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
이동시간(분) is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
대여구분코드 is highly imbalanced (57.3%)Imbalance
이동거리(M) has 266 (2.7%) zerosZeros

Reproduction

Analysis started2024-05-18 05:01:18.225464
Analysis finished2024-05-18 05:01:29.286038
Duration11.06 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
2021-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-06-01
2nd row2021-06-01
3rd row2021-06-01
4th row2021-06-01
5th row2021-06-01

Common Values

ValueCountFrequency (%)
2021-06-01 10000
100.0%

Length

2024-05-18T14:01:29.677813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:01:30.171388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 10000
100.0%

대여소번호
Real number (ℝ)

Distinct838
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.1359
Minimum102
Maximum1157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:01:30.671168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile145
Q1318
median587
Q3850
95-th percentile1117
Maximum1157
Range1055
Interquartile range (IQR)532

Descriptive statistics

Standard deviation313.40528
Coefficient of variation (CV)0.51790891
Kurtosis-1.2121267
Mean605.1359
Median Absolute Deviation (MAD)265
Skewness0.12453879
Sum6051359
Variance98222.867
MonotonicityNot monotonic
2024-05-18T14:01:31.228187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 28
 
0.3%
262 28
 
0.3%
232 26
 
0.3%
207 26
 
0.3%
502 26
 
0.3%
1124 25
 
0.2%
152 25
 
0.2%
133 25
 
0.2%
1153 25
 
0.2%
583 24
 
0.2%
Other values (828) 9742
97.4%
ValueCountFrequency (%)
102 16
0.2%
103 18
0.2%
104 13
0.1%
105 12
0.1%
106 15
0.1%
107 15
0.1%
108 18
0.2%
109 12
0.1%
111 10
0.1%
112 13
0.1%
ValueCountFrequency (%)
1157 16
0.2%
1155 15
0.1%
1153 25
0.2%
1152 19
0.2%
1151 15
0.1%
1150 13
0.1%
1149 21
0.2%
1148 13
0.1%
1146 13
0.1%
1145 11
0.1%

대여소
Text

Distinct838
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:01:31.894972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length25
Mean length14.8051
Min length7

Characters and Unicode

Total characters148051
Distinct characters438
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

Unique6 ?
Unique (%)0.1%

Sample

1st row367. 독립문역 3-1번출구
2nd row1060. 천일초교 사거리
3rd row391. 정동길입구
4th row342. 대학로 마로니에공원
5th row385. 종각역 5번출구
ValueCountFrequency (%)
앞 2978
 
9.8%
옆 537
 
1.8%
출구 376
 
1.2%
사거리 334
 
1.1%
1번출구 323
 
1.1%
2번출구 303
 
1.0%
4번출구 271
 
0.9%
뒤 260
 
0.9%
3번출구 214
 
0.7%
버스정류장 212
 
0.7%
Other values (1731) 24497
80.8%
2024-05-18T14:01:33.247672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20472
 
13.8%
. 10000
 
6.8%
1 6801
 
4.6%
2 4380
 
3.0%
3 3606
 
2.4%
앞 3554
 
2.4%
4 3511
 
2.4%
5 3410
 
2.3%
0 3409
 
2.3%
구 3355
 
2.3%
Other values (428) 85553
57.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77925
52.6%
Decimal Number 36244
24.5%
Space Separator 20472
 
13.8%
Other Punctuation 10061
 
6.8%
Uppercase Letter 1661
 
1.1%
Close Punctuation 783
 
0.5%
Open Punctuation 783
 
0.5%
Dash Punctuation 53
 
< 0.1%
Lowercase Letter 38
 
< 0.1%
Math Symbol 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3554
 
4.6%
구 3355
 
4.3%
역 2790
 
3.6%
번 2506
 
3.2%
출 2469
 
3.2%
동 2229
 
2.9%
리 1532
 
2.0%
교 1498
 
1.9%
원 1273
 
1.6%
아 1272
 
1.6%
Other values (389) 55447
71.2%
Uppercase Letter
ValueCountFrequency (%)
S 225
13.5%
K 194
11.7%
C 179
10.8%
B 115
 
