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-15246/F/1/datasetView.do

Alerts

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 (64.6%)Imbalance
대여소번호 is highly skewed (γ1 = 40.53992539)Skewed
이동거리(M) has 429 (4.3%) zerosZeros

Reproduction

Analysis started2024-03-13 16:26:06.683347
Analysis finished2024-03-13 16:26:09.456422
Duration2.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-02-01
7608 
2021-02-02
2392 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-02-01
2nd row2021-02-01
3rd row2021-02-01
4th row2021-02-01
5th row2021-02-01

Common Values

ValueCountFrequency (%)
2021-02-01 7608
76.1%
2021-02-02 2392
 
23.9%

Length

2024-03-14T01:26:09.505809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:09.577319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-02-01 7608
76.1%
2021-02-02 2392
 
23.9%

대여소번호
Real number (ℝ)

SKEWED 

Distinct1923
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1204.0062
Minimum3
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:26:09.666334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile170.95
Q1466
median913
Q31804.25
95-th percentile3010
Maximum99999
Range99996
Interquartile range (IQR)1338.25

Descriptive statistics

Standard deviation1337.8793
Coefficient of variation (CV)1.1111897
Kurtosis2973.1934
Mean1204.0062
Median Absolute Deviation (MAD)566
Skewness40.539925
Sum12040062
Variance1789920.9
MonotonicityNot monotonic
2024-03-14T01:26:09.788911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 21
 
0.2%
729 20
 
0.2%
703 20
 
0.2%
152 19
 
0.2%
587 19
 
0.2%
502 19
 
0.2%
791 18
 
0.2%
641 18
 
0.2%
232 18
 
0.2%
385 18
 
0.2%
Other values (1913) 9810
98.1%
ValueCountFrequency (%)
3 2
 
< 0.1%
101 6
0.1%
102 12
0.1%
103 10
0.1%
104 7
0.1%
105 9
0.1%
106 14
0.1%
107 5
 
0.1%
108 10
0.1%
109 5
 
0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
3588 1
 
< 0.1%
3587 1
 
< 0.1%
3586 3
 
< 0.1%
3582 4
 
< 0.1%
3581 2
 
< 0.1%
3579 5
0.1%
3578 4
 
< 0.1%
3575 10
0.1%
3573 2
 
< 0.1%

대여소
Text

Distinct1923
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:26:10.023972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length15.1899
Min length4

Characters and Unicode

Total characters151899
Distinct characters541
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

Unique233 ?
Unique (%)2.3%

Sample

1st row2108. 은천치안센터
2nd row1354. 고려대학교 2번출구
3rd row125. 서강대 남문 옆
4th row487.신석초교앞 교차로 교통섬
5th row1856. 모두의학교
ValueCountFrequency (%)
앞 2810
 
9.5%
옆 518
 
1.7%
출구 408
 
1.4%
1번출구 387
 
1.3%
사거리 297
 
1.0%
2번출구 251
 
0.8%
4번출구 235
 
0.8%
3번출구 233
 
0.8%
입구 217
 
0.7%
뒤 215
 
0.7%
Other values (3801) 24092
81.2%
2024-03-14T01:26:10.354391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19848
 
13.1%
. 10023
 
6.6%
1 7814
 
5.1%
2 6141
 
4.0%
3 4396
 
2.9%
구 3611
 
2.4%
5 3582
 
2.4%
4 3486
 
2.3%
앞 3312
 
2.2%
6 3175
 
2.1%
Other values (531) 86511
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78860
51.9%
Decimal Number 39727
26.2%
Space Separator 19848
 
13.1%
Other Punctuation 10102
 
6.7%
Uppercase Letter 1485
 
1.0%
Open Punctuation 860
 
0.6%
Close Punctuation 860
 
0.6%
Dash Punctuation 77
 
0.1%
Lowercase Letter 65
 
< 0.1%
Math Symbol 7
 
< 0.1%
Other values (2) 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3611
 
4.6%
앞 3312
 
4.2%
역 2865
 
3.6%
번 2595
 
3.3%
출 2553
 
3.2%
동 2034
 
2.6%
교 1596
 
2.0%
리 1382
 
1.8%
아 1148
 
1.5%
파 1146
 
1.5%
Other values (473) 56618
71.8%
Uppercase Letter
ValueCountFrequency (%)
S 167
11.2%
C 153
10.3%
K 151
10.2%
T 116
 
