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-15249/A/1/datasetView.do

Alerts

이용건수 is highly overall correlated with 이동거리 and 1 other fieldsHigh correlation
이동거리 is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
이용시간 is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
대여일자 is highly imbalanced (75.7%)Imbalance
이동거리 has 137 (1.4%) zerosZeros

Reproduction

Analysis started2024-05-03 23:57:42.607246
Analysis finished2024-05-03 23:57:51.510158
Duration8.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Jan-20
9599 
Feb-20
 
401

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan-20
2nd rowJan-20
3rd rowJan-20
4th rowJan-20
5th rowFeb-20

Common Values

ValueCountFrequency (%)
Jan-20 9599
96.0%
Feb-20 401
 
4.0%

Length

2024-05-03T23:57:51.751014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:57:52.199528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jan-20 9599
96.0%
feb-20 401
 
4.0%

대여소번호
Real number (ℝ)

Distinct1528
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1231.4022
Minimum3
Maximum9993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:57:52.676252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile137.95
Q1501
median1167
Q31904
95-th percentile2510
Maximum9993
Range9990
Interquartile range (IQR)1403

Descriptive statistics

Standard deviation850.41367
Coefficient of variation (CV)0.69060593
Kurtosis2.8987086
Mean1231.4022
Median Absolute Deviation (MAD)702
Skewness0.84808696
Sum12314022
Variance723203.41
MonotonicityNot monotonic
2024-05-03T23:57:53.264796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146 23
 
0.2%
117 21
 
0.2%
115 21
 
0.2%
144 20
 
0.2%
110 20
 
0.2%
103 20
 
0.2%
154 20
 
0.2%
106 19
 
0.2%
157 18
 
0.2%
131 18
 
0.2%
Other values (1518) 9800
98.0%
ValueCountFrequency (%)
3 2
 
< 0.1%
101 9
0.1%
102 14
0.1%
103 20
0.2%
104 11
0.1%
105 16
0.2%
106 19
0.2%
107 18
0.2%
108 16
0.2%
109 15
0.1%
ValueCountFrequency (%)
9993 3
 
< 0.1%
3543 2
 
< 0.1%
3542 10
0.1%
3541 10
0.1%
3539 6
0.1%
3538 3
 
< 0.1%
3537 8
0.1%
3536 10
0.1%
3535 7
0.1%
3534 7
0.1%

대여소명
Text

Distinct1528
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:57:53.832395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.4647
Min length4

Characters and Unicode

Total characters154647
Distinct characters522
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

Unique21 ?
Unique (%)0.2%

Sample

1st row534. 금호사거리
2nd row290. 당산동 SK V1 빌딩
3rd row232. 양평우림 이비즈센타 앞
4th row2412. 일원1동 주민센터
5th row111. 상수역 2번출구 앞
ValueCountFrequency (%)
앞 2725
 
8.6%
옆 553
 
1.7%
출구 385
 
1.2%
1번출구 365
 
1.1%
2번출구 316
 
1.0%
사거리 277
 
0.9%
뒤 275
 
0.9%
3번출구 237
 
0.7%
4번출구 226
 
0.7%
입구 225
 
0.7%
Other values (3358) 26246
82.5%
2024-05-03T23:57:55.011266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22011
 
14.2%
. 10021
 
6.5%
1 9368
 
6.1%
2 6641
 
4.3%
3 4624
 
3.0%
구 3693
 
2.4%
5 3481
 
2.3%
0 3327
 
2.2%
앞 3238
 
2.1%
4 3170
 
2.0%
Other values (512) 85073
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79179
51.2%
Decimal Number 40436
26.1%
Space Separator 22011
 
14.2%
Other Punctuation 10077
 
6.5%
Uppercase Letter 1115
 
0.7%
Open Punctuation 819
 
0.5%
Close Punctuation 819
 
0.5%
Lowercase Letter 91
 
0.1%
Dash Punctuation 61
 
< 0.1%
Math Symbol 27
 
< 0.1%
Other values (2) 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3693
 
4.7%
앞 3238
 
4.1%
역 2906
 
3.7%
번 2585
 
3.3%
출 2557
 
3.2%
동 1946
 
2.5%
교 1572
 
2.0%
리 1434
 
1.8%
원 1225
 
1.5%
사 1153
 
1.5%
Other values (455) 56870
71.8%
Uppercase Letter
ValueCountFrequency (%)
S 139
12.5%
K 135
12.1%
C 111
10.0%
G 102
 
