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/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 (55.0%)Imbalance
이용시간(분) is highly skewed (γ1 = 20.12950192)Skewed
이동거리(M) has 420 (4.2%) zerosZeros

Reproduction

Analysis started2024-05-18 05:02:20.777921
Analysis finished2024-05-18 05:02:30.268865
Duration9.49 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

2024-05-18T14:02:30.575700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:02:30.913315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-04-01 10000
100.0%

대여소번호
Real number (ℝ)

Distinct813
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean585.354
Minimum102
Maximum1130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:02:31.226574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile144
Q1305
median573
Q3829
95-th percentile1077
Maximum1130
Range1028
Interquartile range (IQR)524

Descriptive statistics

Standard deviation303.18056
Coefficient of variation (CV)0.51794395
Kurtosis-1.197783
Mean585.354
Median Absolute Deviation (MAD)262
Skewness0.1356805
Sum5853540
Variance91918.452
MonotonicityNot monotonic
2024-05-18T14:02:31.766148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 33
 
0.3%
583 33
 
0.3%
780 30
 
0.3%
996 30
 
0.3%
182 29
 
0.3%
207 28
 
0.3%
215 27
 
0.3%
272 26
 
0.3%
131 26
 
0.3%
262 26
 
0.3%
Other values (803) 9712
97.1%
ValueCountFrequency (%)
102 13
0.1%
103 17
0.2%
104 12
0.1%
105 13
0.1%
106 19
0.2%
107 12
0.1%
108 16
0.2%
109 14
0.1%
111 16
0.2%
112 9
0.1%
ValueCountFrequency (%)
1130 9
 
0.1%
1129 12
0.1%
1128 17
0.2%
1127 15
0.1%
1126 16
0.2%
1125 26
0.3%
1124 16
0.2%
1122 15
0.1%
1121 16
0.2%
1120 23
0.2%

대여소
Text

Distinct813
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:02:32.478263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length14.7594
Min length7

Characters and Unicode

Total characters147594
Distinct characters429
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

Unique5 ?
Unique (%)< 0.1%

Sample

1st row1077.강동역 1번출구 앞
2nd row964. 북한산힐스테이트 7차아파트
3rd row182. 망원2빗물펌프장 앞
4th row1070.(시립)고덕평생학습관
5th row130. 신촌역(2호선) 7번출구 앞
ValueCountFrequency (%)
앞 2978
 
9.9%
옆 573
 
1.9%
1번출구 348
 
1.2%
사거리 346
 
1.1%
출구 344
 
1.1%
2번출구 283
 
0.9%
4번출구 273
 
0.9%
입구 233
 
0.8%
뒤 220
 
0.7%
3번출구 217
 
0.7%
Other values (1685) 24399
80.8%
2024-05-18T14:02:33.654814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20390
 
13.8%
. 10000
 
6.8%
1 6183
 
4.2%
2 4381
 
3.0%
앞 3613
 
2.4%
3 3491
 
2.4%
4 3491
 
2.4%
5 3421
 
2.3%
0 3400
 
2.3%
구 3357
 
2.3%
Other values (419) 85867
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77854
52.7%
Decimal Number 35784
24.2%
Space Separator 20390
 
13.8%
Other Punctuation 10035
 
6.8%
Uppercase Letter 1750
 
1.2%
Close Punctuation 842
 
0.6%
Open Punctuation 842
 
0.6%
Lowercase Letter 38
 
< 0.1%
Dash Punctuation 29
 
< 0.1%
Math Symbol 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3613
 
4.6%
구 3357
 
4.3%
역 2710
 
3.5%
번 2450
 
3.1%
출 2445
 
3.1%
동 2132
 
2.7%
교 1611
 
2.1%
리 1493
 
1.9%
아 1299
 
1.7%
원 1288
 
1.7%
Other values (380) 55456
71.2%
Uppercase Letter
ValueCountFrequency (%)
S 245
14.0%
K 202
11.5%
C 187
10.7%
B 127
 
