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-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 514 (5.1%) zerosZeros

Reproduction

Analysis started2024-05-11 02:38:00.532437
Analysis finished2024-05-11 02:38:09.984510
Duration9.45 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
Minimum2022-01-01 00:00:00
Maximum2022-01-01 00:00:00
2024-05-11T02:38:10.146711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:10.612650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct2476
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2025.794
Minimum102
Maximum4892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:38:11.000455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile236
Q1858
median1716
Q33107
95-th percentile4582
Maximum4892
Range4790
Interquartile range (IQR)2249

Descriptive statistics

Standard deviation1385.6202
Coefficient of variation (CV)0.68398871
Kurtosis-0.90274102
Mean2025.794
Median Absolute Deviation (MAD)976
Skewness0.52670993
Sum20257940
Variance1919943.4
MonotonicityNot monotonic
2024-05-11T02:38:11.901730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1961 12
 
0.1%
677 12
 
0.1%
613 11
 
0.1%
1106 11
 
0.1%
2210 11
 
0.1%
2921 11
 
0.1%
1470 11
 
0.1%
565 11
 
0.1%
102 11
 
0.1%
769 10
 
0.1%
Other values (2466) 9889
98.9%
ValueCountFrequency (%)
102 11
0.1%
103 5
0.1%
104 2
 
< 0.1%
105 4
 
< 0.1%
106 2
 
< 0.1%
107 8
0.1%
108 2
 
< 0.1%
109 2
 
< 0.1%
111 5
0.1%
113 6
0.1%
ValueCountFrequency (%)
4892 4
< 0.1%
4891 5
0.1%
4889 2
 
< 0.1%
4888 5
0.1%
4887 2
 
< 0.1%
4886 5
0.1%
4885 1
 
< 0.1%
4884 5
0.1%
4883 4
< 0.1%
4882 2
 
< 0.1%

대여소명
Text

Distinct2476
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:38:12.706778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.5178
Min length7

Characters and Unicode

Total characters155178
Distinct characters581
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique206 ?
Unique (%)2.1%

Sample

1st row536. 행당역 2번출구 앞
2nd row3548.뚝섬역 6번출구
3rd row3131.연가교 교차로
4th row1380.월곡역 5번출구 앞
5th row826. 서울역 서부교차로2
ValueCountFrequency (%)
앞 2647
 
9.1%
출구 432
 
1.5%
옆 385
 
1.3%
입구 255
 
0.9%
1번출구 250
 
0.9%
교차로 242
 
0.8%
사거리 217
 
0.7%
2번출구 205
 
0.7%
3번출구 196
 
0.7%
뒤 167
 
0.6%
Other values (4946) 24194
82.9%
2024-05-11T02:38:14.311534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19368
 
12.5%
. 10037
 
6.5%
1 7987
 
5.1%
2 6152
 
4.0%
3 4948
 
3.2%
4 4694
 
3.0%
5 3572
 
2.3%
0 3537
 
2.3%
6 3407
 
2.2%
구 3202
 
2.1%
Other values (571) 88274
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79977
51.5%
Decimal Number 42580
27.4%
Space Separator 19368
 
12.5%
Other Punctuation 10139
 
6.5%
Uppercase Letter 1256
 
0.8%
Close Punctuation 805
 
0.5%
Open Punctuation 805
 
0.5%
Lowercase Letter 138
 
0.1%
Dash Punctuation 75
 
< 0.1%
Math Symbol 16
 
< 0.1%
Other values (3) 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3202
 
4.0%
앞 3086
 
3.9%
역 2489
 
3.1%
번 2167
 
2.7%
동 2158
 
2.7%
출 2113
 
2.6%
교 1643
 
2.1%
파 1503
 
1.9%
아 1440
 
1.8%
리 1377
 
1.7%
Other values (507) 58799
73.5%
Uppercase Letter
ValueCountFrequency (%)
K 155
12.3%
S 139
11.1%
C 129
10.3%
T 115
9.2%
D 90
 
7.2%
A 82
 
6.5%
G 75
 
6.0%
M 69
 
5.5%
P 64
 
5.1%
B 62
 
4.9%
Other values (13) 276
22.0%
Lowercase Letter
ValueCountFrequency (%)
e 54
39.1%
k 18
 