9.1%
L 102
 
9.1%
A 56
 
5.0%
M 54
 
4.8%
I 53
 
4.8%
T 52
 
4.7%
B 49
 
4.4%
Other values (14) 262
23.5%
Decimal Number
ValueCountFrequency (%)
1 9368
23.2%
2 6641
16.4%
3 4624
11.4%
5 3481
 
8.6%
0 3327
 
8.2%
4 3170
 
7.8%
6 3008
 
7.4%
7 2349
 
5.8%
9 2288
 
5.7%
8 2180
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
e 37
40.7%
k 12
 
13.2%
n 10
 
11.0%
t 9
 
9.9%
l 7
 
7.7%
y 5
 
5.5%
s 5
 
5.5%
m 2
 
2.2%
o 2
 
2.2%
c 2
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 10021
99.4%
, 40
 
0.4%
& 10
 
0.1%
? 4
 
< 0.1%
· 2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 17
63.0%
+ 10
37.0%
Space Separator
ValueCountFrequency (%)
22011
100.0%
Open Punctuation
ValueCountFrequency (%)
( 819
100.0%
Close Punctuation
ValueCountFrequency (%)
) 819
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79184
51.2%
Common 74257
48.0%
Latin 1206
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3693
 
4.7%
앞 3238
 
4.1%
역 2906
 
3.7%
번 2585
 
3.3%
출 2557
 
3.2%
동 1946
 
2.5%
교 1572
 
2.0%
리 1434
 
1.8%
원 1225
 
1.5%
사 1153
 
1.5%
Other values (456) 56875
71.8%
Latin
ValueCountFrequency (%)
S 139
 
11.5%
K 135
 
11.2%
C 111
 
9.2%
G 102
 
8.5%
L 102
 
8.5%
A 56
 
4.6%
M 54
 
4.5%
I 53
 
4.4%
T 52
 
4.3%
B 49
 
4.1%
Other values (24) 353
29.3%
Common
ValueCountFrequency (%)
22011
29.6%
. 10021
13.5%
1 9368
12.6%
2 6641
 
8.9%
3 4624
 
6.2%
5 3481
 
4.7%
0 3327
 
4.5%
4 3170
 
4.3%
6 3008
 
4.1%
7 2349
 
3.2%
Other values (12) 6257
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79179
51.2%
ASCII 75461
48.8%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22011
29.2%
. 10021
13.3%
1 9368
12.4%
2 6641
 
8.8%
3 4624
 
6.1%
5 3481
 
4.6%
0 3327
 
4.4%
4 3170
 
4.2%
6 3008
 
4.0%
7 2349
 
3.1%
Other values (45) 7461
 
9.9%
Hangul
ValueCountFrequency (%)
구 3693
 
4.7%
앞 3238
 
4.1%
역 2906
 
3.7%
번 2585
 
3.3%
출 2557
 
3.2%
동 1946
 
2.5%
교 1572
 
2.0%
리 1434
 
1.8%
원 1225
 
1.5%
사 1153
 
1.5%
Other values (455) 56870
71.8%
None
ValueCountFrequency (%)
㈜ 5
71.4%
· 2
 
28.6%

대여구분코드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5867 
일일(회원)
3423 
단체
 
407
일일(비회원)
 
303

Length

Max length7
Median length2
Mean length3.5207
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5867
58.7%
일일(회원) 3423
34.2%
단체 407
 
4.1%
일일(비회원) 303
 
3.0%

Length

2024-05-03T23:57:55.445730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:57:55.785161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5867
58.7%
일일(회원 3423
34.2%
단체 407
 
4.1%
일일(비회원 303
 
3.0%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3443 
M
2755 
F
2171 
<NA>
1628 
f
 
2

Length

Max length4
Median length2
Mean length1.8327
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3443
34.4%
M 2755
27.6%
F 2171
21.7%
<NA> 1628
16.3%
f 2
 
< 0.1%
m 1
 
< 0.1%

Length

2024-05-03T23:57:56.210304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:57:56.784512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3443
34.4%
m 2756
27.6%
f 2173
21.7%
na 1628
16.3%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2309 
AGE_003
1922 
AGE_004
1654 
AGE_005
1341 
AGE_001
1225 
Other values (3)
1549 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_003
2nd rowAGE_008
3rd rowAGE_001
4th rowAGE_001
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 2309
23.1%
AGE_003 1922
19.2%
AGE_004 1654
16.5%
AGE_005 1341
13.4%
AGE_001 1225
12.2%
AGE_006 720
 
