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

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 (54.7%)Imbalance
이동거리(M) has 432 (4.3%) zerosZeros

Reproduction

Analysis started2024-03-13 16:25:50.885173
Analysis finished2024-03-13 16:25:53.526579
Duration2.64 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
2021-04-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-04-01
2nd row2021-04-01
3rd row2021-04-01
4th row2021-04-01
5th row2021-04-01

Common Values

ValueCountFrequency (%)
2021-04-01 10000
100.0%

Length

2024-03-14T01:25:53.576781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:53.649473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 10000
100.0%

대여소번호
Real number (ℝ)

Distinct812
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean582.5805
Minimum10
Maximum1130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:53.729745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile144
Q1302
median569
Q3825
95-th percentile1074
Maximum1130
Range1120
Interquartile range (IQR)523

Descriptive statistics

Standard deviation301.58803
Coefficient of variation (CV)0.51767616
Kurtosis-1.1922867
Mean582.5805
Median Absolute Deviation (MAD)261
Skewness0.14338983
Sum5825805
Variance90955.342
MonotonicityNot monotonic
2024-03-14T01:25:53.851220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
583 33
 
0.3%
272 31
 
0.3%
502 30
 
0.3%
207 29
 
0.3%
186 27
 
0.3%
152 27
 
0.3%
780 27
 
0.3%
133 26
 
0.3%
913 26
 
0.3%
202 25
 
0.2%
Other values (802) 9719
97.2%
ValueCountFrequency (%)
10 1
 
< 0.1%
102 17
0.2%
103 13
0.1%
104 11
0.1%
105 13
0.1%
106 24
0.2%
107 11
0.1%
108 15
0.1%
109 18
0.2%
111 11
0.1%
ValueCountFrequency (%)
1130 7
 
0.1%
1129 14
0.1%
1128 17
0.2%
1127 10
0.1%
1126 17
0.2%
1125 20
0.2%
1124 18
0.2%
1122 18
0.2%
1121 13
0.1%
1120 18
0.2%

대여소
Text

Distinct812
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:54.052835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length14.8079
Min length3

Characters and Unicode

Total characters148079
Distinct characters428
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

Unique3 ?
Unique (%)< 0.1%

Sample

1st row284. 센트럴 푸르지오 시티 앞
2nd row950. 구산역 2번 출구
3rd row1128. 화곡역 6번출구
4th row514. 성수사거리 버스정류장 앞
5th row203. 국회의사당역 3번출구 옆
ValueCountFrequency (%)
앞 3035
 
10.0%
옆 591
 
1.9%
1번출구 355
 
1.2%
출구 349
 
1.1%
사거리 336
 
1.1%
2번출구 306
 
1.0%
4번출구 282
 
0.9%
3번출구 229
 
0.8%
뒤 225
 
0.7%
입구 223
 
0.7%
Other values (1681) 24433
80.5%
2024-03-14T01:25:54.371236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20540
 
13.9%
. 9999
 
6.8%
1 6191
 
4.2%
2 4435
 
3.0%
앞 3660
 
2.5%
3 3536
 
2.4%
4 3502
 
2.4%
0 3447
 
2.3%
구 3429
 
2.3%
5 3383
 
2.3%
Other values (418) 85957
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78014
52.7%
Decimal Number 35931
24.3%
Space Separator 20540
 
13.9%
Other Punctuation 10036
 
6.8%
Uppercase Letter 1814
 
1.2%
Open Punctuation 824
 
0.6%
Close Punctuation 824
 
0.6%
Dash Punctuation 35
 
< 0.1%
Lowercase Letter 34
 
< 0.1%
Math Symbol 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3660
 
4.7%
구 3429
 
4.4%
역 2808
 
3.6%
번 2543
 
3.3%
출 2536
 
3.3%
동 2124
 
2.7%
교 1575
 
2.0%
리 1494
 
1.9%
아 1278
 
1.6%
원 1238
 
1.6%
Other values (379) 55329
70.9%
Uppercase Letter
ValueCountFrequency (%)
S 242
13.3%
K 209
11.5%
C 199
11.0%
B 135
 
