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

대여구분코드 is highly imbalanced (56.4%)Imbalance
이동거리(M) has 3588 (35.9%) zerosZeros

Reproduction

Analysis started2024-05-18 05:03:55.309779
Analysis finished2024-05-18 05:04:05.143273
Duration9.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-01-01
6624 
2021-01-02
3376 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-02
2nd row2021-01-01
3rd row2021-01-01
4th row2021-01-01
5th row2021-01-01

Common Values

ValueCountFrequency (%)
2021-01-01 6624
66.2%
2021-01-02 3376
33.8%

Length

2024-05-18T14:04:05.344080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:04:05.710287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-01 6624
66.2%
2021-01-02 3376
33.8%

대여소번호
Real number (ℝ)

Distinct1893
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1186.7545
Minimum101
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:04:06.200761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile171
Q1515
median1009
Q31670
95-th percentile2910.05
Maximum9999
Range9898
Interquartile range (IQR)1155

Descriptive statistics

Standard deviation863.40886
Coefficient of variation (CV)0.72753789
Kurtosis1.1701305
Mean1186.7545
Median Absolute Deviation (MAD)548
Skewness1.0182237
Sum11867545
Variance745474.87
MonotonicityNot monotonic
2024-05-18T14:04:06.750897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152 29
 
0.3%
565 26
 
0.3%
502 26
 
0.3%
284 25
 
0.2%
1210 24
 
0.2%
419 23
 
0.2%
907 23
 
0.2%
186 23
 
0.2%
853 23
 
0.2%
228 22
 
0.2%
Other values (1883) 9756
97.6%
ValueCountFrequency (%)
101 9
0.1%
102 16
0.2%
103 11
0.1%
104 5
 
0.1%
105 10
0.1%
106 16
0.2%
107 6
 
0.1%
108 7
0.1%
109 8
0.1%
111 8
0.1%
ValueCountFrequency (%)
9999 1
 
< 0.1%
3588 1
 
< 0.1%
3587 3
< 0.1%
3586 4
< 0.1%
3582 3
< 0.1%
3581 1
 
< 0.1%
3579 5
0.1%
3578 3
< 0.1%
3575 2
 
< 0.1%
3573 5
0.1%

대여소
Text

Distinct1893
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:04:07.396739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length15.2984
Min length6

Characters and Unicode

Total characters152984
Distinct characters551
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

Unique243 ?
Unique (%)2.4%

Sample

1st row416. 상암월드컵파크 1단지 교차로
2nd row1870.독산3동주민센터 앞
3rd row1670. 노원경찰서교차로
4th row1616. 하계2동 공항버스정류장 옆
5th row311. 서울광장 옆
ValueCountFrequency (%)
앞 2734
 
9.2%
옆 503
 
1.7%
출구 421
 
1.4%
1번출구 384
 
1.3%
3번출구 284
 
1.0%
뒤 239
 
0.8%
사거리 237
 
0.8%
2번출구 232
 
0.8%
4번출구 208
 
0.7%
교차로 207
 
0.7%
Other values (3765) 24412
81.8%
2024-05-18T14:04:08.728226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20055
 
13.1%
. 10018
 
6.5%
1 8732
 
5.7%
2 6320
 
4.1%
3 4141
 
2.7%
구 3507
 
2.3%
5 3446
 
2.3%
0 3374
 
2.2%
앞 3307
 
2.2%
4 3256
 
2.1%
Other values (541) 86828
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79085
51.7%
Decimal Number 40319
26.4%
Space Separator 20055
 
13.1%
Other Punctuation 10094
 
6.6%
Uppercase Letter 1416
 
0.9%
Open Punctuation 904
 
0.6%
Close Punctuation 904
 
0.6%
Lowercase Letter 91
 
0.1%
Dash Punctuation 90
 
0.1%
Math Symbol 17
 
< 0.1%
Other values (2) 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3507
 
4.4%
앞 3307
 
4.2%
역 2841
 
3.6%
번 2536
 
3.2%
출 2484
 
3.1%
동 2097
 
2.7%
교 1636
 
2.1%
리 1370
 
1.7%
파 1293
 
1.6%
아 1260
 
1.6%
Other values (484) 56754
71.8%
Uppercase Letter
ValueCountFrequency (%)
S 183
12.9%
C 149
10.5%
K 148
10.5%
G 109
 
