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

DateTime1
Numeric4
Text3
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15248/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
이동거리(M) has 351 (3.5%) zerosZeros

Reproduction

Analysis started2024-03-13 13:00:20.733755
Analysis finished2024-03-13 13:00:25.100378
Duration4.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-02-01 00:00:00
Maximum2022-02-01 00:00:00
2024-03-13T22:00:25.167477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:25.294213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct2452
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.9908
Minimum3
Maximum4829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:25.442816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile215
Q1816
median1674
Q32910
95-th percentile4523
Maximum4829
Range4826
Interquartile range (IQR)2094

Descriptive statistics

Standard deviation1366.2319
Coefficient of variation (CV)0.69387417
Kurtosis-0.90028884
Mean1968.9908
Median Absolute Deviation (MAD)965
Skewness0.53226022
Sum19689908
Variance1866589.5
MonotonicityNot monotonic
2024-03-13T22:00:25.606260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2648 13
 
0.1%
816 12
 
0.1%
1004 12
 
0.1%
207 12
 
0.1%
206 12
 
0.1%
1070 11
 
0.1%
4107 11
 
0.1%
2621 11
 
0.1%
3907 11
 
0.1%
836 11
 
0.1%
Other values (2442) 9884
98.8%
ValueCountFrequency (%)
3 1
 
< 0.1%
102 2
 
< 0.1%
103 5
0.1%
104 7
0.1%
105 5
0.1%
106 6
0.1%
107 5
0.1%
108 4
< 0.1%
109 4
< 0.1%
111 4
< 0.1%
ValueCountFrequency (%)
4829 2
 
< 0.1%
4828 3
< 0.1%
4827 2
 
< 0.1%
4826 2
 
< 0.1%
4825 1
 
< 0.1%
4824 3
< 0.1%
4821 2
 
< 0.1%
4820 3
< 0.1%
4819 6
0.1%
4815 3
< 0.1%

대여소명
Text

Distinct2452
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:25.930146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.5114
Min length4

Characters and Unicode

Total characters155114
Distinct characters578
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)2.2%

Sample

1st row283. 아크로타워 스퀘어(영등포시장)
2nd row1259. 방이역 1번출구
3rd row1157. 강서구청
4th row780.신정교 하부
5th row3904. 테크노마트 앞
ValueCountFrequency (%)
앞 2635
 
9.0%
옆 397
 
1.4%
출구 394
 
1.3%
1번출구 269
 
0.9%
입구 234
 
0.8%
교차로 232
 
0.8%
사거리 223
 
0.8%
2번출구 200
 
0.7%
3번출구 199
 
0.7%
4번출구 185
 
0.6%
Other values (4885) 24298
83.0%
2024-03-13T22:00:26.588539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19454
 
12.5%
. 10033
 
6.5%
1 7869
 
5.1%
2 6144
 
4.0%
3 4898
 
3.2%
4 4672
 
3.0%
5 3656
 
2.4%
0 3531
 
2.3%
6 3405
 
2.2%
구 3271
 
2.1%
Other values (568) 88181
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79843
51.5%
Decimal Number 42404
27.3%
Space Separator 19454
 
12.5%
Other Punctuation 10127
 
6.5%
Uppercase Letter 1252
 
0.8%
Open Punctuation 878
 
0.6%
Close Punctuation 878
 
0.6%
Lowercase Letter 162
 
0.1%
Dash Punctuation 81
 
0.1%
Math Symbol 14
 
< 0.1%
Other values (3) 21
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3271
 
4.1%
앞 3095
 
3.9%
역 2504
 
3.1%
번 2209
 
2.8%
출 2147
 
2.7%
동 2130
 
2.7%
교 1679
 
2.1%
파 1429
 
1.8%
아 1392
 
1.7%
리 1322
 
1.7%
Other values (505) 58665
73.5%
Uppercase Letter
ValueCountFrequency (%)
K 167
13.3%
S 164
13.1%
T 118
9.4%
C 117
9.3%
A 82
 
6.5%
G 74
 
5.9%
B 74
 
5.9%
D 74
 
5.9%
P 60
 
4.8%
M 60
 
4.8%
Other values (13) 262
20.9%
Lowercase Letter
ValueCountFrequency (%)
e 65
40.1%
k 23
 
