.8%
8 262
 
2.6%
9 258
 
2.6%
11 245
 
2.5%
13 237
 
2.4%
3 230
 
2.3%
Other values (306) 7340
73.4%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 30
 
0.3%
2 129
1.3%
3 230
2.3%
4 282
2.8%
5 313
3.1%
6 277
2.8%
7 276
2.8%
8 262
2.6%
9 258
2.6%
ValueCountFrequency (%)
881 1
< 0.1%
834 1
< 0.1%
761 1
< 0.1%
626 1
< 0.1%
618 1
< 0.1%
559 1
< 0.1%
541 1
< 0.1%
527 1
< 0.1%
514 1
< 0.1%
495 1
< 0.1%

Interactions

2024-05-18T14:01:59.963748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:56.165607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:57.442916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:58.593858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:00.333237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:56.498412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:57.740563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:58.878534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:00.742044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:56.865662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:58.038190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:59.429113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:01.033772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:57.177097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:58.328185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:59.704066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:02:15.349356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0820.0720.0430.0810.0730.103
대여구분코드0.0821.0000.1850.3030.1670.0690.149
성별0.0720.1851.0000.1170.1220.0000.000
연령대코드0.0430.3030.1171.0000.1760.0490.040
이용건수0.0810.1670.1220.1761.0000.5040.720
이동거리(M)0.0730.0690.0000.0490.5041.0000.732
이용시간(분)0.1030.1490.0000.0400.7200.7321.000
2024-05-18T14:02:15.881342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0720.191
성별0.0721.0000.070
대여구분코드0.1910.0701.000
2024-05-18T14:02:16.133365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.031-0.033-0.0530.0340.0300.020
이용건수-0.0311.0000.5270.5420.0530.0500.073
이동거리(M)-0.0330.5271.0000.7980.0420.0000.016
이용시간(분)-0.0530.5420.7981.0000.0620.0000.019
대여구분코드0.0340.0530.0420.0621.0000.0700.191
성별0.0300.0500.0000.0000.0701.0000.072
연령대코드0.0200.0730.0160.0190.1910.0721.000

Missing values

2024-05-18T14:02:01.410379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:02:01.945602image/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)
39212021-05-01512512. 뚝섬역 1번 출구 옆정기FAGE_001160.920.73016.5225
74952021-05-01901901. 응암1동사무소정기FAGE_008118.40.19801.09161
135792021-05-0116611661. 당현천근린공원정기MAGE_0023276.411.978530.8970
33612021-05-01433433. 을지로입구역 2번출구일일(비회원)\NAGE_008119.310.17750.07
8262021-05-01179179. 가좌역 4번출구 앞정기FAGE_002121.040.19830.07
129262021-05-0115431543. 수유1동 주민센터정기FAGE_0024208.571.938286.1684
54722021-05-01676676.FITI시험연구원 앞일일(회원)<NA>AGE_002158.310.572454.1114
147122021-05-0118351835. STX V타워일일(회원)\NAGE_0031000.09
136552021-05-0116671667. 중계중학교정기MAGE_003246.540.381652.8210
102892021-05-0112011201. 가락시장역 3번 출구일일(회원)<NA>AGE_002146.170.512200.013
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
148472021-05-0118591859. 대륭테크노타운 18차정기\NAGE_002133.510.361538.388
45012021-05-01572572. 국립정신 건강센터 앞정기\NAGE_003149.280.441914.5626
108062021-05-0112531253. 오금역 3번 출구 뒤일일(회원)FAGE_002128.010.321360.09
62732021-05-01757757. 신정이펜하우스 1단지아파트 입구 사거리정기\NAGE_0024181.411.636992.7262
43612021-05-01560560. 비전교회 앞일일(회원)MAGE_002179.860.672880.815
26412021-05-01329329. 청계2가 사거리 옆정기MAGE_0041334.182.189376.53111
38502021-05-01507507. 성수아이에스비즈타워 앞일일(회원)\NAGE_0021184.0328605.9755
61902021-05-01750750. 연의근린공원 건너편일일(회원)FAGE_002156.320.592539.827
64082021-05-01766766. 신목동역 3번출구정기FAGE_003174.230.974165.75114
17542021-05-01249249. 여의도중학교 옆정기FAGE_0021233.662.119077.6540