7.3%
I 110
 
6.3%
G 109
 
6.2%
T 106
 
6.1%
D 100
 
5.7%
M 98
 
5.6%
L 88
 
5.0%
Other values (9) 378
21.6%
Decimal Number
ValueCountFrequency (%)
1 6183
17.3%
2 4381
12.2%
3 3491
9.8%
4 3491
9.8%
5 3421
9.6%
0 3400
9.5%
7 3241
9.1%
6 2958
8.3%
8 2711
7.6%
9 2507
7.0%
Other Punctuation
ValueCountFrequency (%)
. 10000
99.7%
, 35
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
k 19
50.0%
t 19
50.0%
Space Separator
ValueCountFrequency (%)
20390
100.0%
Close Punctuation
ValueCountFrequency (%)
) 842
100.0%
Open Punctuation
ValueCountFrequency (%)
( 842
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77854
52.7%
Common 67952
46.0%
Latin 1788
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3613
 
4.6%
구 3357
 
4.3%
역 2710
 
3.5%
번 2450
 
3.1%
출 2445
 
3.1%
동 2132
 
2.7%
교 1611
 
2.1%
리 1493
 
1.9%
아 1299
 
1.7%
원 1288
 
1.7%
Other values (380) 55456
71.2%
Latin
ValueCountFrequency (%)
S 245
13.7%
K 202
11.3%
C 187
10.5%
B 127
 
7.1%
I 110
 
6.2%
G 109
 
6.1%
T 106
 
5.9%
D 100
 
5.6%
M 98
 
5.5%
L 88
 
4.9%
Other values (11) 416
23.3%
Common
ValueCountFrequency (%)
20390
30.0%
. 10000
14.7%
1 6183
 
9.1%
2 4381
 
6.4%
3 3491
 
5.1%
4 3491
 
5.1%
5 3421
 
5.0%
0 3400
 
5.0%
7 3241
 
4.8%
6 2958
 
4.4%
Other values (8) 6996
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77854
52.7%
ASCII 69740
47.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20390
29.2%
. 10000
14.3%
1 6183
 
8.9%
2 4381
 
6.3%
3 3491
 
5.0%
4 3491
 
5.0%
5 3421
 
4.9%
0 3400
 
4.9%
7 3241
 
4.6%
6 2958
 
4.2%
Other values (29) 8784
12.6%
Hangul
ValueCountFrequency (%)
앞 3613
 
4.6%
구 3357
 
4.3%
역 2710
 
3.5%
번 2450
 
3.1%
출 2445
 
3.1%
동 2132
 
2.7%
교 1611
 
2.1%
리 1493
 
1.9%
아 1299
 
1.7%
원 1288
 
1.7%
Other values (380) 55456
71.2%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
6770 
일일(회원)
3019 
일일(비회원)
 
115
단체
 
86
BIL_021
 
10

Length

Max length7
Median length2
Mean length3.2701
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 6770
67.7%
일일(회원) 3019
30.2%
일일(비회원) 115
 
1.1%
단체 86
 
0.9%
BIL_021 10
 
0.1%

Length

2024-05-18T14:02:34.271866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:02:34.711678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 6770
67.7%
일일(회원 3019
30.2%
일일(비회원 115
 
1.1%
단체 86
 
0.9%
bil_021 10
 
0.1%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3279 
M
3224 
F
2637 
<NA>
859 
m
 
1

Length

Max length4
Median length1
Mean length1.5856
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3279
32.8%
M 3224
32.2%
F 2637
26.4%
<NA> 859
 
8.6%
m 1
 
< 0.1%

Length

2024-05-18T14:02:35.293878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:02:35.704827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3279
32.8%
m 3225
32.2%
f 2637
26.4%
na 859
 
8.6%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2788 
AGE_003
2167 
AGE_004
1701 
AGE_005
1222 
AGE_008
794 
Other values (3)
1328 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
AGE_002 2788
27.9%
AGE_003 2167
21.7%
AGE_004 1701
17.0%
AGE_005 1222
12.2%
AGE_008 794
 
7.9%
AGE_001 741
 
7.4%
AGE_006 501
 
5.0%
AGE_007 86
 
0.9%

Length

2024-05-18T14:02:36.263045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:02:36.748548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2788
27.9%
age_003 2167
21.7%
age_004 1701
17.0%
age_005 1222
12.2%
age_008 794
 