7.8%
G 109
 
7.3%
L 103
 
6.9%
M 95
 
6.4%
D 91
 
6.1%
A 90
 
6.1%
B 76
 
5.1%
Other values (14) 334
22.5%
Lowercase Letter
ValueCountFrequency (%)
e 26
40.0%
k 9
 
13.8%
s 6
 
9.2%
n 6
 
9.2%
t 4
 
6.2%
l 4
 
6.2%
v 4
 
6.2%
y 3
 
4.6%
m 1
 
1.5%
o 1
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 7814
19.7%
2 6141
15.5%
3 4396
11.1%
5 3582
9.0%
4 3486
8.8%
6 3175
8.0%
7 3153
7.9%
0 3080
 
7.8%
9 2460
 
6.2%
8 2440
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 10023
99.2%
, 65
 
0.6%
? 6
 
0.1%
& 5
 
< 0.1%
· 3
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 4
57.1%
~ 3
42.9%
Space Separator
ValueCountFrequency (%)
19848
100.0%
Open Punctuation
ValueCountFrequency (%)
( 860
100.0%
Close Punctuation
ValueCountFrequency (%)
) 860
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78863
51.9%
Common 71486
47.1%
Latin 1550
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3611
 
4.6%
앞 3312
 
4.2%
역 2865
 
3.6%
번 2595
 
3.3%
출 2553
 
3.2%
동 2034
 
2.6%
교 1596
 
2.0%
리 1382
 
1.8%
아 1148
 
1.5%
파 1146
 
1.5%
Other values (474) 56621
71.8%
Latin
ValueCountFrequency (%)
S 167
10.8%
C 153
 
9.9%
K 151
 
9.7%
T 116
 
7.5%
G 109
 
7.0%
L 103
 
6.6%
M 95
 
6.1%
D 91
 
5.9%
A 90
 
5.8%
B 76
 
4.9%
Other values (25) 399
25.7%
Common
ValueCountFrequency (%)
19848
27.8%
. 10023
14.0%
1 7814
 
10.9%
2 6141
 
8.6%
3 4396
 
6.1%
5 3582
 
5.0%
4 3486
 
4.9%
6 3175
 
4.4%
7 3153
 
4.4%
0 3080
 
4.3%
Other values (12) 6788
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78860
51.9%
ASCII 73033
48.1%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19848
27.2%
. 10023
13.7%
1 7814
 
10.7%
2 6141
 
8.4%
3 4396
 
6.0%
5 3582
 
4.9%
4 3486
 
4.8%
6 3175
 
4.3%
7 3153
 
4.3%
0 3080
 
4.2%
Other values (46) 8335
11.4%
Hangul
ValueCountFrequency (%)
구 3611
 
4.6%
앞 3312
 
4.2%
역 2865
 
3.6%
번 2595
 
3.3%
출 2553
 
3.2%
동 2034
 
2.6%
교 1596
 
2.0%
리 1382
 
1.8%
아 1148
 
1.5%
파 1146
 
1.5%
Other values (473) 56618
71.8%
None
ValueCountFrequency (%)
· 3
50.0%
㈜ 3
50.0%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7868 
일일(회원)
2027 
일일(비회원)
 
52
단체
 
38
BIL_021
 
15

Length

Max length7
Median length2
Mean length2.8443
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 7868
78.7%
일일(회원) 2027
 
20.3%
일일(비회원) 52
 
0.5%
단체 38
 
0.4%
BIL_021 15
 
0.1%

Length

2024-03-14T01:26:10.456369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:10.533728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7868
78.7%
일일(회원 2027
 
20.3%
일일(비회원 52
 
0.5%
단체 38
 
0.4%
bil_021 15
 
0.1%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3657 
M
3369 
F
2234 
<NA>
735 
m
 
4

Length

Max length4
Median length1
Mean length1.5862
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3657
36.6%
M 3369
33.7%
F 2234
22.3%
<NA> 735
 
7.3%
m 4
 
< 0.1%
f 1
 
< 0.1%

Length

2024-03-14T01:26:10.622257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:10.716677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3657
36.6%
m 3373
33.7%
f 2235
22.4%
na 735
 