13.0%
s 17
 
12.3%
t 11
 
8.0%
l 5
 
3.6%
h 4
 
2.9%
f 4
 
2.9%
r 4
 
2.9%
m 4
 
2.9%
o 4
 
2.9%
Other values (6) 13
 
9.4%
Decimal Number
ValueCountFrequency (%)
1 7987
18.8%
2 6152
14.4%
3 4948
11.6%
4 4694
11.0%
5 3572
8.4%
0 3537
8.3%
6 3407
8.0%
7 3125
 
7.3%
8 2755
 
6.5%
9 2403
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 10037
99.0%
, 64
 
0.6%
& 17
 
0.2%
· 13
 
0.1%
? 8
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 11
68.8%
+ 5
31.2%
Other Number
ValueCountFrequency (%)
② 2
50.0%
① 2
50.0%
Space Separator
ValueCountFrequency (%)
19368
100.0%
Close Punctuation
ValueCountFrequency (%)
) 805
100.0%
Open Punctuation
ValueCountFrequency (%)
( 805
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79980
51.5%
Common 73804
47.6%
Latin 1394
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3202
 
4.0%
앞 3086
 
3.9%
역 2489
 
3.1%
번 2167
 
2.7%
동 2158
 
2.7%
출 2113
 
2.6%
교 1643
 
2.1%
파 1503
 
1.9%
아 1440
 
1.8%
리 1377
 
1.7%
Other values (508) 58802
73.5%
Latin
ValueCountFrequency (%)
K 155
 
11.1%
S 139
 
10.0%
C 129
 
9.3%
T 115
 
8.2%
D 90
 
6.5%
A 82
 
5.9%
G 75
 
5.4%
M 69
 
4.9%
P 64
 
4.6%
B 62
 
4.4%
Other values (29) 414
29.7%
Common
ValueCountFrequency (%)
19368
26.2%
. 10037
13.6%
1 7987
10.8%
2 6152
 
8.3%
3 4948
 
6.7%
4 4694
 
6.4%
5 3572
 
4.8%
0 3537
 
4.8%
6 3407
 
4.6%
7 3125
 
4.2%
Other values (14) 6977
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79977
51.5%
ASCII 75181
48.4%
None 16
 
< 0.1%
Enclosed Alphanum 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19368
25.8%
. 10037
13.4%
1 7987
10.6%
2 6152
 
8.2%
3 4948
 
6.6%
4 4694
 
6.2%
5 3572
 
4.8%
0 3537
 
4.7%
6 3407
 
4.5%
7 3125
 
4.2%
Other values (50) 8354
11.1%
Hangul
ValueCountFrequency (%)
구 3202
 
4.0%
앞 3086
 
3.9%
역 2489
 
3.1%
번 2167
 
2.7%
동 2158
 
2.7%
출 2113
 
2.6%
교 1643
 
2.1%
파 1503
 
1.9%
아 1440
 
1.8%
리 1377
 
1.7%
Other values (507) 58799
73.5%
None
ValueCountFrequency (%)
· 13
81.2%
㈜ 3
 
18.8%
Enclosed Alphanum
ValueCountFrequency (%)
② 2
50.0%
① 2
50.0%

대여구분코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5733 
일일(회원)
3683 
단체
 
359
일일(비회원)
 
224
10분이용권
 
1

Length

Max length7
Median length2
Mean length3.5856
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5733
57.3%
일일(회원) 3683
36.8%
단체 359
 
3.6%
일일(비회원) 224
 
2.2%
10분이용권 1
 
< 0.1%

Length

2024-05-11T02:38:14.847873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T02:38:15.261216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5733
57.3%
일일(회원 3683
36.8%
단체 359
 
3.6%
일일(비회원 224
 
2.2%
10분이용권 1
 
< 0.1%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3220 
\N
3002 
F
2694 
<NA>
1082 
m
 
2

Length

Max length4
Median length1
Mean length1.6248
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 3220
32.2%
\N 3002
30.0%
F 2694
26.9%
<NA> 1082
 
10.8%
m 2
 
< 0.1%

Length

2024-05-11T02:38:15.945141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T02:38:16.411667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3222
32.2%
n 3002
30.0%
f 2694
26.9%
na 1082
 
10.8%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
2000 
30대
1772 
40대
1505 
기타
1449 
50대
1292 
Other values (3)
1982 