7.4%
G 119
 
6.6%
I 114
 
6.3%
D 110
 
6.1%
M 108
 
6.0%
T 96
 
5.3%
L 94
 
5.2%
Other values (9) 388
21.4%
Decimal Number
ValueCountFrequency (%)
1 6191
17.2%
2 4435
12.3%
3 3536
9.8%
4 3502
9.7%
0 3447
9.6%
5 3383
9.4%
7 3272
9.1%
6 2962
8.2%
8 2673
7.4%
9 2530
7.0%
Other Punctuation
ValueCountFrequency (%)
. 9999
99.6%
, 37
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
k 17
50.0%
t 17
50.0%
Space Separator
ValueCountFrequency (%)
20540
100.0%
Open Punctuation
ValueCountFrequency (%)
( 824
100.0%
Close Punctuation
ValueCountFrequency (%)
) 824
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Math Symbol
ValueCountFrequency (%)
~ 19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78014
52.7%
Common 68217
46.1%
Latin 1848
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3660
 
4.7%
구 3429
 
4.4%
역 2808
 
3.6%
번 2543
 
3.3%
출 2536
 
3.3%
동 2124
 
2.7%
교 1575
 
2.0%
리 1494
 
1.9%
아 1278
 
1.6%
원 1238
 
1.6%
Other values (379) 55329
70.9%
Latin
ValueCountFrequency (%)
S 242
13.1%
K 209
11.3%
C 199
10.8%
B 135
 
7.3%
G 119
 
6.4%
I 114
 
6.2%
D 110
 
6.0%
M 108
 
5.8%
T 96
 
5.2%
L 94
 
5.1%
Other values (11) 422
22.8%
Common
ValueCountFrequency (%)
20540
30.1%
. 9999
14.7%
1 6191
 
9.1%
2 4435
 
6.5%
3 3536
 
5.2%
4 3502
 
5.1%
0 3447
 
5.1%
5 3383
 
5.0%
7 3272
 
4.8%
6 2962
 
4.3%
Other values (8) 6950
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78014
52.7%
ASCII 70065
47.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20540
29.3%
. 9999
14.3%
1 6191
 
8.8%
2 4435
 
6.3%
3 3536
 
5.0%
4 3502
 
5.0%
0 3447
 
4.9%
5 3383
 
4.8%
7 3272
 
4.7%
6 2962
 
4.2%
Other values (29) 8798
12.6%
Hangul
ValueCountFrequency (%)
앞 3660
 
4.7%
구 3429
 
4.4%
역 2808
 
3.6%
번 2543
 
3.3%
출 2536
 
3.3%
동 2124
 
2.7%
교 1575
 
2.0%
리 1494
 
1.9%
아 1278
 
1.6%
원 1238
 
1.6%
Other values (379) 55329
70.9%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
6768 
일일(회원)
3008 
일일(비회원)
 
125
단체
 
93
BIL_021
 
6

Length

Max length7
Median length2
Mean length3.2687
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 6768
67.7%
일일(회원) 3008
30.1%
일일(비회원) 125
 
1.2%
단체 93
 
0.9%
BIL_021 6
 
0.1%

Length

2024-03-14T01:25:54.485770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:54.567075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 6768
67.7%
일일(회원 3008
30.1%
일일(비회원 125
 
1.2%
단체 93
 
0.9%
bil_021 6
 
0.1%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3291 
M
3207 
F
2611 
<NA>
888 
m
 
2

Length

Max length4
Median length1
Mean length1.5955
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3291
32.9%
M 3207
32.1%
F 2611
26.1%
<NA> 888
 
8.9%
m 2
 
< 0.1%
f 1
 
< 0.1%

Length

2024-03-14T01:25:54.659388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:54.744081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3291
32.9%
m 3209
32.1%
f 2612
26.1%
na 888
 
8.9%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2814 
AGE_003
2108 
AGE_004
1664 
AGE_005
1270 
AGE_008
823 
Other values (3)
1321 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_002
2nd rowAGE_001
3rd rowAGE_005
4th rowAGE_002
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 2814
28.1%
AGE_003 2108
21.1%
AGE_004 1664
16.6%
AGE_005 1270
12.7%
AGE_008 823
 
