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

대여일자 has constant value ""Constant
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
대여소번호 is highly skewed (γ1 = 42.23398209)Skewed
이동거리 has 144 (1.4%) zerosZeros

Reproduction

Analysis started2024-05-03 23:58:11.770484
Analysis finished2024-05-03 23:58:22.158354
Duration10.39 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
Dec-19
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDec-19
2nd rowDec-19
3rd rowDec-19
4th rowDec-19
5th rowDec-19

Common Values

ValueCountFrequency (%)
Dec-19 10000
100.0%

Length

2024-05-03T23:58:22.366400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:58:22.910687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
dec-19 10000
100.0%

대여소번호
Real number (ℝ)

SKEWED 

Distinct1524
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1280.3324
Minimum101
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:58:23.460956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile169
Q1535
median1181
Q31907
95-th percentile2511
Maximum99999
Range99898
Interquartile range (IQR)1372

Descriptive statistics

Standard deviation1898.4203
Coefficient of variation (CV)1.4827558
Kurtosis2192.1579
Mean1280.3324
Median Absolute Deviation (MAD)668
Skewness42.233982
Sum12803324
Variance3603999.4
MonotonicityNot monotonic
2024-05-03T23:58:24.578465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
602 19
 
0.2%
1295 17
 
0.2%
2169 16
 
0.2%
1210 16
 
0.2%
106 15
 
0.1%
431 15
 
0.1%
617 15
 
0.1%
1203 15
 
0.1%
324 14
 
0.1%
146 14
 
0.1%
Other values (1514) 9844
98.4%
ValueCountFrequency (%)
101 6
 
0.1%
102 8
0.1%
103 12
0.1%
104 8
0.1%
105 8
0.1%
106 15
0.1%
107 8
0.1%
108 8
0.1%
109 8
0.1%
110 11
0.1%
ValueCountFrequency (%)
99999 2
 
< 0.1%
99998 1
 
< 0.1%
3543 2
 
< 0.1%
3542 5
0.1%
3541 5
0.1%
3539 7
0.1%
3538 6
0.1%
3537 4
< 0.1%
3536 4
< 0.1%
3535 8
0.1%

대여소명
Text

Distinct1524
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:58:25.795943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.4459
Min length8

Characters and Unicode

Total characters154459
Distinct characters520
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

Unique28 ?
Unique (%)0.3%

Sample

1st row1130. 화곡본동시장 버스정류장
2nd row103. 망원역 2번출구 앞
3rd row583. 청계천 생태교실 앞
4th row390. 충무로역 1번출구
5th row226. 샛강역 1번출구 앞
ValueCountFrequency (%)
앞 2668
 
8.4%
옆 523
 
1.6%
출구 379
 
1.2%
1번출구 356
 
1.1%
사거리 305
 
1.0%
뒤 287
 
0.9%
2번출구 284
 
0.9%
교차로 254
 
0.8%
3번출구 250
 
0.8%
4번출구 218
 
0.7%
Other values (3351) 26261
82.6%
2024-05-03T23:58:27.542995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21962
 
14.2%
. 10024
 
6.5%
1 9047
 
5.9%
2 6661
 
4.3%
3 4705
 
3.0%
구 3661
 
2.4%
5 3467
 
2.2%
0 3358
 
2.2%
4 3254
 
2.1%
앞 3163
 
2.0%
Other values (510) 85157
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78853
51.1%
Decimal Number 40480
26.2%
Space Separator 21962
 
14.2%
Other Punctuation 10091
 
6.5%
Uppercase Letter 1284
 
0.8%
Open Punctuation 781
 
0.5%
Close Punctuation 781
 
0.5%
Lowercase Letter 109
 
0.1%
Dash Punctuation 76
 
< 0.1%
Math Symbol 26
 
< 0.1%
Other values (2) 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3661
 
