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-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
이용시간(분) is highly skewed (γ1 = 21.569527)Skewed
이동거리(M) has 139 (1.4%) zerosZeros

Reproduction

Analysis started2024-05-18 00:15:32.700581
Analysis finished2024-05-18 00:15:39.812403
Duration7.11 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
2022-03
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03
2nd row2022-03
3rd row2022-03
4th row2022-03
5th row2022-03

Common Values

ValueCountFrequency (%)
2022-03 10000
100.0%

Length

2024-05-18T09:15:40.029701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:15:40.307688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03 10000
100.0%

대여소번호
Real number (ℝ)

Distinct1972
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1453.2957
Minimum102
Maximum3553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:15:40.621592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile207
Q1677
median1327
Q32172.25
95-th percentile3122.05
Maximum3553
Range3451
Interquartile range (IQR)1495.25

Descriptive statistics

Standard deviation906.73381
Coefficient of variation (CV)0.62391556
Kurtosis-0.75882441
Mean1453.2957
Median Absolute Deviation (MAD)730
Skewness0.42204916
Sum14532957
Variance822166.2
MonotonicityNot monotonic
2024-05-18T09:15:41.090372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
259 15
 
0.1%
1210 15
 
0.1%
2708 14
 
0.1%
185 14
 
0.1%
1509 14
 
0.1%
829 13
 
0.1%
207 13
 
0.1%
3123 13
 
0.1%
746 13
 
0.1%
2183 12
 
0.1%
Other values (1962) 9864
98.6%
ValueCountFrequency (%)
102 4
< 0.1%
103 9
0.1%
104 5
0.1%
105 7
0.1%
106 8
0.1%
107 3
 
< 0.1%
108 4
< 0.1%
109 6
0.1%
111 5
0.1%
112 5
0.1%
ValueCountFrequency (%)
3553 2
 
< 0.1%
3552 7
0.1%
3551 5
0.1%
3550 5
0.1%
3549 2
 
< 0.1%
3548 5
0.1%
3547 4
 
< 0.1%
3545 5
0.1%
3544 10
0.1%
3543 2
 
< 0.1%

대여소명
Text

Distinct1972
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:15:41.595681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length30
Mean length15.4687
Min length7

Characters and Unicode

Total characters154687
Distinct characters545
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

Unique80 ?
Unique (%)0.8%

Sample

1st row2658.가락몰 업무동
2nd row2389. 경기여자고등학교 후문 (삼성로3길 입구)
3rd row247. 당산역 10번출구 앞
4th row1670. 노원경찰서교차로
5th row2054. 삼익아파트
ValueCountFrequency (%)
앞 2665
 
9.1%
옆 444
 
1.5%
출구 442
 
1.5%
1번출구 282
 
1.0%
교차로 254
 
0.9%
사거리 240
 
0.8%
3번출구 233
 
0.8%
2번출구 222
 
0.8%
입구 206
 
0.7%
4번출구 205
 
0.7%
Other values (3910) 23982
82.2%
2024-05-18T09:15:42.557991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19371
 
12.5%
. 10041
 
6.5%
1 8641
 
5.6%
2 6930
 
4.5%
3 4697
 
3.0%
4 3508
 
2.3%
0 3468
 
2.2%
구 3457
 
2.2%
5 3376
 
2.2%
6 3091
 
2.0%
Other values (535) 88107
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80006
51.7%
Decimal Number 41716
27.0%
Space Separator 19371
 
12.5%
Other Punctuation 10158
 
6.6%
Uppercase Letter 1290
 
0.8%
Open Punctuation 952
 
0.6%
Close Punctuation 952
 
0.6%
Lowercase Letter 143
 
0.1%
Dash Punctuation 71
 
< 0.1%
Math Symbol 18
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3457
 
4.3%
앞 3083
 
3.9%
역 2653
 
3.3%
번 2385
 
3.0%
출 2320
 
2.9%
동 2156
 
2.7%
교 1755
 
2.2%
리 1420
 
1.8%
파 1319
 
1.6%
아 1307
 
1.6%
Other values (478) 58151
72.7%
Uppercase Letter
ValueCountFrequency (%)
S 147
11.4%
K 144
11.2%
C 136
10.5%
T 101
 