Length

Max length5
Median length3
Mean length2.9047
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70대이상
2nd row30대
3rd row10대
4th row20대
5th row30대

Common Values

ValueCountFrequency (%)
20대 2000
20.0%
30대 1772
17.7%
40대 1505
15.0%
기타 1449
14.5%
50대 1292
12.9%
10대 1046
10.5%
60대 688
 
6.9%
70대이상 248
 
2.5%

Length

2024-05-11T02:38:16.854471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T02:38:17.259802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 2000
20.0%
30대 1772
17.7%
40대 1505
15.0%
기타 1449
14.5%
50대 1292
12.9%
10대 1046
10.5%
60대 688
 
6.9%
70대이상 248
 
2.5%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.615
Minimum1
Maximum707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:38:17.905447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q316
95-th percentile62
Maximum707
Range706
Interquartile range (IQR)14

Descriptive statistics

Standard deviation26.173664
Coefficient of variation (CV)1.7908768
Kurtosis68.178438
Mean14.615
Median Absolute Deviation (MAD)4
Skewness5.4148581
Sum146150
Variance685.06068
MonotonicityNot monotonic
2024-05-11T02:38:18.448277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1964
19.6%
2 1367
13.7%
3 861
 
8.6%
4 621
 
6.2%
5 503
 
5.0%
6 375
 
3.8%
7 295
 
2.9%
8 274
 
2.7%
9 247
 
2.5%
10 216
 
2.2%
Other values (174) 3277
32.8%
ValueCountFrequency (%)
1 1964
19.6%
2 1367
13.7%
3 861
8.6%
4 621
 
6.2%
5 503
 
5.0%
6 375
 
3.8%
7 295
 
2.9%
8 274
 
2.7%
9 247
 
2.5%
10 216
 
2.2%
ValueCountFrequency (%)
707 1
< 0.1%
340 1
< 0.1%
298 1
< 0.1%
282 1
< 0.1%
256 1
< 0.1%
251 1
< 0.1%
250 1
< 0.1%
249 1
< 0.1%
235 1
< 0.1%
229 1
< 0.1%

운동량
Text

Distinct8856
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:38:19.496550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.8371
Min length2

Characters and Unicode

Total characters58371
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

Unique8307 ?
Unique (%)83.1%

Sample

1st row38.26
2nd row47.68
3rd row219.23
4th row2569.18
5th row4573.64
ValueCountFrequency (%)
0.00 497
 
5.0%
n 17
 
0.2%
27.80 6
 
0.1%
42.47 5
 
< 0.1%
21.36 5
 
< 0.1%
74.94 5
 
< 0.1%
18.53 4
 
< 0.1%
25.23 4
 
< 0.1%
49.42 4
 
< 0.1%
10.81 4
 
< 0.1%
Other values (8846) 9449
94.5%
2024-05-11T02:38:20.764650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9983
17.1%
1 6669
11.4%
2 5549
9.5%
0 5415
9.3%
3 4880
8.4%
4 4716
8.1%
5 4410
7.6%
6 4364
7.5%
7 4249
7.3%
8 4126
7.1%
Other values (3) 4010
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48354
82.8%
Other Punctuation 10000
 
17.1%
Uppercase Letter 17
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6669
13.8%
2 5549
11.5%
0 5415
11.2%
3 4880
10.1%
4 4716
9.8%
5 4410
9.1%
6 4364
9.0%
7 4249
8.8%
8 4126
8.5%
9 3976
8.2%
Other Punctuation
ValueCountFrequency (%)
. 9983
99.8%
\ 17
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58354
> 99.9%
Latin 17
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9983
17.1%
1 6669
11.4%
2 5549
9.5%
0 5415
9.3%
3 4880
8.4%
4 4716
8.1%
5 4410
7.6%
6 4364
7.5%
7 4249
7.3%
8 4126
7.1%
Other values (2) 3993
 
6.8%
Latin
ValueCountFrequency (%)
N 17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9983
17.1%
1 6669
11.4%
2 5549
9.5%
0 5415
9.3%
3 4880
8.4%
4 4716
8.1%
5 4410
7.6%
6 4364
7.5%
7 4249
7.3%
8 4126
7.1%
Other values (3) 4010
6.9%

탄소량
Text

Distinct2201
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T02:38:21.644726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1687
Min length2