14.2%
s 22
 
13.6%
t 10
 
6.2%
l 7
 
4.3%
o 5
 
3.1%
m 5
 
3.1%
c 5
 
3.1%
n 4
 
2.5%
v 4
 
2.5%
Other values (6) 12
 
7.4%
Decimal Number
ValueCountFrequency (%)
1 7869
18.6%
2 6144
14.5%
3 4898
11.6%
4 4672
11.0%
5 3656
8.6%
0 3531
8.3%
6 3405
8.0%
7 3191
7.5%
8 2602
 
6.1%
9 2436
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 10033
99.1%
, 63
 
0.6%
& 15
 
0.1%
· 8
 
0.1%
? 8
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 8
57.1%
+ 6
42.9%
Space Separator
ValueCountFrequency (%)
19454
100.0%
Open Punctuation
ValueCountFrequency (%)
( 878
100.0%
Close Punctuation
ValueCountFrequency (%)
) 878
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Other Number
ValueCountFrequency (%)
① 8
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79848
51.5%
Common 73852
47.6%
Latin 1414
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3271
 
4.1%
앞 3095
 
3.9%
역 2504
 
3.1%
번 2209
 
2.8%
출 2147
 
2.7%
동 2130
 
2.7%
교 1679
 
2.1%
파 1429
 
1.8%
아 1392
 
1.7%
리 1322
 
1.7%
Other values (506) 58670
73.5%
Latin
ValueCountFrequency (%)
K 167
 
11.8%
S 164
 
11.6%
T 118
 
8.3%
C 117
 
8.3%
A 82
 
5.8%
G 74
 
5.2%
B 74
 
5.2%
D 74
 
5.2%
e 65
 
4.6%
P 60
 
4.2%
Other values (29) 419
29.6%
Common
ValueCountFrequency (%)
19454
26.3%
. 10033
13.6%
1 7869
10.7%
2 6144
 
8.3%
3 4898
 
6.6%
4 4672
 
6.3%
5 3656
 
5.0%
0 3531
 
4.8%
6 3405
 
4.6%
7 3191
 
4.3%
Other values (13) 6999
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79843
51.5%
ASCII 75250
48.5%
None 13
 
< 0.1%
Enclosed Alphanum 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19454
25.9%
. 10033
13.3%
1 7869
10.5%
2 6144
 
8.2%
3 4898
 
6.5%
4 4672
 
6.2%
5 3656
 
4.9%
0 3531
 
4.7%
6 3405
 
4.5%
7 3191
 
4.2%
Other values (50) 8397
11.2%
Hangul
ValueCountFrequency (%)
구 3271
 
4.1%
앞 3095
 
3.9%
역 2504
 
3.1%
번 2209
 
2.8%
출 2147
 
2.7%
동 2130
 
2.7%
교 1679
 
2.1%
파 1429
 
1.8%
아 1392
 
1.7%
리 1322
 
1.7%
Other values (505) 58665
73.5%
None
ValueCountFrequency (%)
· 8
61.5%
㈜ 5
38.5%
Enclosed Alphanum
ValueCountFrequency (%)
① 8
100.0%

대여구분코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5661 
일일(회원)
3775 
단체
 
339
일일(비회원)
 
224
10분이용권
 
1

Length

Max length7
Median length2
Mean length3.6224
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5661
56.6%
일일(회원) 3775
37.8%
단체 339
 
3.4%
일일(비회원) 224
 
2.2%
10분이용권 1
 
< 0.1%

Length

2024-03-13T22:00:26.881573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:27.078687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5661
56.6%
일일(회원 3775
37.8%
단체 339
 
3.4%
일일(비회원 224
 
2.2%
10분이용권 1
 
< 0.1%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3277 
\N
2926 
F
2775 
<NA>
1021 
m
 
1

Length

Max length4
Median length1
Mean length1.5989
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3277
32.8%
\N 2926
29.3%
F 2775
27.8%
<NA> 1021
 
10.2%
m 1
 
< 0.1%

Length

2024-03-13T22:00:27.300610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:27.495376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3278
32.8%
n 2926
29.3%
f 2775
27.8%
na 1021
 
10.2%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
2033 
30대
1695 
기타
1553 
40대
1530 
50대
1216 
Other values (3)
1973 