8.2%
AGE_001 736
 
7.4%
AGE_006 500
 
5.0%
AGE_007 85
 
0.9%

Length

2024-03-14T01:25:54.832229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:25:54.914698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2814
28.1%
age_003 2108
21.1%
age_004 1664
16.6%
age_005 1270
12.7%
age_008 823
 
8.2%
age_001 736
 
7.4%
age_006 500
 
5.0%
age_007 85
 
0.9%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0427
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:55.013414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum136
Range135
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.822007
Coefficient of variation (CV)1.2561235
Kurtosis223.87104
Mean3.0427
Median Absolute Deviation (MAD)1
Skewness9.1559895
Sum30427
Variance14.607737
MonotonicityNot monotonic
2024-03-14T01:25:55.117268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 4339
43.4%
2 2040
20.4%
3 1066
 
10.7%
4 676
 
6.8%
5 488
 
4.9%
6 339
 
3.4%
7 250
 
2.5%
8 184
 
1.8%
9 131
 
1.3%
10 111
 
1.1%
Other values (28) 376
 
3.8%
ValueCountFrequency (%)
1 4339
43.4%
2 2040
20.4%
3 1066
 
10.7%
4 676
 
6.8%
5 488
 
4.9%
6 339
 
3.4%
7 250
 
2.5%
8 184
 
1.8%
9 131
 
1.3%
10 111
 
1.1%
ValueCountFrequency (%)
136 1
< 0.1%
110 1
< 0.1%
56 1
< 0.1%
52 1
< 0.1%
47 1
< 0.1%
42 1
< 0.1%
39 1
< 0.1%
34 2
< 0.1%
33 1
< 0.1%
31 2
< 0.1%

운동량
Text

Distinct8605
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:55.392761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.3248
Min length1

Characters and Unicode

Total characters53248
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

Unique7745 ?
Unique (%)77.5%

Sample

1st row232.6
2nd row3.14
3rd row54.67
4th row442.42
5th row356.45
ValueCountFrequency (%)
0 391
 
3.9%
n 42
 
0.4%
37.07 5
 
< 0.1%
92.96 4
 
< 0.1%
2.86 4
 
< 0.1%
58.43 4
 
< 0.1%
25.94 4
 
< 0.1%
6.16 4
 
< 0.1%
35.52 4
 
< 0.1%
28.41 4
 
< 0.1%
Other values (8595) 9534
95.3%
2024-03-14T01:25:55.819766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9467
17.8%
1 6338
11.9%
2 5312
10.0%
3 4792
9.0%
4 4565
8.6%
5 4107
7.7%
6 4064
7.6%
7 3998
7.5%
8 3783
 
7.1%
9 3701
 
7.0%
Other values (3) 3121
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43697
82.1%
Other Punctuation 9509
 
17.9%
Uppercase Letter 42
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6338
14.5%
2 5312
12.2%
3 4792
11.0%
4 4565
10.4%
5 4107
9.4%
6 4064
9.3%
7 3998
9.1%
8 3783
8.7%
9 3701
8.5%
0 3037
7.0%
Other Punctuation
ValueCountFrequency (%)
. 9467
99.6%
\ 42
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53206
99.9%
Latin 42
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9467
17.8%
1 6338
11.9%
2 5312
10.0%
3 4792
9.0%
4 4565
8.6%
5 4107
7.7%
6 4064
7.6%
7 3998
7.5%
8 3783
 
7.1%
9 3701
 
7.0%
Other values (2) 3079
 
5.8%
Latin
ValueCountFrequency (%)
N 42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9467
17.8%
1 6338
11.9%
2 5312
10.0%
3 4792
9.0%
4 4565
8.6%
5 4107
7.7%
6 4064
7.6%
7 3998
7.5%
8 3783
 
7.1%
9 3701
 
7.0%
Other values (3) 3121
 
5.9%

탄소량
Text

Distinct1161
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:25:56.156646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.7993
Min length1

Characters and Unicode

Total characters37993
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

Unique411 ?
Unique (%)4.1%

Sample

1st row2.33
2nd row0.31
3rd row0.55
4th row3.39
5th row3.19
ValueCountFrequency (%)
0 398
 