Characters and Unicode

Total characters41687
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

Unique990 ?
Unique (%)9.9%

Sample

1st row0.30
2nd row0.40
3rd row1.99
4th row26.14
5th row33.84
ValueCountFrequency (%)
0.00 502
 
5.0%
0.25 48
 
0.5%
0.37 47
 
0.5%
0.35 43
 
0.4%
0.46 42
 
0.4%
0.23 42
 
0.4%
0.36 41
 
0.4%
0.21 41
 
0.4%
0.26 40
 
0.4%
0.27 38
 
0.4%
Other values (2191) 9116
91.2%
2024-05-11T02:38:22.970369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9983
23.9%
0 6200
14.9%
1 4838
11.6%
2 3587
 
8.6%
3 3015
 
7.2%
4 2695
 
6.5%
5 2533
 
6.1%
6 2390
 
5.7%
7 2238
 
5.4%
8 2148
 
5.2%
Other values (3) 2060
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31670
76.0%
Other Punctuation 10000
 
24.0%
Uppercase Letter 17
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6200
19.6%
1 4838
15.3%
2 3587
11.3%
3 3015
9.5%
4 2695
8.5%
5 2533
8.0%
6 2390
 
7.5%
7 2238
 
7.1%
8 2148
 
6.8%
9 2026
 
6.4%
Other Punctuation
ValueCountFrequency (%)
. 9983
99.8%
\ 17
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41670
> 99.9%
Latin 17
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9983
24.0%
0 6200
14.9%
1 4838
11.6%
2 3587
 
8.6%
3 3015
 
7.2%
4 2695
 
6.5%
5 2533
 
6.1%
6 2390
 
5.7%
7 2238
 
5.4%
8 2148
 
5.2%
Other values (2) 2043
 
4.9%
Latin
ValueCountFrequency (%)
N 17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9983
23.9%
0 6200
14.9%
1 4838
11.6%
2 3587
 
8.6%
3 3015
 
7.2%
4 2695
 
6.5%
5 2533
 
6.1%
6 2390
 
5.7%
7 2238
 
5.4%
8 2148
 
5.2%
Other values (3) 2060
 
4.9%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8461
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25588.658
Minimum0
Maximum728506.59
Zeros514
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:38:23.447016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13030.0525
median9894.455
Q328386.7
95-th percentile103695.15
Maximum728506.59
Range728506.59
Interquartile range (IQR)25356.647

Descriptive statistics

Standard deviation44957.817
Coefficient of variation (CV)1.7569431
Kurtosis43.643406
Mean25588.658
Median Absolute Deviation (MAD)8353.805
Skewness5.0714089
Sum2.5588658 × 108
Variance2.0212053 × 109
MonotonicityNot monotonic
2024-05-11T02:38:23.938428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 514
 
5.1%
590.0 10
 
0.1%
1110.0 9
 
0.1%
830.0 9
 
0.1%
1080.0 8
 
0.1%
1090.0 8
 
0.1%
2520.0 8
 
0.1%
1230.0 8
 
0.1%
1170.0 8
 
0.1%
2000.0 7
 
0.1%
Other values (8451) 9411
94.1%
ValueCountFrequency (%)
0.0 514
5.1%
0.66 1
 
< 0.1%
10.0 2
 
< 0.1%
20.0 2
 
< 0.1%
30.0 1
 
< 0.1%
50.0 1
 
< 0.1%
60.0 2
 
< 0.1%
70.0 2
 
< 0.1%
90.0 2
 
< 0.1%
110.0 1
 
< 0.1%
ValueCountFrequency (%)
728506.59 1
< 0.1%
722540.05 1
< 0.1%
701650.23 1
< 0.1%
594372.46 1
< 0.1%
590414.84 1
< 0.1%
590196.41 1
< 0.1%
545123.7 1
< 0.1%
532512.02 1
< 0.1%
512019.46 1
< 0.1%
493811.59 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct1437
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.6913
Minimum0
Maximum7208
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T02:38:24.454149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q140
median113
Q3318
95-th percentile1181.1
Maximum7208
Range7208
Interquartile range (IQR)278

Descriptive statistics

Standard deviation493.75871
Coefficient of variation (CV)1.7044306
Kurtosis33.395458
Mean289.6913
Median Absolute Deviation (MAD)92
Skewness4.5328522
Sum2896913
Variance243797.67
MonotonicityNot monotonic
2024-05-11T02:38:24.952203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 100
 