Length

Max length5
Median length3
Mean length2.8907
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70대이상
2nd row30대
3rd row기타
4th row10대
5th row70대이상

Common Values

ValueCountFrequency (%)
20대 2033
20.3%
30대 1695
17.0%
기타 1553
15.5%
40대 1530
15.3%
50대 1216
12.2%
10대 1052
10.5%
60대 691
 
6.9%
70대이상 230
 
2.3%

Length

2024-03-13T22:00:27.707746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:27.903739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 2033
20.3%
30대 1695
17.0%
기타 1553
15.5%
40대 1530
15.3%
50대 1216
12.2%
10대 1052
10.5%
60대 691
 
6.9%
70대이상 230
 
2.3%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.1321
Minimum1
Maximum375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:28.103256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q316
95-th percentile58
Maximum375
Range374
Interquartile range (IQR)14

Descriptive statistics

Standard deviation24.117658
Coefficient of variation (CV)1.706587
Kurtosis27.462955
Mean14.1321
Median Absolute Deviation (MAD)4
Skewness4.2577903
Sum141321
Variance581.66142
MonotonicityNot monotonic
2024-03-13T22:00:28.795712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1926
19.3%
2 1335
13.4%
3 834
 
8.3%
4 608
 
6.1%
5 490
 
4.9%
6 417
 
4.2%
7 305
 
3.0%
8 286
 
2.9%
9 255
 
2.5%
10 231
 
2.3%
Other values (174) 3313
33.1%
ValueCountFrequency (%)
1 1926
19.3%
2 1335
13.4%
3 834
8.3%
4 608
 
6.1%
5 490
 
4.9%
6 417
 
4.2%
7 305
 
3.0%
8 286
 
2.9%
9 255
 
2.5%
10 231
 
2.3%
ValueCountFrequency (%)
375 1
< 0.1%
303 1
< 0.1%
265 1
< 0.1%
256 1
< 0.1%
244 1
< 0.1%
235 1
< 0.1%
231 1
< 0.1%
230 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%

운동량
Text

Distinct9061
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:29.200411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.9182
Min length2

Characters and Unicode

Total characters59182
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

Unique8530 ?
Unique (%)85.3%

Sample

1st row37.05
2nd row1892.21
3rd row1070.49
4th row843.26
5th row88.33
ValueCountFrequency (%)
0.00 341
 
3.4%
n 13
 
0.1%
31.40 4
 
< 0.1%
111.97 4
 
< 0.1%
32.95 4
 
< 0.1%
57.93 4
 
< 0.1%
116.09 4
 
< 0.1%
43.24 4
 
< 0.1%
64.86 4
 
< 0.1%
54.65 3
 
< 0.1%
Other values (9051) 9615
96.2%
2024-03-13T22:00:29.781811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9987
16.9%
1 6888
11.6%
2 5752
9.7%
0 5150
8.7%
3 5006
8.5%
4 4733
8.0%
5 4540
7.7%
6 4507
7.6%
7 4324
7.3%
9 4135
7.0%
Other values (3) 4160
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49169
83.1%
Other Punctuation 10000
 
16.9%
Uppercase Letter 13
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6888
14.0%
2 5752
11.7%
0 5150
10.5%
3 5006
10.2%
4 4733
9.6%
5 4540
9.2%
6 4507
9.2%
7 4324
8.8%
9 4135
8.4%
8 4134
8.4%
Other Punctuation
ValueCountFrequency (%)
. 9987
99.9%
\ 13
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59169
> 99.9%
Latin 13
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9987
16.9%
1 6888
11.6%
2 5752
9.7%
0 5150
8.7%
3 5006
8.5%
4 4733
8.0%
5 4540
7.7%
6 4507
7.6%
7 4324
7.3%
9 4135
7.0%
Other values (2) 4147
7.0%
Latin
ValueCountFrequency (%)
N 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9987
16.9%
1 6888
11.6%
2 5752
9.7%
0 5150
8.7%
3 5006
8.5%
4 4733
8.0%
5 4540
7.7%
6 4507
7.6%
7 4324
7.3%
9 4135
7.0%
Other values (3) 4160
7.0%

탄소량
Text

Distinct2344
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:30.245737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1939
Min length2