6.9%
G 115
 
6.9%
I 102
 
6.1%
D 100
 
6.0%
T 99
 
6.0%
L 98
 
5.9%
M 75
 
4.5%
Other values (9) 359
21.6%
Decimal Number
ValueCountFrequency (%)
1 6801
18.8%
2 4380
12.1%
3 3606
9.9%
4 3511
9.7%
5 3410
9.4%
0 3409
9.4%
7 3231
8.9%
6 2839
7.8%
8 2620
 
7.2%
9 2437
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 10000
99.4%
, 61
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
k 19
50.0%
t 19
50.0%
Space Separator
ValueCountFrequency (%)
20472
100.0%
Close Punctuation
ValueCountFrequency (%)
) 783
100.0%
Open Punctuation
ValueCountFrequency (%)
( 783
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 53
100.0%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77925
52.6%
Common 68427
46.2%
Latin 1699
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3554
 
4.6%
구 3355
 
4.3%
역 2790
 
3.6%
번 2506
 
3.2%
출 2469
 
3.2%
동 2229
 
2.9%
리 1532
 
2.0%
교 1498
 
1.9%
원 1273
 
1.6%
아 1272
 
1.6%
Other values (389) 55447
71.2%
Latin
ValueCountFrequency (%)
S 225
13.2%
K 194
11.4%
C 179
10.5%
B 115
 
6.8%
G 115
 
6.8%
I 102
 
6.0%
D 100
 
5.9%
T 99
 
5.8%
L 98
 
5.8%
M 75
 
4.4%
Other values (11) 397
23.4%
Common
ValueCountFrequency (%)
20472
29.9%
. 10000
14.6%
1 6801
 
9.9%
2 4380
 
6.4%
3 3606
 
5.3%
4 3511
 
5.1%
5 3410
 
5.0%
0 3409
 
5.0%
7 3231
 
4.7%
6 2839
 
4.1%
Other values (8) 6768
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77925
52.6%
ASCII 70126
47.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20472
29.2%
. 10000
14.3%
1 6801
 
9.7%
2 4380
 
6.2%
3 3606
 
5.1%
4 3511
 
5.0%
5 3410
 
4.9%
0 3409
 
4.9%
7 3231
 
4.6%
6 2839
 
4.0%
Other values (29) 8467
12.1%
Hangul
ValueCountFrequency (%)
앞 3554
 
4.6%
구 3355
 
4.3%
역 2790
 
3.6%
번 2506
 
3.2%
출 2469
 
3.2%
동 2229
 
2.9%
리 1532
 
2.0%
교 1498
 
1.9%
원 1273
 
1.6%
아 1272
 
1.6%
Other values (389) 55447
71.2%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7252 
일일(회원)
2520 
일일(비회원)
 
137
단체
 
58
BIL_021
 
33

Length

Max length7
Median length2
Mean length3.093
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기
2nd row정기
3rd row정기
4th row정기
5th row정기

Common Values

ValueCountFrequency (%)
정기 7252
72.5%
일일(회원) 2520
 
25.2%
일일(비회원) 137
 
1.4%
단체 58
 
0.6%
BIL_021 33
 
0.3%

Length

2024-05-18T14:01:33.954221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:01:34.460212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7252
72.5%
일일(회원 2520
 
25.2%
일일(비회원 137
 
1.4%
단체 58
 
0.6%
bil_021 33
 
0.3%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3382 
\N
3067 
F
2690 
<NA>
857 
f
 
3

Length

Max length4
Median length1
Mean length1.5638
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3382
33.8%
\N 3067
30.7%
F 2690
26.9%
<NA> 857
 
8.6%
f 3
 
< 0.1%
m 1
 
< 0.1%

Length

2024-05-18T14:01:34.953851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:01:35.419132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3383
33.8%
n 3067
30.7%
f 2693
26.9%
na 857
 
8.6%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2618 
AGE_003
1971 
AGE_004
1532 
AGE_008
1474 
AGE_005
1176 
Other values (3)
1229 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_002
2nd rowAGE_003
3rd rowAGE_005
4th rowAGE_005
5th rowAGE_004

Common Values

ValueCountFrequency (%)
AGE_002 2618
26.2%
AGE_003 1971
19.7%
AGE_004 1532
15.3%
AGE_008 1474
14.7%
AGE_005 1176
11.8%
AGE_001 687
 