7.7%
B 106
 
7.5%
T 103
 
7.3%
L 99
 
7.0%
I 99
 
7.0%
A 78
 
5.5%
M 68
 
4.8%
Other values (14) 274
19.4%
Lowercase Letter
ValueCountFrequency (%)
e 36
39.6%
l 9
 
9.9%
n 8
 
8.8%
t 7
 
7.7%
k 6
 
6.6%
c 5
 
5.5%
o 5
 
5.5%
m 5
 
5.5%
s 4
 
4.4%
y 4
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 8732
21.7%
2 6320
15.7%
3 4141
10.3%
5 3446
 
8.5%
0 3374
 
8.4%
4 3256
 
8.1%
6 3206
 
8.0%
7 3014
 
7.5%
9 2449
 
6.1%
8 2381
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 10018
99.2%
, 65
 
0.6%
& 10
 
0.1%
? 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 15
88.2%
+ 2
 
11.8%
Space Separator
ValueCountFrequency (%)
20055
100.0%
Open Punctuation
ValueCountFrequency (%)
( 904
100.0%
Close Punctuation
ValueCountFrequency (%)
) 904
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 90
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79086
51.7%
Common 72391
47.3%
Latin 1507
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3507
 
4.4%
앞 3307
 
4.2%
역 2841
 
3.6%
번 2536
 
3.2%
출 2484
 
3.1%
동 2097
 
2.7%
교 1636
 
2.1%
리 1370
 
1.7%
파 1293
 
1.6%
아 1260
 
1.6%
Other values (485) 56755
71.8%
Latin
ValueCountFrequency (%)
S 183
12.1%
C 149
9.9%
K 148
9.8%
G 109
 
7.2%
B 106
 
7.0%
T 103
 
6.8%
L 99
 
6.6%
I 99
 
6.6%
A 78
 
5.2%
M 68
 
4.5%
Other values (25) 365
24.2%
Common
ValueCountFrequency (%)
20055
27.7%
. 10018
13.8%
1 8732
12.1%
2 6320
 
8.7%
3 4141
 
5.7%
5 3446
 
4.8%
0 3374
 
4.7%
4 3256
 
4.5%
6 3206
 
4.4%
7 3014
 
4.2%
Other values (11) 6829
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79085
51.7%
ASCII 73898
48.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20055
27.1%
. 10018
13.6%
1 8732
11.8%
2 6320
 
8.6%
3 4141
 
5.6%
5 3446
 
4.7%
0 3374
 
4.6%
4 3256
 
4.4%
6 3206
 
4.3%
7 3014
 
4.1%
Other values (46) 8336
11.3%
Hangul
ValueCountFrequency (%)
구 3507
 
4.4%
앞 3307
 
4.2%
역 2841
 
3.6%
번 2536
 
3.2%
출 2484
 
3.1%
동 2097
 
2.7%
교 1636
 
2.1%
리 1370
 
1.7%
파 1293
 
1.6%
아 1260
 
1.6%
Other values (484) 56754
71.8%
None
ValueCountFrequency (%)
㈜ 1
100.0%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
7014 
일일(회원)
2784 
단체
 
103
일일(비회원)
 
86
BIL_021
 
13

Length

Max length7
Median length2
Mean length3.1631
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 7014
70.1%
일일(회원) 2784
 
27.8%
단체 103
 
1.0%
일일(비회원) 86
 
0.9%
BIL_021 13
 
0.1%

Length

2024-05-18T14:04:09.268887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:04:09.608345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 7014
70.1%
일일(회원 2784
 
27.8%
단체 103
 
1.0%
일일(비회원 86
 
0.9%
bil_021 13
 
0.1%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3758 
M
3467 
F
2165 
<NA>
608 
f
 
1

Length

Max length4
Median length1
Mean length1.5582
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3758
37.6%
M 3467
34.7%
F 2165
21.6%
<NA> 608
 
6.1%
f 1
 
< 0.1%
m 1
 
< 0.1%

Length

2024-05-18T14:04:10.101865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:04:10.523550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3758
37.6%
m 3468
34.7%
f 2166
21.7%
na 608
 
6.1%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
3270 
AGE_003
2446 
AGE_004
1828 
AGE_005
1168 
AGE_001
593 
Other values (3)
695 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_003
2nd rowAGE_007
3rd rowAGE_003
4th rowAGE_006
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 3270
32.7%
AGE_003 2446
24.5%
AGE_004 1828
18.3%
AGE_005 1168
 