7.9%
age_001 741
 
7.4%
age_006 501
 
5.0%
age_007 86
 
0.9%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0296
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:02:37.325312image/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 deviation3.8613229
Coefficient of variation (CV)1.2745323
Kurtosis266.4881
Mean3.0296
Median Absolute Deviation (MAD)1
Skewness10.535486
Sum30296
Variance14.909815
MonotonicityNot monotonic
2024-05-18T14:02:37.832212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 4295
43.0%
2 2049
20.5%
3 1106
 
11.1%
4 675
 
6.8%
5 479
 
4.8%
6 356
 
3.6%
7 250
 
2.5%
8 179
 
1.8%
9 150
 
1.5%
10 110
 
1.1%
Other values (29) 351
 
3.5%
ValueCountFrequency (%)
1 4295
43.0%
2 2049
20.5%
3 1106
 
11.1%
4 675
 
6.8%
5 479
 
4.8%
6 356
 
3.6%
7 250
 
2.5%
8 179
 
1.8%
9 150
 
1.5%
10 110
 
1.1%
ValueCountFrequency (%)
136 1
 
< 0.1%
110 1
 
< 0.1%
108 1
 
< 0.1%
56 1
 
< 0.1%
51 1
 
< 0.1%
42 1
 
< 0.1%
39 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
30 3
< 0.1%

운동량
Text

Distinct8593
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:02:39.380156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.3207
Min length1

Characters and Unicode

Total characters53207
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

Unique7725 ?
Unique (%)77.2%

Sample

1st row40.85
2nd row65.64
3rd row723.42
4th row70.07
5th row268.43
ValueCountFrequency (%)
0 382
 
3.8%
n 38
 
0.4%
119.18 6
 
0.1%
37.07 5
 
< 0.1%
6.16 5
 
< 0.1%
48.65 4
 
< 0.1%
94.71 4
 
< 0.1%
31.4 4
 
< 0.1%
48.13 4
 
< 0.1%
19.29 4
 
< 0.1%
Other values (8583) 9544
95.4%
2024-05-18T14:02:41.071244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9465
17.8%
1 6341
11.9%
2 5256
9.9%
3 4818
9.1%
4 4505
8.5%
5 4128
7.8%
6 4083
7.7%
7 4036
7.6%
8 3760
 
7.1%
9 3685
 
6.9%
Other values (3) 3130
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43666
82.1%
Other Punctuation 9503
 
17.9%
Uppercase Letter 38
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6341
14.5%
2 5256
12.0%
3 4818
11.0%
4 4505
10.3%
5 4128
9.5%
6 4083
9.4%
7 4036
9.2%
8 3760
8.6%
9 3685
8.4%
0 3054
7.0%
Other Punctuation
ValueCountFrequency (%)
. 9465
99.6%
\ 38
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53169
99.9%
Latin 38
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9465
17.8%
1 6341
11.9%
2 5256
9.9%
3 4818
9.1%
4 4505
8.5%
5 4128
7.8%
6 4083
7.7%
7 4036
7.6%
8 3760
 
7.1%
9 3685
 
6.9%
Other values (2) 3092
 
5.8%
Latin
ValueCountFrequency (%)
N 38
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9465
17.8%
1 6341
11.9%
2 5256
9.9%
3 4818
9.1%
4 4505
8.5%
5 4128
7.8%
6 4083
7.7%
7 4036
7.6%
8 3760
 
7.1%
9 3685
 
6.9%
Other values (3) 3130
 
5.9%

탄소량
Text

Distinct1127
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:02:42.092026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.7977
Min length1

Characters and Unicode

Total characters37977
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

Unique364 ?
Unique (%)3.6%

Sample

1st row0.37
2nd row0.64
3rd row8.08
4th row0.63
5th row2.42
ValueCountFrequency (%)
0 387
 
3.9%
0.22 71
 
0.7%
0.25 64
 
0.6%
0.24 62
 
0.6%
0.35 61
 
0.6%
0.36 60
 
0.6%
0.55 60
 
0.6%
0.16 60
 
0.6%
0.21 60
 
0.6%
0.27 58
 
0.6%
Other values (1117) 9057
90.6%
2024-05-18T14:02:43.828310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9509
25.0%
0 5308
14.0%
1 4347
11.4%
2 3410
 