7.8%
D 90
 
7.0%
M 88
 
6.8%
A 86
 
6.7%
G 76
 
5.9%
L 71
 
5.5%
I 64
 
5.0%
Other values (13) 287
22.2%
Lowercase Letter
ValueCountFrequency (%)
e 48
33.6%
n 14
 
9.8%
l 14
 
9.8%
s 12
 
8.4%
k 12
 
8.4%
v 8
 
5.6%
y 7
 
4.9%
o 7
 
4.9%
t 7
 
4.9%
c 7
 
4.9%
Decimal Number
ValueCountFrequency (%)
1 8641
20.7%
2 6930
16.6%
3 4697
11.3%
4 3508
8.4%
0 3468
8.3%
5 3376
 
8.1%
6 3091
 
7.4%
7 3041
 
7.3%
9 2545
 
6.1%
8 2419
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 10041
98.8%
, 87
 
0.9%
? 13
 
0.1%
& 12
 
0.1%
· 5
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 13
72.2%
+ 5
 
27.8%
Space Separator
ValueCountFrequency (%)
19371
100.0%
Open Punctuation
ValueCountFrequency (%)
( 952
100.0%
Close Punctuation
ValueCountFrequency (%)
) 952
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80013
51.7%
Common 73241
47.3%
Latin 1433
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3457
 
4.3%
앞 3083
 
3.9%
역 2653
 
3.3%
번 2385
 
3.0%
출 2320
 
2.9%
동 2156
 
2.7%
교 1755
 
2.2%
리 1420
 
1.8%
파 1319
 
1.6%
아 1307
 
1.6%
Other values (479) 58158
72.7%
Latin
ValueCountFrequency (%)
S 147
 
10.3%
K 144
 
10.0%
C 136
 
9.5%
T 101
 
7.0%
D 90
 
6.3%
M 88
 
6.1%
A 86
 
6.0%
G 76
 
5.3%
L 71
 
5.0%
I 64
 
4.5%
Other values (24) 430
30.0%
Common
ValueCountFrequency (%)
19371
26.4%
. 10041
13.7%
1 8641
11.8%
2 6930
 
9.5%
3 4697
 
6.4%
4 3508
 
4.8%
0 3468
 
4.7%
5 3376
 
4.6%
6 3091
 
4.2%
7 3041
 
4.2%
Other values (12) 7077
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80006
51.7%
ASCII 74669
48.3%
None 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19371
25.9%
. 10041
13.4%
1 8641
11.6%
2 6930
 
9.3%
3 4697
 
6.3%
4 3508
 
4.7%
0 3468
 
4.6%
5 3376
 
4.5%
6 3091
 
4.1%
7 3041
 
4.1%
Other values (45) 8505
11.4%
Hangul
ValueCountFrequency (%)
구 3457
 
4.3%
앞 3083
 
3.9%
역 2653
 
3.3%
번 2385
 
3.0%
출 2320
 
2.9%
동 2156
 
2.7%
교 1755
 
2.2%
리 1420
 
1.8%
파 1319
 
1.6%
아 1307
 
1.6%
Other values (478) 58151
72.7%
None
ValueCountFrequency (%)
㈜ 7
58.3%
· 5
41.7%

대여구분코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5229 
일일(회원)
3863 
단체
642 
일일(비회원)
 
264
10분이용권
 
2

Length

Max length7
Median length2
Mean length3.678
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5229
52.3%
일일(회원) 3863
38.6%
단체 642
 
6.4%
일일(비회원) 264
 
2.6%
10분이용권 2
 
< 0.1%

Length

2024-05-18T09:15:42.970130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:15:43.210298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5229
52.3%
일일(회원 3863
38.6%
단체 642
 
6.4%
일일(비회원 264
 
2.6%
10분이용권 2
 
< 0.1%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3113 
\N
2967 
F
2728 
<NA>
1191 
m
 
1

Length

Max length4
Median length1
Mean length1.654
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3113
31.1%
\N 2967
29.7%
F 2728
27.3%
<NA> 1191
 
11.9%
m 1
 
< 0.1%

Length

2024-05-18T09:15:43.450566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:15:43.653564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3114
31.1%
n 2967
29.7%
f 2728
27.3%
na 1191
 
11.9%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
1818 
30대
1658 
40대
1570 
기타
1471 
50대
1235 
Other values (3)
2248 

Length

Max length5
Median length3
Mean length2.9057
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row40대
3rd row20대
4th row50대
5th row10대