11.7%
AGE_001 593
 
5.9%
AGE_006 396
 
4.0%
AGE_008 241
 
2.4%
AGE_007 58
 
0.6%

Length

2024-05-18T14:04:11.016261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:04:11.352332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 3270
32.7%
age_003 2446
24.5%
age_004 1828
18.3%
age_005 1168
 
11.7%
age_001 593
 
5.9%
age_006 396
 
4.0%
age_008 241
 
2.4%
age_007 58
 
0.6%

이용건수
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5058
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:04:11.706498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9879595
Coefficient of variation (CV)0.65610273
Kurtosis14.661163
Mean1.5058
Median Absolute Deviation (MAD)0
Skewness3.1126538
Sum15058
Variance0.97606397
MonotonicityNot monotonic
2024-05-18T14:04:12.220859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 6920
69.2%
2 1957
 
19.6%
3 676
 
6.8%
4 236
 
2.4%
5 113
 
1.1%
6 46
 
0.5%
7 28
 
0.3%
8 12
 
0.1%
9 7
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
1 6920
69.2%
2 1957
 
19.6%
3 676
 
6.8%
4 236
 
2.4%
5 113
 
1.1%
6 46
 
0.5%
7 28
 
0.3%
8 12
 
0.1%
9 7
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
13 2
 
< 0.1%
10 3
 
< 0.1%
9 7
 
0.1%
8 12
 
0.1%
7 28
 
0.3%
6 46
 
0.5%
5 113
 
1.1%
4 236
 
2.4%
3 676
 
6.8%
2 1957
19.6%

운동량
Text

Distinct5478
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:04:12.785902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.8588
Min length2

Characters and Unicode

Total characters48588
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

Unique4699 ?
Unique (%)47.0%

Sample

1st row33.75
2nd row0.00
3rd row0.00
4th row17.94
5th row58.34
ValueCountFrequency (%)
0.00 3543
35.4%
n 49
 
0.5%
5.92 5
 
< 0.1%
18.58 5
 
< 0.1%
2.27 5
 
< 0.1%
13.72 5
 
< 0.1%
25.08 5
 
< 0.1%
56.89 4
 
< 0.1%
17.71 4
 
< 0.1%
24.71 4
 
< 0.1%
Other values (5468) 6371
63.7%
2024-05-18T14:04:13.832142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12850
26.4%
. 9951
20.5%
1 4092
 
8.4%
2 3449
 
7.1%
3 3021
 
6.2%
4 2754
 
5.7%
5 2617
 
5.4%
6 2543
 
5.2%
7 2448
 
5.0%
8 2425
 
5.0%
Other values (3) 2438
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38539
79.3%
Other Punctuation 10000
 
20.6%
Uppercase Letter 49
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12850
33.3%
1 4092
 
10.6%
2 3449
 
8.9%
3 3021
 
7.8%
4 2754
 
7.1%
5 2617
 
6.8%
6 2543
 
6.6%
7 2448
 
6.4%
8 2425
 
6.3%
9 2340
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 9951
99.5%
\ 49
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48539
99.9%
Latin 49
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12850
26.5%
. 9951
20.5%
1 4092
 
8.4%
2 3449
 
7.1%
3 3021
 
6.2%
4 2754
 
5.7%
5 2617
 
5.4%
6 2543
 
5.2%
7 2448
 
5.0%
8 2425
 
5.0%
Other values (2) 2389
 
4.9%
Latin
ValueCountFrequency (%)
N 49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12850
26.4%
. 9951
20.5%
1 4092
 
8.4%
2 3449
 
7.1%
3 3021
 
6.2%
4 2754
 
5.7%
5 2617
 
5.4%
6 2543
 
5.2%
7 2448
 
5.0%
8 2425
 
5.0%
Other values (3) 2438
 
5.0%

탄소량
Text

Distinct563
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:04:14.573387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9918
Min length2

Characters and Unicode

Total characters39918
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

Unique156 ?
Unique (%)1.6%

Sample

1st row0.34
2nd row0.00
3rd row0.00
4th row0.13
5th row0.53
ValueCountFrequency (%)
0.00 3545
35.4%
0.19 87
 