7.2%
AGE_008 551
 
5.5%
AGE_007 278
 
2.8%

Length

2024-05-03T23:57:57.196571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:57:57.556149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2309
23.1%
age_003 1922
19.2%
age_004 1654
16.5%
age_005 1341
13.4%
age_001 1225
12.2%
age_006 720
 
7.2%
age_008 551
 
5.5%
age_007 278
 
2.8%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct205
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0825
Minimum1
Maximum451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:57:58.014632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q316
95-th percentile63.05
Maximum451
Range450
Interquartile range (IQR)14

Descriptive statistics

Standard deviation27.346676
Coefficient of variation (CV)1.8131395
Kurtosis36.54141
Mean15.0825
Median Absolute Deviation (MAD)4
Skewness4.8045159
Sum150825
Variance747.84068
MonotonicityNot monotonic
2024-05-03T23:57:58.517998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2002
20.0%
2 1352
13.5%
3 859
 
8.6%
4 608
 
6.1%
5 435
 
4.3%
6 381
 
3.8%
7 318
 
3.2%
8 270
 
2.7%
9 227
 
2.3%
10 217
 
2.2%
Other values (195) 3331
33.3%
ValueCountFrequency (%)
1 2002
20.0%
2 1352
13.5%
3 859
8.6%
4 608
 
6.1%
5 435
 
4.3%
6 381
 
3.8%
7 318
 
3.2%
8 270
 
2.7%
9 227
 
2.3%
10 217
 
2.2%
ValueCountFrequency (%)
451 1
< 0.1%
389 1
< 0.1%
356 1
< 0.1%
339 1
< 0.1%
326 1
< 0.1%
307 1
< 0.1%
304 1
< 0.1%
302 1
< 0.1%
297 1
< 0.1%
296 1
< 0.1%

운동량
Text

Distinct9261
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:57:59.655999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.013
Min length1

Characters and Unicode

Total characters60130
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

Unique8784 ?
Unique (%)87.8%

Sample

1st row1375.45
2nd row69.5
3rd row399.36
4th row247.21
5th row10009.4
ValueCountFrequency (%)
0 123
 
1.2%
n 14
 
0.1%
24.2 6
 
0.1%
70.01 5
 
< 0.1%
58.69 5
 
< 0.1%
45.56 5
 
< 0.1%
25.74 4
 
< 0.1%
92.92 4
 
< 0.1%
44.19 4
 
< 0.1%
20.2 4
 
< 0.1%
Other values (9251) 9826
98.3%
2024-05-03T23:58:01.190592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9759
16.2%
1 7187
12.0%
2 5970
9.9%
3 5341
8.9%
4 5088
8.5%
5 4845
8.1%
6 4784
8.0%
7 4709
7.8%
8 4618
7.7%
9 4429
7.4%
Other values (3) 3400
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50343
83.7%
Other Punctuation 9773
 
16.3%
Uppercase Letter 14
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7187
14.3%
2 5970
11.9%
3 5341
10.6%
4 5088
10.1%
5 4845
9.6%
6 4784
9.5%
7 4709
9.4%
8 4618
9.2%
9 4429
8.8%
0 3372
6.7%
Other Punctuation
ValueCountFrequency (%)
. 9759
99.9%
\ 14
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60116
> 99.9%
Latin 14
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9759
16.2%
1 7187
12.0%
2 5970
9.9%
3 5341
8.9%
4 5088
8.5%
5 4845
8.1%
6 4784
8.0%
7 4709
7.8%
8 4618
7.7%
9 4429
7.4%
Other values (2) 3386
 
5.6%
Latin
ValueCountFrequency (%)
N 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9759
16.2%
1 7187
12.0%
2 5970
9.9%
3 5341
8.9%
4 5088
8.5%
5 4845
8.1%
6 4784
8.0%
7 4709
7.8%
8 4618
7.7%
9 4429
7.4%
Other values (3) 3400
 
5.7%

탄소량
Text

Distinct3467
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:58:02.523182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1979
Min length1

Characters and Unicode

Total characters41979
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

Unique1997 ?
Unique (%)20.0%

Sample

1st row11.11
2nd row0.63
3rd row3.55
4th row2.13
5th row88.12
ValueCountFrequency (%)
0 124
 