Common Values

ValueCountFrequency (%)
20대 1818
18.2%
30대 1658
16.6%
40대 1570
15.7%
기타 1471
14.7%
50대 1235
12.3%
10대 1209
12.1%
60대 775
7.8%
70대이상 264
 
2.6%

Length

2024-05-18T09:15:43.870772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:15:44.181716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 1818
18.2%
30대 1658
16.6%
40대 1570
15.7%
기타 1471
14.7%
50대 1235
12.3%
10대 1209
12.1%
60대 775
7.8%
70대이상 264
 
2.6%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct252
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5322
Minimum1
Maximum1003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:15:44.453006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q323
95-th percentile91
Maximum1003
Range1002
Interquartile range (IQR)21

Descriptive statistics

Standard deviation38.865813
Coefficient of variation (CV)1.8050089
Kurtosis65.113143
Mean21.5322
Median Absolute Deviation (MAD)6
Skewness5.4728502
Sum215322
Variance1510.5514
MonotonicityNot monotonic
2024-05-18T09:15:44.725774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1497
 
15.0%
2 1185
 
11.8%
3 729
 
7.3%
4 543
 
5.4%
5 459
 
4.6%
6 387
 
3.9%
7 313
 
3.1%
8 270
 
2.7%
9 246
 
2.5%
10 222
 
2.2%
Other values (242) 4149
41.5%
ValueCountFrequency (%)
1 1497
15.0%
2 1185
11.8%
3 729
7.3%
4 543
 
5.4%
5 459
 
4.6%
6 387
 
3.9%
7 313
 
3.1%
8 270
 
2.7%
9 246
 
2.5%
10 222
 
2.2%
ValueCountFrequency (%)
1003 1
< 0.1%
565 1
< 0.1%
504 1
< 0.1%
458 1
< 0.1%
453 1
< 0.1%
402 1
< 0.1%
401 1
< 0.1%
397 1
< 0.1%
380 1
< 0.1%
373 1
< 0.1%

운동량
Text

Distinct9514
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:15:45.418655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.1733
Min length2

Characters and Unicode

Total characters61733
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

Unique9188 ?
Unique (%)91.9%

Sample

1st row3539.76
2nd row963.03
3rd row14457.29
4th row1677.81
5th row260.10
ValueCountFrequency (%)
0.00 134
 
1.3%
n 9
 
0.1%
26.25 4
 
< 0.1%
84.92 3
 
< 0.1%
59.20 3
 
< 0.1%
8.20 3
 
< 0.1%
44.02 3
 
< 0.1%
139.00 3
 
< 0.1%
23.28 3
 
< 0.1%
24.12 3
 
< 0.1%
Other values (9504) 9832
98.3%
2024-05-18T09:15:46.554292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9991
16.2%
1 7218
11.7%
2 5956
9.6%
3 5481
8.9%
4 5090
8.2%
5 4915
8.0%
6 4852
7.9%
0 4742
7.7%
7 4576
7.4%
9 4454
7.2%
Other values (3) 4458
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51724
83.8%
Other Punctuation 10000
 
16.2%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7218
14.0%
2 5956
11.5%
3 5481
10.6%
4 5090
9.8%
5 4915
9.5%
6 4852
9.4%
0 4742
9.2%
7 4576
8.8%
9 4454
8.6%
8 4440
8.6%
Other Punctuation
ValueCountFrequency (%)
. 9991
99.9%
\ 9
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61724
> 99.9%
Latin 9
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9991
16.2%
1 7218
11.7%
2 5956
9.6%
3 5481
8.9%
4 5090
8.2%
5 4915
8.0%
6 4852
7.9%
0 4742
7.7%
7 4576
7.4%
9 4454
7.2%
Other values (2) 4449
7.2%
Latin
ValueCountFrequency (%)
N 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9991
16.2%
1 7218
11.7%
2 5956
9.6%
3 5481
8.9%
4 5090
8.2%
5 4915
8.0%
6 4852
7.9%
0 4742
7.7%
7 4576
7.4%
9 4454
7.2%
Other values (3) 4458
7.2%

탄소량
Text

Distinct3252
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:15:47.353999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.3371
Min length2