7.3%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
3262 
AGE_003
2326 
AGE_004
1764 
AGE_005
1280 
AGE_001
551 
Other values (3)
817 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_003
2nd rowAGE_006
3rd rowAGE_003
4th rowAGE_003
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 3262
32.6%
AGE_003 2326
23.3%
AGE_004 1764
17.6%
AGE_005 1280
 
12.8%
AGE_001 551
 
5.5%
AGE_006 503
 
5.0%
AGE_008 228
 
2.3%
AGE_007 86
 
0.9%

Length

2024-03-14T01:26:10.823981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:26:10.928233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 3262
32.6%
age_003 2326
23.3%
age_004 1764
17.6%
age_005 1280
 
12.8%
age_001 551
 
5.5%
age_006 503
 
5.0%
age_008 228
 
2.3%
age_007 86
 
0.9%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8772
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:26:11.048467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5072317
Coefficient of variation (CV)0.80291481
Kurtosis11.319963
Mean1.8772
Median Absolute Deviation (MAD)0
Skewness2.7980769
Sum18772
Variance2.2717473
MonotonicityNot monotonic
2024-03-14T01:26:11.154422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 5862
58.6%
2 2097
 
21.0%
3 952
 
9.5%
4 466
 
4.7%
5 253
 
2.5%
6 149
 
1.5%
7 94
 
0.9%
8 60
 
0.6%
9 28
 
0.3%
10 17
 
0.2%
Other values (7) 22
 
0.2%
ValueCountFrequency (%)
1 5862
58.6%
2 2097
 
21.0%
3 952
 
9.5%
4 466
 
4.7%
5 253
 
2.5%
6 149
 
1.5%
7 94
 
0.9%
8 60
 
0.6%
9 28
 
0.3%
10 17
 
0.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 2
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 4
 
< 0.1%
11 10
 
0.1%
10 17
 
0.2%
9 28
0.3%
8 60
0.6%

운동량
Text

Distinct7967
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:26:11.440084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.1255
Min length1

Characters and Unicode

Total characters51255
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

Unique6641 ?
Unique (%)66.4%

Sample

1st row48.27
2nd row46.9
3rd row0
4th row139.82
5th row31.51
ValueCountFrequency (%)
0 384
 
3.8%
n 47
 
0.5%
2.27 6
 
0.1%
16.48 5
 
< 0.1%
22.64 5
 
< 0.1%
51.48 5
 
< 0.1%
51.22 5
 
< 0.1%
3.65 5
 
< 0.1%
9.08 5
 
< 0.1%
9.02 5
 
< 0.1%
Other values (7957) 9528
95.3%
2024-03-14T01:26:11.835933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9485
18.5%
1 6318
12.3%
2 5208
10.2%
3 4491
8.8%
4 4190
8.2%
5 3968
7.7%
6 3876
7.6%
7 3729
 
7.3%
9 3551
 
6.9%
8 3547
 
6.9%
Other values (3) 2892
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41676
81.3%
Other Punctuation 9532
 
18.6%
Uppercase Letter 47
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6318
15.2%
2 5208
12.5%
3 4491
10.8%
4 4190
10.1%
5 3968
9.5%
6 3876
9.3%
7 3729
8.9%
9 3551
8.5%
8 3547
8.5%
0 2798
6.7%
Other Punctuation
ValueCountFrequency (%)
. 9485
99.5%
\ 47
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51208
99.9%
Latin 47
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9485
18.5%
1 6318
12.3%
2 5208
10.2%
3 4491
8.8%
4 4190
8.2%
5 3968
7.7%
6 3876
7.6%
7 3729
 
7.3%
9 3551
 
6.9%
8 3547
 
6.9%
Other values (2) 2845
 
5.6%
Latin
ValueCountFrequency (%)
N 47
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9485
18.5%
1 6318
12.3%
2 5208
10.2%
3 4491
8.8%
4 4190
8.2%
5 3968
7.7%
6 3876
7.6%
7 3729
 
7.3%
9 3551
 
6.9%
8 3547
 
6.9%
Other values (3) 2892
 
5.6%

탄소량
Text

Distinct661
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:26:12.174235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.771
Min length1

Characters and Unicode

Total characters37710
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

Unique167 ?
Unique (%)1.7%

Sample

1st row0.44
2nd row0.54
3rd row0
4th row1.02
5th row0.19
ValueCountFrequency (%)
0 387
 