6.9%
AGE_006 465
 
4.7%
AGE_007 77
 
0.8%

Length

2024-05-18T14:01:36.096399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:01:36.563672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2618
26.2%
age_003 1971
19.7%
age_004 1532
15.3%
age_008 1474
14.7%
age_005 1176
11.8%
age_001 687
 
6.9%
age_006 465
 
4.7%
age_007 77
 
0.8%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7796
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:01:37.201737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum45
Range44
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0198238
Coefficient of variation (CV)1.0864239
Kurtosis24.054445
Mean2.7796
Median Absolute Deviation (MAD)1
Skewness3.7643236
Sum27796
Variance9.1193358
MonotonicityNot monotonic
2024-05-18T14:01:37.692168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 4506
45.1%
2 2091
20.9%
3 1120
 
11.2%
4 686
 
6.9%
5 457
 
4.6%
6 279
 
2.8%
7 223
 
2.2%
8 137
 
1.4%
9 122
 
1.2%
10 75
 
0.8%
Other values (24) 304
 
3.0%
ValueCountFrequency (%)
1 4506
45.1%
2 2091
20.9%
3 1120
 
11.2%
4 686
 
6.9%
5 457
 
4.6%
6 279
 
2.8%
7 223
 
2.2%
8 137
 
1.4%
9 122
 
1.2%
10 75
 
0.8%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
42 1
 
< 0.1%
31 2
< 0.1%
30 2
< 0.1%
29 2
< 0.1%
28 3
< 0.1%
27 1
 
< 0.1%
26 2
< 0.1%
25 3
< 0.1%

운동량
Text

Distinct8440
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:01:38.663452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.3457
Min length1

Characters and Unicode

Total characters53457
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

Unique7347 ?
Unique (%)73.5%

Sample

1st row400.1
2nd row52.34
3rd row71.56
4th row202.17
5th row898.91
ValueCountFrequency (%)
0 234
 
2.3%
n 32
 
0.3%
31.36 6
 
0.1%
19.56 5
 
< 0.1%
50.45 5
 
< 0.1%
56.37 4
 
< 0.1%
61.78 4
 
< 0.1%
30.62 4
 
< 0.1%
113.68 4
 
< 0.1%
11.29 4
 
< 0.1%
Other values (8430) 9698
97.0%
2024-05-18T14:01:40.096135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9655
18.1%
1 6492
12.1%
2 5242
9.8%
3 4799
9.0%
4 4458
8.3%
5 4305
8.1%
6 4105
7.7%
7 3880
7.3%
8 3844
 
7.2%
9 3753
 
7.0%
Other values (3) 2924
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43738
81.8%
Other Punctuation 9687
 
18.1%
Uppercase Letter 32
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6492
14.8%
2 5242
12.0%
3 4799
11.0%
4 4458
10.2%
5 4305
9.8%
6 4105
9.4%
7 3880
8.9%
8 3844
8.8%
9 3753
8.6%
0 2860
6.5%
Other Punctuation
ValueCountFrequency (%)
. 9655
99.7%
\ 32
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53425
99.9%
Latin 32
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9655
18.1%
1 6492
12.2%
2 5242
9.8%
3 4799
9.0%
4 4458
8.3%
5 4305
8.1%
6 4105
7.7%
7 3880
7.3%
8 3844
 
7.2%
9 3753
 
7.0%
Other values (2) 2892
 
5.4%
Latin
ValueCountFrequency (%)
N 32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9655
18.1%
1 6492
12.1%
2 5242
9.8%
3 4799
9.0%
4 4458
8.3%
5 4305
8.1%
6 4105
7.7%
7 3880
7.3%
8 3844
 
7.2%
9 3753
 
7.0%
Other values (3) 2924
 
5.5%

탄소량
Text

Distinct1008
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:01:41.086062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.829
Min length1

Characters and Unicode

Total characters38290
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

Unique319 ?
Unique (%)3.2%

Sample

1st row3.8
2nd row0.58
3rd row0.62
4th row1.75
5th row6.9
ValueCountFrequency (%)
0 237
 