9.0%
3 2837
 
7.5%
4 2432
 
6.4%
5 2348
 
6.2%
6 2095
 
5.5%
7 2023
 
5.3%
8 1833
 
4.8%
Other values (3) 1835
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28392
74.8%
Other Punctuation 9547
 
25.1%
Uppercase Letter 38
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5308
18.7%
1 4347
15.3%
2 3410
12.0%
3 2837
10.0%
4 2432
8.6%
5 2348
8.3%
6 2095
 
7.4%
7 2023
 
7.1%
8 1833
 
6.5%
9 1759
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 9509
99.6%
\ 38
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37939
99.9%
Latin 38
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9509
25.1%
0 5308
14.0%
1 4347
11.5%
2 3410
 
9.0%
3 2837
 
7.5%
4 2432
 
6.4%
5 2348
 
6.2%
6 2095
 
5.5%
7 2023
 
5.3%
8 1833
 
4.8%
Other values (2) 1797
 
4.7%
Latin
ValueCountFrequency (%)
N 38
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9509
25.0%
0 5308
14.0%
1 4347
11.4%
2 3410
 
9.0%
3 2837
 
7.5%
4 2432
 
6.4%
5 2348
 
6.2%
6 2095
 
5.5%
7 2023
 
5.3%
8 1833
 
4.8%
Other values (3) 1835
 
4.8%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9056
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10086.559
Minimum0
Maximum768274.74
Zeros420
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:02:44.509018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile253.5075
Q12014.7825
median5151.07
Q312386.97
95-th percentile33859.83
Maximum768274.74
Range768274.74
Interquartile range (IQR)10372.188

Descriptive statistics

Standard deviation18385.365
Coefficient of variation (CV)1.8227588
Kurtosis529.25277
Mean10086.559
Median Absolute Deviation (MAD)3885.835
Skewness16.287622
Sum1.0086559 × 108
Variance3.3802165 × 108
MonotonicityNot monotonic
2024-05-18T14:02:45.170179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 420
 
4.2%
111.2 6
 
0.1%
1560.0 6
 
0.1%
1170.0 6
 
0.1%
690.0 6
 
0.1%
810.0 5
 
0.1%
1680.0 5
 
0.1%
1750.0 5
 
0.1%
1040.0 5
 
0.1%
333.59 5
 
0.1%
Other values (9046) 9531
95.3%
ValueCountFrequency (%)
0.0 420
4.2%
0.2 1
 
< 0.1%
0.29 1
 
< 0.1%
10.0 2
 
< 0.1%
20.0 1
 
< 0.1%
60.0 1
 
< 0.1%
88.1 1
 
< 0.1%
88.13 1
 
< 0.1%
88.16 3
 
< 0.1%
88.18 1
 
< 0.1%
ValueCountFrequency (%)
768274.74 1
< 0.1%
637582.47 1
< 0.1%
573983.16 1
< 0.1%
262959.08 1
< 0.1%
233153.42 1
< 0.1%
218164.51 1
< 0.1%
216969.0 1
< 0.1%
196283.84 1
< 0.1%
196028.37 1
< 0.1%
168877.99 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct581
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.798
Minimum0
Maximum7501
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:02:45.674925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.95
Q119
median50
Q3109.25
95-th percentile291
Maximum7501
Range7501
Interquartile range (IQR)90.25

Descriptive statistics

Standard deviation167.53159
Coefficient of variation (CV)1.8656495
Kurtosis735.90266
Mean89.798
Median Absolute Deviation (MAD)36
Skewness20.129502
Sum897980
Variance28066.834
MonotonicityNot monotonic
2024-05-18T14:02:46.098583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 188
 
1.9%
10 171
 
1.7%
11 166
 
1.7%
7 154
 
1.5%
9 153
 
1.5%
6 152
 
1.5%
4 151
 
1.5%
5 150
 
1.5%
12 148
 
1.5%
15 140
 
1.4%
Other values (571) 8427
84.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 23
 