0.9%
0.29 85
 
0.9%
0.17 76
 
0.8%
0.15 76
 
0.8%
0.16 76
 
0.8%
0.26 75
 
0.8%
0.30 75
 
0.8%
0.21 74
 
0.7%
0.18 73
 
0.7%
Other values (553) 5758
57.6%
2024-05-18T14:04:15.691119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16048
40.2%
. 9951
24.9%
1 2820
 
7.1%
2 2127
 
5.3%
3 1726
 
4.3%
4 1451
 
3.6%
5 1316
 
3.3%
6 1213
 
3.0%
7 1077
 
2.7%
9 1047
 
2.6%
Other values (3) 1142
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29869
74.8%
Other Punctuation 10000
 
25.1%
Uppercase Letter 49
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16048
53.7%
1 2820
 
9.4%
2 2127
 
7.1%
3 1726
 
5.8%
4 1451
 
4.9%
5 1316
 
4.4%
6 1213
 
4.1%
7 1077
 
3.6%
9 1047
 
3.5%
8 1044
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 9951
99.5%
\ 49
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39869
99.9%
Latin 49
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16048
40.3%
. 9951
25.0%
1 2820
 
7.1%
2 2127
 
5.3%
3 1726
 
4.3%
4 1451
 
3.6%
5 1316
 
3.3%
6 1213
 
3.0%
7 1077
 
2.7%
9 1047
 
2.6%
Other values (2) 1093
 
2.7%
Latin
ValueCountFrequency (%)
N 49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16048
40.2%
. 9951
24.9%
1 2820
 
7.1%
2 2127
 
5.3%
3 1726
 
4.3%
4 1451
 
3.6%
5 1316
 
3.3%
6 1213
 
3.0%
7 1077
 
2.7%
9 1047
 
2.6%
Other values (3) 1142
 
2.9%

이동거리(M)
Real number (ℝ)

ZEROS 

Distinct6169
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2982.7814
Minimum0
Maximum91290
Zeros3588
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:04:16.025871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1189.245
Q33697.38
95-th percentile12510.691
Maximum91290
Range91290
Interquartile range (IQR)3697.38

Descriptive statistics

Standard deviation5122.4132
Coefficient of variation (CV)1.7173277
Kurtosis32.861025
Mean2982.7814
Median Absolute Deviation (MAD)1189.245
Skewness4.2358443
Sum29827814
Variance26239117
MonotonicityNot monotonic
2024-05-18T14:04:16.370747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3588
35.9%
111.2 14
 
0.1%
1660.0 6
 
0.1%
333.59 6
 
0.1%
1260.0 6
 
0.1%
1090.0 5
 
0.1%
810.0 4
 
< 0.1%
630.0 4
 
< 0.1%
1500.0 4
 
< 0.1%
1280.0 4
 
< 0.1%
Other values (6159) 6359
63.6%
ValueCountFrequency (%)
0.0 3588
35.9%
0.1 4
 
< 0.1%
0.26 2
 
< 0.1%
40.0 1
 
< 0.1%
70.0 1
 
< 0.1%
88.07 1
 
< 0.1%
88.1 1
 
< 0.1%
88.11 1
 
< 0.1%
88.13 2
 
< 0.1%
88.15 1
 
< 0.1%
ValueCountFrequency (%)
91290.0 1
< 0.1%
81987.37 1
< 0.1%
62318.63 1
< 0.1%
60870.0 1
< 0.1%
53585.76 1
< 0.1%
52797.53 1
< 0.1%
52449.85 1
< 0.1%
51254.72 1
< 0.1%
49363.75 1
< 0.1%
49340.0 1
< 0.1%

이용시간(분)
Real number (ℝ)

Distinct312
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.3076
Minimum0
Maximum1117
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:04:16.772817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q112
median29
Q362
95-th percentile138
Maximum1117
Range1117
Interquartile range (IQR)50

Descriptive statistics

Standard deviation54.546506
Coefficient of variation (CV)1.1779169
Kurtosis48.002603
Mean46.3076
Median Absolute Deviation (MAD)20
Skewness4.5198586
Sum463076
Variance2975.3213
MonotonicityNot monotonic
2024-05-18T14:04:17.108519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 300
 
3.0%
6 287
 
2.9%
8 278
 
2.8%
10 270
 
2.7%
9 260
 
2.6%
7 228
 
2.3%
12 222
 
2.2%
13 220
 
2.2%
4 215
 
2.1%
15 209
 
2.1%
Other values (302) 7511
75.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 26
 