Characters and Unicode

Total characters43371
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

Unique1684 ?
Unique (%)16.8%

Sample

1st row27.53
2nd row7.73
3rd row141.66
4th row13.94
5th row1.90
ValueCountFrequency (%)
0.00 131
 
1.3%
0.23 30
 
0.3%
0.45 27
 
0.3%
0.68 26
 
0.3%
0.38 26
 
0.3%
0.22 25
 
0.2%
0.36 25
 
0.2%
0.35 25
 
0.2%
0.25 25
 
0.2%
0.56 25
 
0.2%
Other values (3242) 9635
96.4%
2024-05-18T09:15:48.319568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9991
23.0%
1 5258
12.1%
0 4424
10.2%
2 3997
9.2%
3 3407
 
7.9%
4 3148
 
7.3%
5 2853
 
6.6%
6 2774
 
6.4%
7 2642
 
6.1%
8 2447
 
5.6%
Other values (3) 2430
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33362
76.9%
Other Punctuation 10000
 
23.1%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5258
15.8%
0 4424
13.3%
2 3997
12.0%
3 3407
10.2%
4 3148
9.4%
5 2853
8.6%
6 2774
8.3%
7 2642
7.9%
8 2447
7.3%
9 2412
7.2%
Other Punctuation
ValueCountFrequency (%)
. 9991
99.9%
\ 9
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43362
> 99.9%
Latin 9
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9991
23.0%
1 5258
12.1%
0 4424
10.2%
2 3997
9.2%
3 3407
 
7.9%
4 3148
 
7.3%
5 2853
 
6.6%
6 2774
 
6.4%
7 2642
 
6.1%
8 2447
 
5.6%
Other values (2) 2421
 
5.6%
Latin
ValueCountFrequency (%)
N 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9991
23.0%
1 5258
12.1%
0 4424
10.2%
2 3997
9.2%
3 3407
 
7.9%
4 3148
 
7.3%
5 2853
 
6.6%
6 2774
 
6.4%
7 2642
 
6.1%
8 2447
 
5.6%
Other values (3) 2430
 
5.6%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9405
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56325.476
Minimum0
Maximum5827087.9
Zeros139
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:15:48.823184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1111.349
Q16209.13
median20316.16
Q362868.825
95-th percentile232588.06
Maximum5827087.9
Range5827087.9
Interquartile range (IQR)56659.695

Descriptive statistics

Standard deviation113321.58
Coefficient of variation (CV)2.0119063
Kurtosis697.68577
Mean56325.476
Median Absolute Deviation (MAD)17241.99
Skewness16.402863
Sum5.6325476 × 108
Variance1.284178 × 1010
MonotonicityNot monotonic
2024-05-18T09:15:49.260962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 139
 
1.4%
1070.0 6
 
0.1%
970.0 5
 
0.1%
1240.0 5
 
0.1%
1000.0 5
 
0.1%
790.0 5
 
0.1%
1950.0 5
 
0.1%
1160.0 5
 
0.1%
3540.0 5
 
0.1%
1750.0 5
 
0.1%
Other values (9395) 9815
98.2%
ValueCountFrequency (%)
0.0 139
1.4%
20.0 1
 
< 0.1%
30.0 1
 
< 0.1%
50.0 1
 
< 0.1%
60.0 1
 
< 0.1%
90.0 1
 
< 0.1%
110.0 1
 
< 0.1%
120.0 2
 
< 0.1%
131.02 1
 
< 0.1%
140.0 1
 
< 0.1%
ValueCountFrequency (%)
5827087.91 1
< 0.1%
1947369.61 1
< 0.1%
1618037.83 1
< 0.1%
1544427.5 1
< 0.1%
1483680.94 1
< 0.1%
1319513.48 1
< 0.1%
1085515.54 1
< 0.1%
1011478.44 1
< 0.1%
1000153.49 1
< 0.1%
962447.28 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2002
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.6831
Minimum0
Maximum59760
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:15:49.689770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q162
median191
Q3565
95-th percentile1999.05
Maximum59760
Range59760
Interquartile range (IQR)503

Descriptive statistics

Standard deviation1030.7346
Coefficient of variation (CV)2.0383015
Kurtosis1110.0483
Mean505.6831
Median Absolute Deviation (MAD)159
Skewness21.569527
Sum5056831
Variance1062413.9
MonotonicityNot monotonic
2024-05-18T09:15:50.141842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 70
 