3.9%
0.18 116
 
1.2%
0.27 109
 
1.1%
0.24 106
 
1.1%
0.29 98
 
1.0%
0.21 96
 
1.0%
0.3 95
 
0.9%
0.39 95
 
0.9%
0.16 95
 
0.9%
0.26 92
 
0.9%
Other values (651) 8711
87.1%
2024-03-14T01:26:12.601944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9499
25.2%
0 6990
18.5%
1 4329
11.5%
2 3147
 
8.3%
3 2649
 
7.0%
4 2282
 
6.1%
5 1977
 
5.2%
6 1845
 
4.9%
7 1665
 
4.4%
8 1647
 
4.4%
Other values (3) 1680
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28117
74.6%
Other Punctuation 9546
 
25.3%
Uppercase Letter 47
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6990
24.9%
1 4329
15.4%
2 3147
11.2%
3 2649
 
9.4%
4 2282
 
8.1%
5 1977
 
7.0%
6 1845
 
6.6%
7 1665
 
5.9%
8 1647
 
5.9%
9 1586
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 9499
99.5%
\ 47
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37663
99.9%
Latin 47
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9499
25.2%
0 6990
18.6%
1 4329
11.5%
2 3147
 
8.4%
3 2649
 
7.0%
4 2282
 
6.1%
5 1977
 
5.2%
6 1845
 
4.9%
7 1665
 
4.4%
8 1647
 
4.4%
Other values (2) 1633
 
4.3%
Latin
ValueCountFrequency (%)
N 47
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9499
25.2%
0 6990
18.5%
1 4329
11.5%
2 3147
 
8.3%
3 2649
 
7.0%
4 2282
 
6.1%
5 1977
 
5.2%
6 1845
 
4.9%
7 1665
 
4.4%
8 1647
 
4.4%
Other values (3) 1680
 
4.5%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9314
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5012.6203
Minimum0
Maximum116398.09
Zeros429
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:26:12.720543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile222.39
Q11312.74
median2946.485
Q36333.65
95-th percentile16597.457
Maximum116398.09
Range116398.09
Interquartile range (IQR)5020.91

Descriptive statistics

Standard deviation6189.0672
Coefficient of variation (CV)1.234697
Kurtosis32.039669
Mean5012.6203
Median Absolute Deviation (MAD)1990.74
Skewness3.888888
Sum50126203
Variance38304553
MonotonicityNot monotonic
2024-03-14T01:26:12.825661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 429
 
4.3%
444.78 10
 
0.1%
222.39 10
 
0.1%
111.2 9
 
0.1%
1000.0 5
 
0.1%
2210.0 5
 
0.1%
333.59 5
 
0.1%
780.0 4
 
< 0.1%
1410.0 4
 
< 0.1%
1190.0 4
 
< 0.1%
Other values (9304) 9515
95.2%
ValueCountFrequency (%)
0.0 429
4.3%
0.1 2
 
< 0.1%
0.2 2
 
< 0.1%
10.74 1
 
< 0.1%
40.0 1
 
< 0.1%
88.08 1
 
< 0.1%
88.12 1
 
< 0.1%
88.14 1
 
< 0.1%
88.15 2
 
< 0.1%
88.16 3
 
< 0.1%
ValueCountFrequency (%)
116398.09 1
< 0.1%
94461.62 1
< 0.1%
92170.24 1
< 0.1%
80809.5 1
< 0.1%
69251.24 1
< 0.1%
64902.41 1
< 0.1%
62363.47 1
< 0.1%
60357.96 1
< 0.1%
57412.8 1
< 0.1%
55403.23 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct308
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.714
Minimum0
Maximum818
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:26:12.933016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q111
median26
Q357
95-th percentile135
Maximum818
Range818
Interquartile range (IQR)46

Descriptive statistics

Standard deviation50.990793
Coefficient of variation (CV)1.1664637
Kurtosis20.910842
Mean43.714
Median Absolute Deviation (MAD)18
Skewness3.3231126
Sum437140
Variance2600.061
MonotonicityNot monotonic
2024-03-14T01:26:13.044699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 308
 
3.1%
5 293
 
2.9%
7 282
 
2.8%
8 265
 
2.6%
10 261
 
2.6%
4 255
 
2.5%
9 251
 
2.5%
12 246
 
2.5%
11 244
 
2.4%
13 237
 
2.4%
Other values (298) 7358
73.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 39
 