4.0%
0.49 70
 
0.7%
0.35 67
 
0.7%
0.34 65
 
0.7%
0.27 65
 
0.7%
0.21 62
 
0.6%
0.32 62
 
0.6%
0.28 61
 
0.6%
0.19 60
 
0.6%
0.4 59
 
0.6%
Other values (1151) 9031
90.3%
2024-03-14T01:25:56.570048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9497
25.0%
0 5301
14.0%
1 4408
11.6%
2 3348
 
8.8%
3 2819
 
7.4%
4 2493
 
6.6%
5 2261
 
6.0%
6 2079
 
5.5%
7 2031
 
5.3%
8 1900
 
5.0%
Other values (3) 1856
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28412
74.8%
Other Punctuation 9539
 
25.1%
Uppercase Letter 42
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5301
18.7%
1 4408
15.5%
2 3348
11.8%
3 2819
9.9%
4 2493
8.8%
5 2261
8.0%
6 2079
 
7.3%
7 2031
 
7.1%
8 1900
 
6.7%
9 1772
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 9497
99.6%
\ 42
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37951
99.9%
Latin 42
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9497
25.0%
0 5301
14.0%
1 4408
11.6%
2 3348
 
8.8%
3 2819
 
7.4%
4 2493
 
6.6%
5 2261
 
6.0%
6 2079
 
5.5%
7 2031
 
5.4%
8 1900
 
5.0%
Other values (2) 1814
 
4.8%
Latin
ValueCountFrequency (%)
N 42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37993
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9497
25.0%
0 5301
14.0%
1 4408
11.6%
2 3348
 
8.8%
3 2819
 
7.4%
4 2493
 
6.6%
5 2261
 
6.0%
6 2079
 
5.5%
7 2031
 
5.3%
8 1900
 
5.0%
Other values (3) 1856
 
4.9%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9070
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10117.957
Minimum0
Maximum768274.74
Zeros432
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:56.687982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile229.9975
Q12037.8925
median5109.895
Q312276.968
95-th percentile34923.641
Maximum768274.74
Range768274.74
Interquartile range (IQR)10239.075

Descriptive statistics

Standard deviation17769.734
Coefficient of variation (CV)1.756257
Kurtosis459.92923
Mean10117.957
Median Absolute Deviation (MAD)3811.18
Skewness14.234266
Sum1.0117957 × 108
Variance3.1576343 × 108
MonotonicityNot monotonic
2024-03-14T01:25:56.798534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 432
 
4.3%
111.2 7
 
0.1%
900.0 6
 
0.1%
890.0 6
 
0.1%
3130.0 6
 
0.1%
2290.0 6
 
0.1%
1050.0 6
 
0.1%
1560.0 5
 
0.1%
1520.0 5
 
0.1%
1500.0 5
 
0.1%
Other values (9060) 9516
95.2%
ValueCountFrequency (%)
0.0 432
4.3%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.29 1
 
< 0.1%
10.0 4
 
< 0.1%
20.0 1
 
< 0.1%
50.0 1
 
< 0.1%
60.0 1
 
< 0.1%
88.13 2
 
< 0.1%
88.15 1
 
< 0.1%
ValueCountFrequency (%)
768274.74 1
< 0.1%
573983.16 1
< 0.1%
312275.19 1
< 0.1%
262959.08 1
< 0.1%
233153.42 1
< 0.1%
217013.91 1
< 0.1%
206458.83 1
< 0.1%
198111.15 1
< 0.1%
179691.17 1
< 0.1%
168877.99 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct602
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.4541
Minimum0
Maximum7501
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:25:56.909825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q119
median49
Q3110
95-th percentile297
Maximum7501
Range7501
Interquartile range (IQR)91

Descriptive statistics

Standard deviation160.73225
Coefficient of variation (CV)1.7769482
Kurtosis630.52636
Mean90.4541
Median Absolute Deviation (MAD)36
Skewness17.297982
Sum904541
Variance25834.856
MonotonicityNot monotonic
2024-03-14T01:25:57.018028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 183
 
1.8%
7 167
 
1.7%
12 163
 
1.6%
9 160
 
1.6%
5 159
 
1.6%
4 151
 
1.5%
6 151
 
1.5%
10 151
 
1.5%
15 147
 
1.5%
19 142
 
1.4%
Other values (592) 8426
84.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 17
 