0.7%
14 61
 
0.6%
9 58
 
0.6%
13 58
 
0.6%
18 57
 
0.6%
20 54
 
0.5%
26 51
 
0.5%
23 51
 
0.5%
15 51
 
0.5%
12 51
 
0.5%
Other values (1992) 9438
94.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 10
 
0.1%
2 20
 
0.2%
3 37
0.4%
4 48
0.5%
5 47
0.5%
6 70
0.7%
7 43
0.4%
8 49
0.5%
9 58
0.6%
ValueCountFrequency (%)
59760 1
< 0.1%
17858 1
< 0.1%
11805 1
< 0.1%
11385 1
< 0.1%
10256 1
< 0.1%
9718 1
< 0.1%
9290 1
< 0.1%
9266 1
< 0.1%
8261 1
< 0.1%
8134 1
< 0.1%

Interactions

2024-05-18T09:15:37.591721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:34.922479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:35.704568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:36.589796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:37.938010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:35.155328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:35.897933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:36.781010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:38.216820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:35.331860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:36.109709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:37.045707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:38.670109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:35.522133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:36.407680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:15:37.319009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T09:15:50.414941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0470.0000.0570.0640.0120.000
대여구분코드0.0471.0000.1480.3260.1340.0360.000
성별0.0000.1481.0000.0800.0390.0070.000
연령대코드0.0570.3260.0801.0000.1430.0520.058
이용건수0.0640.1340.0390.1431.0000.7340.782
이동거리(M)0.0120.0360.0070.0520.7341.0000.891
이용시간(분)0.0000.0000.0000.0580.7820.8911.000
2024-05-18T09:15:50.669951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0360.206
성별0.0361.0000.121
대여구분코드0.2060.1211.000
2024-05-18T09:15:51.061060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.046-0.045-0.0470.0190.0000.027
이용건수-0.0461.0000.9090.9210.0860.0270.077
이동거리(M)-0.0450.9091.0000.9690.0140.0050.032
이용시간(분)-0.0470.9210.9691.0000.0000.0000.026
대여구분코드0.0190.0860.0140.0001.0000.1210.206
성별0.0000.0270.0050.0000.1211.0000.036
연령대코드0.0270.0770.0320.0260.2060.0361.000

Missing values

2024-05-18T09:15:39.027666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T09:15:39.573946image/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)
809982022-0326582658.가락몰 업무동정기M기타913539.7627.53118685.82879
752212022-0323892389. 경기여자고등학교 후문 (삼성로3길 입구)정기\N40대13963.037.7333362.85244
63372022-03247247. 당산역 10번출구 앞정기F20대34314457.29141.66610893.396044
552282022-0316701670. 노원경찰서교차로정기\N50대341677.8113.9460108.88484
649492022-0320542054. 삼익아파트단체\N10대2260.101.908210.058
373272022-0311241124. 발산역 6번 출구 뒤정기\N60대131300.4011.5149610.4343
264432022-03790790.화곡고가 사거리정기F40대7356.423.5215168.64341
754792022-0324012401. 밀알학교 입구 (삼성서울병원 입구)일일(회원)\N50대113.430.12530.02
328132022-0310011001. 광진교 남단 사거리(천호공원 방면)일일(회원)\N10대5245.872.119116.7188
498142022-0314591459. 용마한신아파트사거리정기M10대8432.954.2218221.96331
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
634742022-0319881988. 고척LIGA아파트 앞정기<NA>40대3100.900.863678.9126
299292022-03909909. 백련산 힐스테이트 3차일일(회원)M20대4466.583.7115983.15115
53142022-03226226. 샛강역 1번출구 앞정기\N20대784150.7635.56153456.71323
475982022-0314021402. 금란주차장 앞정기\N20대1204706.3442.13181452.031921
145362022-03465465. 삼청공원 앞일일(회원)<NA>20대1138.001.154978.3251
196892022-03592592. 건국대학교 학생회관정기\N10대5100.890.843587.4935
666032022-0321072107. 도림천 신화교일일(회원)M30대418520.2867.74292091.191641
170662022-03530530. 청계벽산아파트 앞일일(회원)\N기타7894.708.2435516.79246
373642022-0311251125. 명덕고교입구(영종빌딩)일일(회원)\N기타7224.045.5423877.33206
217982022-03654654. 전농동 텃골공원정기M60대2496.444.1818020.081