2.4%
0.29 83
 
0.8%
0.3 83
 
0.8%
0.23 83
 
0.8%
0.22 77
 
0.8%
0.38 77
 
0.8%
0.32 72
 
0.7%
0.24 71
 
0.7%
0.25 71
 
0.7%
0.19 69
 
0.7%
Other values (998) 9077
90.8%
2024-05-18T14:01:42.698186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9660
25.2%
0 5615
14.7%
1 4349
11.4%
2 3483
 
9.1%
3 2897
 
7.6%
4 2472
 
6.5%
5 2224
 
5.8%
6 2027
 
5.3%
7 1932
 
5.0%
8 1849
 
4.8%
Other values (3) 1782
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28566
74.6%
Other Punctuation 9692
 
25.3%
Uppercase Letter 32
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5615
19.7%
1 4349
15.2%
2 3483
12.2%
3 2897
10.1%
4 2472
8.7%
5 2224
 
7.8%
6 2027
 
7.1%
7 1932
 
6.8%
8 1849
 
6.5%
9 1718
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 9660
99.7%
\ 32
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38258
99.9%
Latin 32
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9660
25.2%
0 5615
14.7%
1 4349
11.4%
2 3483
 
9.1%
3 2897
 
7.6%
4 2472
 
6.5%
5 2224
 
5.8%
6 2027
 
5.3%
7 1932
 
5.0%
8 1849
 
4.8%
Other values (2) 1750
 
4.6%
Latin
ValueCountFrequency (%)
N 32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9660
25.2%
0 5615
14.7%
1 4349
11.4%
2 3483
 
9.1%
3 2897
 
7.6%
4 2472
 
6.5%
5 2224
 
5.8%
6 2027
 
5.3%
7 1932
 
5.0%
8 1849
 
4.8%
Other values (3) 1782
 
4.7%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8787
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8269.7444
Minimum0
Maximum169711.84
Zeros266
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:01:43.178424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile478.473
Q11840
median4568.92
Q310429.552
95-th percentile28351.554
Maximum169711.84
Range169711.84
Interquartile range (IQR)8589.5525

Descriptive statistics

Standard deviation11012.874
Coefficient of variation (CV)1.3317067
Kurtosis30.283636
Mean8269.7444
Median Absolute Deviation (MAD)3299.235
Skewness4.0577191
Sum82697444
Variance1.2128339 × 108
MonotonicityNot monotonic
2024-05-18T14:01:43.701894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 266
 
2.7%
1280.0 9
 
0.1%
1360.0 9
 
0.1%
950.0 9
 
0.1%
850.0 8
 
0.1%
1540.0 8
 
0.1%
1020.0 8
 
0.1%
1130.0 8
 
0.1%
1670.0 8
 
0.1%
222.39 8
 
0.1%
Other values (8777) 9659
96.6%
ValueCountFrequency (%)
0.0 266
2.7%
0.29 1
 
< 0.1%
10.0 1
 
< 0.1%
20.0 1
 
< 0.1%
70.0 1
 
< 0.1%
80.0 1
 
< 0.1%
88.12 1
 
< 0.1%
88.16 1
 
< 0.1%
88.17 1
 
< 0.1%
88.18 1
 
< 0.1%
ValueCountFrequency (%)
169711.84 1
< 0.1%
165355.53 1
< 0.1%
153954.03 1
< 0.1%
151651.09 1
< 0.1%
123519.12 1
< 0.1%
121676.12 1
< 0.1%
121436.76 1
< 0.1%
119694.22 1
< 0.1%
118050.83 1
< 0.1%
115870.5 1
< 0.1%

이동시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct458
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.7207
Minimum0
Maximum1572
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:01:44.188914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q117
median43
Q393
95-th percentile227
Maximum1572
Range1572
Interquartile range (IQR)76

Descriptive statistics

Standard deviation87.983054
Coefficient of variation (CV)1.244092
Kurtosis36.137306
Mean70.7207
Median Absolute Deviation (MAD)31
Skewness4.1499112
Sum707207
Variance7741.0178
MonotonicityNot monotonic
2024-05-18T14:01:44.752821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 200
 
2.0%
7 197
 
2.0%
6 191
 
1.9%
9 188
 
1.9%
11 184
 
1.8%
5 179
 
1.8%
10 175
 
1.8%
13 172
 
1.7%
4 163
 
1.6%
14 161
 
1.6%
Other values (448) 8190
81.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 20
 