1.2%
0.45 32
 
0.3%
0.52 32
 
0.3%
0.34 32
 
0.3%
0.35 31
 
0.3%
0.36 31
 
0.3%
0.55 31
 
0.3%
0.23 31
 
0.3%
0.42 30
 
0.3%
0.4 29
 
0.3%
Other values (3457) 9597
96.0%
2024-05-03T23:58:04.075843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9776
23.3%
1 5132
12.2%
2 4002
9.5%
0 3457
 
8.2%
3 3408
 
8.1%
4 3190
 
7.6%
5 2839
 
6.8%
6 2729
 
6.5%
7 2546
 
6.1%
8 2445
 
5.8%
Other values (3) 2455
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32175
76.6%
Other Punctuation 9790
 
23.3%
Uppercase Letter 14
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5132
16.0%
2 4002
12.4%
0 3457
10.7%
3 3408
10.6%
4 3190
9.9%
5 2839
8.8%
6 2729
8.5%
7 2546
7.9%
8 2445
7.6%
9 2427
7.5%
Other Punctuation
ValueCountFrequency (%)
. 9776
99.9%
\ 14
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41965
> 99.9%
Latin 14
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9776
23.3%
1 5132
12.2%
2 4002
9.5%
0 3457
 
8.2%
3 3408
 
8.1%
4 3190
 
7.6%
5 2839
 
6.8%
6 2729
 
6.5%
7 2546
 
6.1%
8 2445
 
5.8%
Other values (2) 2441
 
5.8%
Latin
ValueCountFrequency (%)
N 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9776
23.3%
1 5132
12.2%
2 4002
9.5%
0 3457
 
8.2%
3 3408
 
8.1%
4 3190
 
7.6%
5 2839
 
6.8%
6 2729
 
6.5%
7 2546
 
6.1%
8 2445
 
5.8%
Other values (3) 2455
 
5.8%

이동거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5869
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63867.52
Minimum0
Maximum1845380
Zeros137
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:58:04.563644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1130
Q15270
median17950
Q369565
95-th percentile276181.5
Maximum1845380
Range1845380
Interquartile range (IQR)64295

Descriptive statistics

Standard deviation118476.83
Coefficient of variation (CV)1.8550403
Kurtosis30.205889
Mean63867.52
Median Absolute Deviation (MAD)15620
Skewness4.3772176
Sum6.386752 × 108
Variance1.4036758 × 1010
MonotonicityNot monotonic
2024-05-03T23:58:05.151138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137
 
1.4%
1920 12
 
0.1%
2560 12
 
0.1%
1540 12
 
0.1%
1480 11
 
0.1%
2790 11
 
0.1%
1220 11
 
0.1%
1200 11
 
0.1%
4510 11
 
0.1%
2230 10
 
0.1%
Other values (5859) 9762
97.6%
ValueCountFrequency (%)
0 137
1.4%
20 1
 
< 0.1%
30 1
 
< 0.1%
70 1
 
< 0.1%
90 2
 
< 0.1%
100 1
 
< 0.1%
110 1
 
< 0.1%
120 1
 
< 0.1%
190 1
 
< 0.1%
200 2
 
< 0.1%
ValueCountFrequency (%)
1845380 1
< 0.1%
1655740 1
< 0.1%
1542670 1
< 0.1%
1433510 1
< 0.1%
1323750 1
< 0.1%
1294210 1
< 0.1%
1213890 1
< 0.1%
1209650 1
< 0.1%
1194180 1
< 0.1%
1121210 1
< 0.1%

이용시간
Real number (ℝ)

HIGH CORRELATION 

Distinct1517
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314.0679
Minimum0
Maximum7312
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:58:05.628385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q141
median120
Q3348.25
95-th percentile1263.05
Maximum7312
Range7312
Interquartile range (IQR)307.25

Descriptive statistics

Standard deviation530.56471
Coefficient of variation (CV)1.6893312
Kurtosis24.065831
Mean314.0679
Median Absolute Deviation (MAD)98
Skewness4.0745206
Sum3140679
Variance281498.91
MonotonicityNot monotonic
2024-05-03T23:58:06.065912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 87
 
0.9%
8 86
 
0.9%
12 85
 
0.9%
15 84
 
0.8%
9 83
 
0.8%
13 82
 
0.8%
14 75
 
0.8%
4 74
 
0.7%
11 74
 
0.7%
17 74
 
0.7%
Other values (1507) 9196
92.0%
ValueCountFrequency (%)
0 9
 
0.1%
1 8
 
0.1%
2 30
 
0.3%
3 49
0.5%
4 74
0.7%
5 68
0.7%
6 87
0.9%
7 68
0.7%
8 86
0.9%
9 83
0.8%
ValueCountFrequency (%)
7312 1
< 0.1%
6239 1
< 0.1%
5602 1
< 0.1%
5502 1
< 0.1%
5219 1
< 0.1%
5191 1
< 0.1%
4992 1
< 0.1%
4964 1
< 0.1%
4872 1
< 0.1%
4803 1
< 0.1%