Characters and Unicode

Total characters41939
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

Unique1073 ?
Unique (%)10.7%

Sample

1st row0.33
2nd row15.25
3rd row8.35
4th row8.36
5th row0.73
ValueCountFrequency (%)
0.00 340
 
3.4%
0.39 44
 
0.4%
0.28 37
 
0.4%
0.33 36
 
0.4%
0.42 36
 
0.4%
0.35 35
 
0.4%
0.27 35
 
0.4%
0.29 35
 
0.4%
0.55 35
 
0.4%
0.72 34
 
0.3%
Other values (2334) 9333
93.3%
2024-03-13T22:00:30.945190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9987
23.8%
0 5654
13.5%
1 4987
11.9%
2 3628
 
8.7%
3 3113
 
7.4%
4 2763
 
6.6%
5 2584
 
6.2%
6 2463
 
5.9%
7 2348
 
5.6%
8 2213
 
5.3%
Other values (3) 2199
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31926
76.1%
Other Punctuation 10000
 
23.8%
Uppercase Letter 13
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5654
17.7%
1 4987
15.6%
2 3628
11.4%
3 3113
9.8%
4 2763
8.7%
5 2584
8.1%
6 2463
7.7%
7 2348
7.4%
8 2213
 
6.9%
9 2173
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 9987
99.9%
\ 13
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41926
> 99.9%
Latin 13
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9987
23.8%
0 5654
13.5%
1 4987
11.9%
2 3628
 
8.7%
3 3113
 
7.4%
4 2763
 
6.6%
5 2584
 
6.2%
6 2463
 
5.9%
7 2348
 
5.6%
8 2213
 
5.3%
Other values (2) 2186
 
5.2%
Latin
ValueCountFrequency (%)
N 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9987
23.8%
0 5654
13.5%
1 4987
11.9%
2 3628
 
8.7%
3 3113
 
7.4%
4 2763
 
6.6%
5 2584
 
6.2%
6 2463
 
5.9%
7 2348
 
5.6%
8 2213
 
5.3%
Other values (3) 2199
 
5.2%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8856
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28726.329
Minimum0
Maximum1218581.6
Zeros351
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:31.158834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile550.095
Q13609.165
median11203.72
Q333088.103
95-th percentile114363.24
Maximum1218581.6
Range1218581.6
Interquartile range (IQR)29478.938

Descriptive statistics

Standard deviation49551.961
Coefficient of variation (CV)1.7249667
Kurtosis63.816966
Mean28726.329
Median Absolute Deviation (MAD)9293.72
Skewness5.5178405
Sum2.8726329 × 108
Variance2.4553969 × 109
MonotonicityNot monotonic
2024-03-13T22:00:31.330755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 351
 
3.5%
740.0 10
 
0.1%
1220.0 9
 
0.1%
1280.0 9
 
0.1%
1480.0 8
 
0.1%
2830.0 8
 
0.1%
1680.0 8
 
0.1%
1300.0 7
 
0.1%
1270.0 7
 
0.1%
3100.0 7
 
0.1%
Other values (8846) 9576
95.8%
ValueCountFrequency (%)
0.0 351
3.5%
0.1 1
 
< 0.1%
0.29 1
 
< 0.1%
30.0 1
 
< 0.1%
32.46 1
 
< 0.1%
50.0 2
 
< 0.1%
60.0 2
 
< 0.1%
70.0 2
 
< 0.1%
88.14 1
 
< 0.1%
88.17 1
 
< 0.1%
ValueCountFrequency (%)
1218581.58 1
< 0.1%
901836.73 1
< 0.1%
638444.2 1
< 0.1%
589943.14 1
< 0.1%
586340.76 1
< 0.1%
567288.48 1
< 0.1%
521551.74 1
< 0.1%
488513.37 1
< 0.1%
476001.81 1
< 0.1%
471502.01 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct1453
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.7518
Minimum0
Maximum11813
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:31.489825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q142
median121
Q3336
95-th percentile1185.1
Maximum11813
Range11813
Interquartile range (IQR)294

Descriptive statistics

Standard deviation513.9936
Coefficient of variation (CV)1.7204703
Kurtosis64.818182
Mean298.7518
Median Absolute Deviation (MAD)98
Skewness5.6792014
Sum2987518
Variance264189.43
MonotonicityNot monotonic
2024-03-13T22:00:31.674064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 93
 