0.2%
2 76
 
0.8%
3 127
1.3%
4 163
1.6%
5 179
1.8%
6 191
1.9%
7 197
2.0%
8 200
2.0%
9 188
1.9%
ValueCountFrequency (%)
1572 1
< 0.1%
1511 1
< 0.1%
1336 1
< 0.1%
1241 1
< 0.1%
1146 1
< 0.1%
1072 1
< 0.1%
966 1
< 0.1%
868 1
< 0.1%
784 1
< 0.1%
767 1
< 0.1%

Interactions

2024-05-18T14:01:25.727613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:21.316343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:22.579728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:23.936487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:26.191892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:21.612241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:22.918094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:24.289328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:26.593109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:21.934101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:23.261320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:24.963136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:27.027192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:22.243906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:23.627515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:01:25.362352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:01:45.088224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이동시간(분)
대여소번호1.0000.0000.0000.0520.0570.1440.113
대여구분코드0.0001.0000.2210.3000.1440.0990.122
성별0.0000.2211.0000.1150.0660.0730.061
연령대코드0.0520.3000.1151.0000.2730.1200.113
이용건수0.0570.1440.0660.2731.0000.6630.690
이동거리(M)0.1440.0990.0730.1200.6631.0000.926
이동시간(분)0.1130.1220.0610.1130.6900.9261.000
2024-05-18T14:01:45.397410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0700.189
성별0.0701.0000.084
대여구분코드0.1890.0841.000
2024-05-18T14:01:45.751525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이동시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.051-0.102-0.1040.0000.0000.025
이용건수-0.0511.0000.6840.7050.0880.0410.094
이동거리(M)-0.1020.6841.0000.8750.0410.0310.057
이동시간(분)-0.1040.7050.8751.0000.0510.0250.054
대여구분코드0.0000.0880.0410.0511.0000.0840.189
성별0.0000.0410.0310.0250.0841.0000.070
연령대코드0.0250.0940.0570.0540.1890.0701.000

Missing values

2024-05-18T14:01:27.707955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:01:28.733906image/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)이동시간(분)
47862021-06-01367367. 독립문역 3-1번출구정기\NAGE_0024400.13.816404.18148
146342021-06-0110601060. 천일초교 사거리정기\NAGE_003152.340.582493.7741
51552021-06-01391391. 정동길입구정기\NAGE_005371.560.622640.5814
44162021-06-01342342. 대학로 마로니에공원정기\NAGE_0052202.171.757542.8174
50742021-06-01385385. 종각역 5번출구정기MAGE_0046898.916.929730.37157
99912021-06-01722722. LG전자베스트샵 신정점정기FAGE_001142.230.492132.8412
103392021-06-01742742. 등촌역 5번 출구 뒤정기<NA>AGE_004145.690.331407.1113
65032021-06-01490490.가온문화공원일일(회원)\NAGE_0041000.02
87762021-06-01630630. 동대문구 보건소정기MAGE_001245.230.421800.021
19582021-06-01212212. 여의도역 1번출구 옆정기MAGE_0056466.983.8816746.0198
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이동시간(분)
147192021-06-0110651065.보훈병원 정문옆(중앙대영약국)정기FAGE_002379.510.944056.9531
40512021-06-01314314. 국립현대미술관정기\NAGE_006272.160.572429.48105
119062021-06-01832832. 이촌1동 주민센터 뒤정기FAGE_0032126.881.245340.060
126752021-06-01913913. 이마트 은평점정기\NAGE_0046345.293.3214326.5275
88402021-06-01634634. 외국어대 정문 앞정기\NAGE_002181074.829.8842553.27277
75622021-06-01557557. 도선동 주민센터 앞일일(회원)MAGE_003148.810.441896.232
30152021-06-01260260. 여의도 마리나선착장 앞정기\NAGE_0021210.451.355840.030
6892021-06-01137137. NH농협 신촌지점 앞정기\NAGE_004113.610.1440.753
100532021-06-01725725. 양강중학교앞 교차로정기MAGE_00415.970.06239.372
4252021-06-01121121. 마포소방서 앞정기\NAGE_0027267.442.7411784.683