0.2%
2 76
0.8%
3 99
1.0%
4 151
1.5%
5 150
1.5%
6 152
1.5%
7 154
1.5%
8 188
1.9%
9 153
1.5%
ValueCountFrequency (%)
7501 1
< 0.1%
6485 1
< 0.1%
5688 1
< 0.1%
2603 1
< 0.1%
2429 1
< 0.1%
1933 1
< 0.1%
1742 1
< 0.1%
1694 1
< 0.1%
1677 1
< 0.1%
1474 1
< 0.1%

Interactions

2024-05-18T14:02:27.731092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:23.788738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:25.117381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:26.505673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:28.107729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:24.098152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:25.401438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:26.808155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:28.508412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:24.423664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:25.712919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:27.126244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:28.888738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:24.784526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:26.149396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:02:27.429536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:02:46.402337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0570.0380.0460.0220.0510.049
대여구분코드0.0571.0000.1040.3560.0000.0220.042
성별0.0380.1041.0000.1310.0770.0000.000
연령대코드0.0460.3560.1311.0000.1260.0260.034
이용건수0.0220.0000.0770.1261.0000.9320.934
이동거리(M)0.0510.0220.0000.0260.9321.0000.997
이용시간(분)0.0490.0420.0000.0340.9340.9971.000
2024-05-18T14:02:46.759973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0590.227
성별0.0591.0000.085
대여구분코드0.2270.0851.000
2024-05-18T14:02:47.123257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.060-0.083-0.0960.0240.0230.022
이용건수-0.0601.0000.7090.7280.0000.0360.042
이동거리(M)-0.0830.7091.0000.8790.0140.0000.014
이용시간(분)-0.0960.7280.8791.0000.0270.0000.018
대여구분코드0.0240.0000.0140.0271.0000.0850.227
성별0.0230.0360.0000.0000.0851.0000.059
연령대코드0.0220.0420.0140.0180.2270.0591.000

Missing values

2024-05-18T14:02:29.383772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:02:30.041166image/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)
153472021-04-0110771077.강동역 1번출구 앞정기\NAGE_008140.850.371586.9410
137282021-04-01964964. 북한산힐스테이트 7차아파트일일(회원)FAGE_003165.640.642762.629
14862021-04-01182182. 망원2빗물펌프장 앞정기FAGE_00211723.428.0834798.06320
152092021-04-0110701070.(시립)고덕평생학습관일일(비회원)\NAGE_008170.070.632722.2148
5832021-04-01130130. 신촌역(2호선) 7번출구 앞일일(비회원)\NAGE_0081268.432.4210428.3685
22522021-04-01219219. 롯데캐슬엠파이어 옆정기FAGE_006150.040.542340.023
46642021-04-01347347. 동대문역사문화공원역 9번출구 앞정기MAGE_003183.370.612631.5215
43362021-04-01326326. 안국역 5번출구 앞정기MAGE_003567.970.512200.0632
125632021-04-01853853.용산역 맞은편정기\NAGE_006124.950.21900.09
81752021-04-01577577. 광진청소년수련관정기MAGE_005176.190.692960.032
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
24552021-04-01227227. 양평2나들목 보행통로 입구일일(회원)FAGE_0031000.060
60382021-04-01436436. 이대역 5번출구일일(회원)MAGE_0081295.422.048776.6552
88282021-04-01617617. 청솔우성아파트 앞일일(회원)\NAGE_0023252.262.299901.078
96412021-04-01668668.서울축산농협(장안지점)정기MAGE_00214666.385.9725747.26234
10172021-04-01153153. 성산2교 사거리정기MAGE_001137.60.341460.578
131642021-04-01921921. 신도고등학교정기MAGE_0042297.171.968440.043
10512021-04-01154154. 마포구청역정기MAGE_004275.050.522227.8222
53962021-04-01399399. 서울역 센트럴 자이아파트일일(회원)\NAGE_0021256.721.586823.9660
109792021-04-01762762. 오목로 무중력지대 앞정기MAGE_001162.180.592532.5820
85882021-04-01602602. 장안동 사거리정기<NA>AGE_0023254.522.7611896.7865