0.2%
2 74
0.7%
3 87
0.9%
4 151
1.5%
5 159
1.6%
6 151
1.5%
7 167
1.7%
8 183
1.8%
9 160
1.6%
ValueCountFrequency (%)
7501 1
< 0.1%
5688 1
< 0.1%
3067 1
< 0.1%
2603 1
< 0.1%
2429 1
< 0.1%
1861 1
< 0.1%
1742 1
< 0.1%
1691 1
< 0.1%
1516 1
< 0.1%
1474 1
< 0.1%

Interactions

2024-03-14T01:25:52.944295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:51.778779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:52.070405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:52.405305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:53.019177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:51.844724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:52.140992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:52.700064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:53.101267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:51.916522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:52.222463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:52.780544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:53.185758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:51.997865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:52.326206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:25:52.862673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:25:57.093010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0530.0000.0640.0370.0610.060
대여구분코드0.0531.0000.2000.3590.0100.0270.045
성별0.0000.2001.0000.0740.0530.0000.000
연령대코드0.0640.3590.0741.0000.0880.0200.041
이용건수0.0370.0100.0530.0881.0000.9710.975
이동거리(M)0.0610.0270.0000.0200.9711.0000.994
이용시간(분)0.0600.0450.0000.0410.9750.9941.000
2024-03-14T01:25:57.172085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.0760.230
성별0.0761.0000.045
연령대코드0.2300.0451.000
2024-03-14T01:25:57.241258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.068-0.091-0.1110.0220.0000.030
이용건수-0.0681.0000.7020.7270.0070.0340.047
이동거리(M)-0.0910.7021.0000.8780.0170.0000.011
이용시간(분)-0.1110.7270.8781.0000.0290.0000.022
대여구분코드0.0220.0070.0170.0291.0000.0760.230
성별0.0000.0340.0000.0000.0761.0000.045
연령대코드0.0300.0470.0110.0220.2300.0451.000

Missing values

2024-03-14T01:25:53.305298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:25:53.456661image/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)
36942021-04-01284284. 센트럴 푸르지오 시티 앞일일(회원)FAGE_0022232.62.3310050.03160
135132021-04-01950950. 구산역 2번 출구정기\NAGE_00113.140.311320.012
160822021-04-0111281128. 화곡역 6번출구정기\NAGE_005154.670.552380.2216
71572021-04-01514514. 성수사거리 버스정류장 앞정기\NAGE_00210442.423.3914600.22151
18262021-04-01203203. 국회의사당역 3번출구 옆일일(회원)\NAGE_0026356.453.1913783.18100
56002021-04-01412412. DMC산학협력연구센터 앞정기\NAGE_0022160.30.823520.050
104662021-04-01734734. 신트리공원 입구정기\NAGE_0081000.04
96562021-04-01669669.청계신한신휴플러스앞 삼거리정기\NAGE_0044225.172.058780.3958
129332021-04-01908908. 구산역 4번출구정기MAGE_0023106.780.843610.2928
120622021-04-01825825. 서빙고동 주민센터 앞일일(회원)FAGE_003291.931.024380.6835
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
139972021-04-01993993.은평뉴타운 온뜨락아파트앞정기MAGE_0012111.3614318.1631
88882021-04-01623623. 서울시립대 정문 앞일일(회원)MAGE_0031000.010
55912021-04-01411411. DMC홍보관정기MAGE_0035186.041.496385.2479
84232021-04-01588588. 뚝섬 유원지역정기MAGE_0023267.682.088967.6360
113212021-04-01779779.양천나눔누리센터일일(회원)\NAGE_0031173.842.048780.054
16772021-04-01194194. 증산교 앞정기MAGE_0062183.921.416065.1650
160242021-04-0111261126. 우장산역 1번출구옆(우장산아이파크105동앞)일일(회원)\NAGE_0011000.09
38982021-04-01293293. 충북 미래관정기\NAGE_008113.880.12523.064
97452021-04-01674674.고대앞사거리 교통섬정기FAGE_0033107.061.185094.9232
127112021-04-01869869.노들섬 동측 앞정기MAGE_0041485.343.4714946.4585