1.0%
7 97
 
1.0%
10 93
 
0.9%
16 92
 
0.9%
11 85
 
0.9%
6 83
 
0.8%
5 82
 
0.8%
12 81
 
0.8%
13 78
 
0.8%
8 74
 
0.7%
Other values (1427) 9135
91.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12
 
0.1%
2 46
0.5%
3 56
0.6%
4 70
0.7%
5 82
0.8%
6 83
0.8%
7 97
1.0%
8 74
0.7%
9 100
1.0%
ValueCountFrequency (%)
7208 1
< 0.1%
7041 1
< 0.1%
6910 1
< 0.1%
6891 1
< 0.1%
6476 1
< 0.1%
5482 1
< 0.1%
5160 1
< 0.1%
5138 1
< 0.1%
5129 1
< 0.1%
5046 1
< 0.1%

Interactions

2024-05-11T02:38:07.083299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:03.183296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:04.366453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:05.635016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:07.435349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:03.434919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:04.658540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:05.923067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:07.888069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:03.731658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:04.994442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:06.302522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:08.389487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:04.054614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:05.325974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T02:38:06.699566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T02:38:25.300941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0270.0000.0280.0510.0630.089
대여구분코드0.0271.0000.1510.3370.1210.2490.280
성별0.0000.1511.0000.0750.0450.0610.035
연령대코드0.0280.3370.0751.0000.1330.1380.148
이용건수0.0510.1210.0450.1331.0000.7420.782
이동거리(M)0.0630.2490.0610.1380.7421.0000.929
이용시간(분)0.0890.2800.0350.1480.7820.9291.000
2024-05-11T02:38:25.627584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.1230.214
성별0.1231.0000.034
연령대코드0.2140.0341.000
2024-05-11T02:38:25.891387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.063-0.061-0.0740.0110.0000.013
이용건수-0.0631.0000.8700.8860.0820.0290.074
이동거리(M)-0.0610.8701.0000.9280.1060.0370.066
이용시간(분)-0.0740.8860.9281.0000.1200.0210.071
대여구분코드0.0110.0820.1060.1201.0000.1230.214
성별0.0000.0290.0370.0210.1231.0000.034
연령대코드0.0130.0740.0660.0710.2140.0341.000

Missing values

2024-05-11T02:38:09.039459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T02:38:09.742058image/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)
146872022-01536536. 행당역 2번출구 앞정기M70대이상338.260.301288.1527
760162022-0135483548.뚝섬역 6번출구정기<NA>30대147.680.401720.012
727352022-0131313131.연가교 교차로일일(회원)\N10대5219.231.998564.29134
398192022-0113801380.월곡역 5번출구 앞정기F20대502569.1826.14112639.0957
236362022-01826826. 서울역 서부교차로2정기M30대874573.6433.84145893.631483
351792022-0112211221. 삼전역 4번출구정기\N20대823383.7329.14125324.331470
120132022-01451451. 청와대앞길정기M30대10465.943.6115594.65173
491332022-0117511751. 창원초등학교 교차로일일(회원)\N40대355.310.682910.0121
748562022-0135093509. 세종사이버대학교정기F20대1743641.5736.95159289.091698
667412022-0126202620. 송파나루역 4번 출구옆일일(회원)F기타20741.697.9834374.38446
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
156582022-01565565. 옥수역 3번출구단체F20대372.070.843640.091
337892022-0111851185. 등촌9단지정기<NA>40대1190.601.406016.4522
903632022-0145634563. 여의도 은하아파트정기M20대292026.7516.4570907.19627
3642022-01111111. 상수역 2번출구 앞일일(회원)F30대245.030.461954.6768
507382022-0118431843. 독산고등학교일일(회원)M30대6508.464.5519609.63154
519992022-0119201920. 서울미래초등학교 사거리일일(회원)M10대236.520.331418.6986
173202022-01616616. 서울시립대 앞정기F30대6181.511.908188.35189
899192022-0145354535. 목동12단지 아파트일일(회원)M기타4140.261.004307.9137
420562022-0114551455. 상봉역 2번 출구정기M20대943335.6423.78102514.81048
309122022-0111021102. 방화사거리 마을버스 버스정류장일일(회원)M20대24828.546.9930035.58329