0.3%
2 124
1.2%
3 189
1.9%
4 215
2.1%
5 300
3.0%
6 287
2.9%
7 228
2.3%
8 278
2.8%
9 260
2.6%
ValueCountFrequency (%)
1117 1
< 0.1%
986 1
< 0.1%
919 1
< 0.1%
870 1
< 0.1%
740 1
< 0.1%
639 1
< 0.1%
582 1
< 0.1%
568 1
< 0.1%
525 1
< 0.1%
516 1
< 0.1%

Interactions

2024-05-18T14:04:02.787897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:58.338740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:00.058812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:01.291609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:03.187784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:58.697084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:00.370070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:01.771242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:03.525282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:59.299360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:00.675669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:02.144679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:03.808259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:03:59.660112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:01.014534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:02.456124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:04:17.279188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여일자1.0000.3400.0240.0000.0550.0290.0400.030
대여소번호0.3401.0000.0240.0000.0490.0000.0130.026
대여구분코드0.0240.0241.0000.1650.4840.0800.1260.199
성별0.0000.0000.1651.0000.1670.0950.0190.000
연령대코드0.0550.0490.4840.1671.0000.1220.0000.000
이용건수0.0290.0000.0800.0950.1221.0000.3170.420
이동거리(M)0.0400.0130.1260.0190.0000.3171.0000.572
이용시간(분)0.0300.0260.1990.0000.0000.4200.5721.000
2024-05-18T14:04:17.617552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여일자대여구분코드
연령대코드1.0000.1030.0410.323
성별0.1031.0000.0000.062
대여일자0.0410.0001.0000.030
대여구분코드0.3230.0620.0301.000
2024-05-18T14:04:17.884066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여일자대여구분코드성별연령대코드
대여소번호1.000-0.036-0.008-0.0220.4140.0090.0000.030
이용건수-0.0361.0000.3400.4720.0270.0460.0550.060
이동거리(M)-0.0080.3401.0000.4190.0400.0730.0110.000
이용시간(분)-0.0220.4720.4191.0000.0230.0840.0000.000
대여일자0.4140.0270.0400.0231.0000.0300.0000.041
대여구분코드0.0090.0460.0730.0840.0301.0000.0620.323
성별0.0000.0550.0110.0000.0000.0621.0000.103
연령대코드0.0300.0600.0000.0000.0410.3230.1031.000

Missing values

2024-05-18T14:04:04.201767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:04:04.861459image/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)
171242021-01-02416416. 상암월드컵파크 1단지 교차로정기<NA>AGE_003133.750.341444.3611
101582021-01-0118701870.독산3동주민센터 앞정기MAGE_00710.000.000.028
93202021-01-0116701670. 노원경찰서교차로정기\NAGE_00310.000.000.055
89282021-01-0116161616. 하계2동 공항버스정류장 옆정기\NAGE_006117.940.13566.442
17542021-01-01311311. 서울광장 옆정기FAGE_002158.340.532266.619
8532021-01-01206206. KBS 앞정기FAGE_0042311.532.7811982.3986
40362021-01-01732732. 신월동 이마트일일(회원)\NAGE_003114.570.15645.665
22132021-01-01408408. LG CNS앞정기MAGE_0021155.120.873730.5555
145732021-01-0135363536. 중앙농협(자양동)정기MAGE_005178.840.552370.072
182842021-01-02613613. 신설동역 10번출구 앞일일(회원)MAGE_00220.000.000.032
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
54792021-01-01979979.원풍빌라앞정기FAGE_003248.420.441880.988
67322021-01-0111821182. KBS 스포츠월드정기MAGE_00119.840.07310.5416
178722021-01-02552552. 대림아크로리버 앞정기\NAGE_002139.900.431831.9116
150002021-01-02123123. 문화촌 공원정기MAGE_007198.810.964158.8419
202572021-01-02937937. 상림마을 롯데캐슬2단지 옆정기FAGE_0041200.381.687228.7591
7362021-01-01186186. 월드컵공원일일(회원)MAGE_0044455.623.9416989.67186
64322021-01-0111461146. 곰달래사거리정기\NAGE_003247.580.462002.45103
169992021-01-02388388. 동성중학교 앞정기\NAGE_00310.000.000.067
88652021-01-0116051605. 헬스케어정기FAGE_002165.300.702998.3619
200342021-01-02907907. CJ 드림시티일일(회원)\NAGE_00110.000.000.015