0.9%
8 88
 
0.9%
12 87
 
0.9%
10 85
 
0.9%
5 83
 
0.8%
13 82
 
0.8%
14 78
 
0.8%
11 76
 
0.8%
19 75
 
0.8%
15 74
 
0.7%
Other values (1443) 9179
91.8%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 11
 
0.1%
2 36
 
0.4%
3 47
0.5%
4 66
0.7%
5 83
0.8%
6 66
0.7%
7 93
0.9%
8 88
0.9%
9 70
0.7%
ValueCountFrequency (%)
11813 1
< 0.1%
9566 1
< 0.1%
8254 1
< 0.1%
8090 1
< 0.1%
6939 1
< 0.1%
6516 1
< 0.1%
5908 1
< 0.1%
5033 1
< 0.1%
5031 1
< 0.1%
4907 1
< 0.1%

Interactions

2024-03-13T22:00:24.170897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:22.402251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:22.972867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:23.515416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:24.289783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:22.544096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:23.093426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:23.659096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:24.430996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:22.704705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:23.236254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:23.898829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:24.564487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:22.842658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:23.376256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:24.034837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:00:31.801352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0000.0000.0000.0730.0340.036
대여구분코드0.0001.0000.1440.3270.2980.1200.136
성별0.0000.1441.0000.0750.0740.0700.050
연령대코드0.0000.3270.0751.0000.1730.1540.110
이용건수0.0730.2980.0740.1731.0000.6130.659
이동거리(M)0.0340.1200.0700.1540.6131.0000.871
이용시간(분)0.0360.1360.0500.1100.6590.8711.000
2024-03-13T22:00:31.940142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.1170.207
성별0.1171.0000.034
연령대코드0.2070.0341.000
2024-03-13T22:00:32.071916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.070-0.075-0.0800.0000.0000.000
이용건수-0.0701.0000.8740.8900.1280.0450.083
이동거리(M)-0.0750.8741.0000.9440.0730.0320.052
이용시간(분)-0.0800.8900.9441.0000.0790.0320.054
대여구분코드0.0000.1280.0730.0791.0000.1170.207
성별0.0000.0450.0320.0320.1171.0000.034
연령대코드0.0000.0830.0520.0540.2070.0341.000

Missing values

2024-03-13T22:00:24.759308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:00:25.005264image/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)
67172022-02283283. 아크로타워 스퀘어(영등포시장)일일(회원)\N70대이상137.050.331439.512
366052022-0212591259. 방이역 1번출구정기M30대341892.2115.2565781.79731
329612022-0211571157. 강서구청정기M기타261070.498.3535947.64383
222772022-02780780.신정교 하부정기F10대6843.268.3636045.8226
828602022-0239043904. 테크노마트 앞정기M70대이상388.330.733146.2424
855352022-0240734073.방아골종합사회복지관정기F30대21462.754.1717977.01266
764042022-0235333533. 건대입구역 사거리(롯데백화점)정기\N30대1897184.1764.29277171.153836
475002022-0216841684. 태릉입구역 5번출구정기\N20대1636874.7558.97254303.432906
342322022-0211921192. 마곡수명산파크 209동 건너편정기\N30대451542.3013.4958298.08477
414172022-0214291429. 장안중학교정기M40대251235.7110.2244185.42733
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
948112022-0247924792. 퇴계로4가 교차로(중구청)일일(회원)\N30대6315.462.3410120.31128
734332022-0231293129.DMC래미안e편한세상203동옆정기M40대422728.1321.6293172.82680
512742022-0218501850. 코오롱테크노밸리일일(비회원)\N기타2813.387.3431600.0240
347202022-0212051205. 종합운동장역 4번출구일일(회원)\N40대3202.321.647048.4689
584432022-0221892189. 봉천역 4번출구단체M기타146.430.421803.6211
346512022-0212031203. 밀리아나2빌딩 앞정기<NA>30대9370.732.4110402.11134
346632022-0212031203. 밀리아나2빌딩 앞정기M30대784984.3036.99159425.081435
367852022-0212651265. 문정동 근린공원일일(회원)\N20대11517.584.7420483.12211
171282022-02609609. 제기2교일일(회원)\N30대496.150.773290.039
83232022-02336336. 티마크 호텔 앞정기\N20대522107.0518.5880139.381409