0.4%
2 124
1.2%
3 191
1.9%
4 255
2.5%
5 293
2.9%
6 308
3.1%
7 282
2.8%
8 265
2.6%
9 251
2.5%
ValueCountFrequency (%)
818 1
< 0.1%
608 1
< 0.1%
586 1
< 0.1%
582 1
< 0.1%
529 2
< 0.1%
507 1
< 0.1%
499 1
< 0.1%
491 1
< 0.1%
489 1
< 0.1%
462 1
< 0.1%

Interactions

2024-03-14T01:26:08.849968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:07.875011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.191394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.496997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.967645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:07.959441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.262685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.573740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:09.065180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.033148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.336765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.654063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:09.147787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.110392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.413694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:26:08.731520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:26:13.412422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.0000.0640.0090.0420.0000.0680.045
대여소번호0.0001.0000.0000.0000.0000.0000.0000.000
대여구분코드0.0640.0001.0000.1390.4230.1730.0570.047
성별0.0090.0000.1391.0000.1700.1390.0000.000
연령대코드0.0420.0000.4230.1701.0000.1530.0370.048
이용건수0.0000.0000.1730.1390.1531.0000.7740.546
이동거리(M)0.0680.0000.0570.0000.0370.7741.0000.752
이용시간(분)0.0450.0000.0470.0000.0480.5460.7521.000
2024-03-14T01:26:13.502111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자성별대여구분코드연령대코드
대여일자1.0000.0110.0790.032
성별0.0111.0000.0520.105
대여구분코드0.0790.0521.0000.276
연령대코드0.0320.1050.2761.000
2024-03-14T01:26:13.580024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여일자대여구분코드성별연령대코드
대여소번호1.000-0.0510.007-0.0170.0000.0000.0000.000
이용건수-0.0511.0000.5680.5830.0000.0730.0580.073
이동거리(M)0.0070.5681.0000.8380.0520.0240.0000.018
이용시간(분)-0.0170.5830.8381.0000.0450.0270.0000.023
대여일자0.0000.0000.0520.0451.0000.0790.0110.032
대여구분코드0.0000.0730.0240.0270.0791.0000.0520.276
성별0.0000.0580.0000.0000.0110.0521.0000.105
연령대코드0.0000.0730.0180.0230.0320.2760.1051.000

Missing values

2024-03-14T01:26:09.264206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:26:09.394249image/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)
158992021-02-0121082108. 은천치안센터정기\NAGE_003148.270.441904.6117
111982021-02-0113541354. 고려대학교 2번출구정기FAGE_006146.90.542322.4725
2832021-02-01125125. 서강대 남문 옆정기\NAGE_0031000.03
39072021-02-01487487.신석초교앞 교차로 교통섬정기MAGE_0031139.821.024413.6317
144452021-02-0118561856. 모두의학교일일(회원)<NA>AGE_002131.510.19837.665
207622021-02-0135323532. 왕십리KCC스위첸아파트정기FAGE_0021000.03
136542021-02-0117161716. 하나로마트 창동점정기\NAGE_005264.260.582514.1514
124802021-02-0115381538. 솔밭공원역정기FAGE_0022141.581.325680.4584
54762021-02-01648648. 장안동위더스빌옆정기MAGE_001147.960.371614.8420
15202021-02-01239239. 유스호스텔 앞정기MAGE_0022769.684.8220772.14110
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
158642021-02-0121062106. 난곡 새마을금고정기\NAGE_0034276.922.4410515.0663
182802021-02-0126122612. 문정·가락 대여소 앞정기\NAGE_003160.360.572458.3215
102622021-02-0112321232. 롯데마트 주차장 옆일일(회원)FAGE_004130.90.31300.3527
253532021-02-02604604. 답십리초등학교 옆 공원정기\NAGE_001140.190.361561.4912
59052021-02-01711711. 신일해피트리아파트 앞일일(회원)\NAGE_0021118.011.235321.4853
53342021-02-01635635. 시조사 앞 (청량고정문 옆)정기\NAGE_0061106.660.863689.6628
69992021-02-01819819. 선린인터넷 고등학교정기MAGE_0051000.066
180282021-02-0125152515.서초초등학교 후문정기<NA>AGE_0021122.971.114777.5455
163222021-02-0121832183. 동방1교정기MAGE_0061121.010.793395.2414
278502021-02-0210101010. 강동세무서일일(회원)\NAGE_002160.80.572476.5820