Interactions

2024-05-03T23:57:48.711524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:45.085423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:46.204671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:47.299148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:49.106026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:45.364907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:46.479088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:47.612175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:49.575430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:45.624540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:46.730641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:47.963050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:49.971902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:45.916800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:47.027688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:57:48.349290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T23:58:06.365052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리이용시간
대여일자1.0000.1900.0000.0000.0000.0000.0400.095
대여소번호0.1901.0000.0000.0330.0130.0610.0450.047
대여구분코드0.0000.0001.0000.1420.7880.2190.1860.193
성별0.0000.0330.1421.0000.2420.1560.1210.142
연령대코드0.0000.0130.7880.2421.0000.1790.1600.166
이용건수0.0000.0610.2190.1560.1791.0000.9140.866
이동거리0.0400.0450.1860.1210.1600.9141.0000.910
이용시간0.0950.0470.1930.1420.1660.8660.9101.000
2024-05-03T23:58:06.665175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드대여구분코드성별대여일자
연령대코드1.0000.4540.1510.000
대여구분코드0.4541.0000.1160.000
성별0.1510.1161.0000.000
대여일자0.0000.0000.0001.000
2024-05-03T23:58:06.941717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리이용시간대여일자대여구분코드성별연령대코드
대여소번호1.000-0.046-0.039-0.0480.2330.0000.0120.008
이용건수-0.0461.0000.8610.8870.0000.1300.0650.085
이동거리-0.0390.8611.0000.9040.0310.1120.0510.077
이용시간-0.0480.8870.9041.0000.0730.1160.0590.080
대여일자0.2330.0000.0310.0731.0000.0000.0000.000
대여구분코드0.0000.1300.1120.1160.0001.0000.1160.454
성별0.0120.0650.0510.0590.0000.1161.0000.151
연령대코드0.0080.0850.0770.0800.0000.4540.1511.000

Missing values

2024-05-03T23:57:50.678087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T23:57:51.210144image/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

대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리이용시간
12774Jan-20534534. 금호사거리정기\NAGE_003121375.4511.1147940242
6643Jan-20290290. 당산동 SK V1 빌딩정기\NAGE_008269.50.63270015
4565Jan-20232232. 양평우림 이비즈센타 앞단체MAGE_0012399.363.5515280101
50529Jan-2024122412. 일원1동 주민센터정기\NAGE_0014247.212.13917051
53869Feb-20111111. 상수역 2번출구 앞일일(회원)\NAGE_0024410009.488.123796801874
15800Jan-20636636. 세종대왕기념관 교차로정기FAGE_0043117.631.06457050
11023Jan-20435435. SK 남산빌딩일일(회원)\NAGE_0031145.531.22525022
8233Jan-20343343. 예일빌딩(율곡로) 앞정기<NA>AGE_0042368.372.991292082
49033Jan-2023512351. 청소년수련관(수영장)앞정기MAGE_0024398.963.011296085
23805Jan-2011031103. 방화역 4번출구앞일일(비회원)\NAGE_008162.550.56243082
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리이용시간
40575Jan-2019521952. 천왕연지타운2단지 앞일일(회원)FAGE_0026625.596.5828340247
45470Jan-2022202220. 반포본동 주민센터 앞정기\NAGE_0046910444.7587.173760201533
8828Jan-20363363. 신설동역 11번출구 뒤정기<NA>AGE_002593323.5627.55118810732
43925Jan-2021352135. 신림역 5번출구정기<NA>AGE_005100014
7246Jan-20311311. 서울광장 옆정기FAGE_0012223.692.02869072
54889Feb-20140140. 이화여대 후문일일(회원)<NA>AGE_0022325.23.3514460181
55098Feb-20146146. 마포역 2번출구 뒤일일(회원)FAGE_002294810.7645.261950401401
17813Jan-20744744. 신목동역 2번 출구정기\NAGE_004616331.4357.53248040682
53843Feb-20110110. 사천교정기\NAGE_005321308.1212.4353570557
37091Jan-2016891689. 마들역 3번출구정기\NAGE_005495618.3349.36212690872