4.6%
앞 3163
 
4.0%
역 2929
 
3.7%
번 2624
 
3.3%
출 2566
 
3.3%
동 1901
 
2.4%
교 1589
 
2.0%
리 1449
 
1.8%
원 1177
 
1.5%
사 1173
 
1.5%
Other values (453) 56621
71.8%
Uppercase Letter
ValueCountFrequency (%)
K 186
14.5%
S 167
13.0%
C 127
9.9%
G 96
 
7.5%
L 94
 
7.3%
T 71
 
5.5%
B 71
 
5.5%
M 62
 
4.8%
A 61
 
4.8%
I 55
 
4.3%
Other values (14) 294
22.9%
Decimal Number
ValueCountFrequency (%)
1 9047
22.3%
2 6661
16.5%
3 4705
11.6%
5 3467
 
8.6%
0 3358
 
8.3%
4 3254
 
8.0%
6 3053
 
7.5%
7 2373
 
5.9%
9 2294
 
5.7%
8 2268
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
e 41
37.6%
k 13
 
11.9%
t 13
 
11.9%
l 9
 
8.3%
n 6
 
5.5%
s 6
 
5.5%
o 6
 
5.5%
c 6
 
5.5%
m 6
 
5.5%
y 3
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 10024
99.3%
, 51
 
0.5%
& 9
 
0.1%
? 6
 
0.1%
· 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 17
65.4%
+ 9
34.6%
Space Separator
ValueCountFrequency (%)
21962
100.0%
Open Punctuation
ValueCountFrequency (%)
( 781
100.0%
Close Punctuation
ValueCountFrequency (%)
) 781
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78860
51.1%
Common 74206
48.0%
Latin 1393
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3661
 
4.6%
앞 3163
 
4.0%
역 2929
 
3.7%
번 2624
 
3.3%
출 2566
 
3.3%
동 1901
 
2.4%
교 1589
 
2.0%
리 1449
 
1.8%
원 1177
 
1.5%
사 1173
 
1.5%
Other values (454) 56628
71.8%
Latin
ValueCountFrequency (%)
K 186
13.4%
S 167
12.0%
C 127
 
9.1%
G 96
 
6.9%
L 94
 
6.7%
T 71
 
5.1%
B 71
 
5.1%
M 62
 
4.5%
A 61
 
4.4%
I 55
 
3.9%
Other values (24) 403
28.9%
Common
ValueCountFrequency (%)
21962
29.6%
. 10024
13.5%
1 9047
12.2%
2 6661
 
9.0%
3 4705
 
6.3%
5 3467
 
4.7%
0 3358
 
4.5%
4 3254
 
4.4%
6 3053
 
4.1%
7 2373
 
3.2%
Other values (12) 6302
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78853
51.1%
ASCII 75598
48.9%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21962
29.1%
. 10024
13.3%
1 9047
12.0%
2 6661
 
8.8%
3 4705
 
6.2%
5 3467
 
4.6%
0 3358
 
4.4%
4 3254
 
4.3%
6 3053
 
4.0%
7 2373
 
3.1%
Other values (45) 7694
 
10.2%
Hangul
ValueCountFrequency (%)
구 3661
 
4.6%
앞 3163
 
4.0%
역 2929
 
3.7%
번 2624
 
3.3%
출 2566
 
3.3%
동 1901
 
2.4%
교 1589
 
2.0%
리 1449
 
1.8%
원 1177
 
1.5%
사 1173
 
1.5%
Other values (453) 56621
71.8%
None
ValueCountFrequency (%)
㈜ 7
87.5%
· 1
 
12.5%

대여구분코드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5968 
일일(회원)
3383 
단체
 
365
일일(비회원)
 
284

Length

Max length7
Median length2
Mean length3.4952
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5968
59.7%
일일(회원) 3383
33.8%
단체 365
 
3.6%
일일(비회원) 284
 
2.8%

Length

2024-05-03T23:58:27.982736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:58:28.385859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5968
59.7%
일일(회원 3383
33.8%
단체 365
 
3.6%
일일(비회원 284
 
2.8%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3454 
M
2682 
F
2080 
<NA>
1777 
f
 
4

Length

Max length4
Median length2
Mean length1.8785
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
\N 3454
34.5%
M 2682
26.8%
F 2080
20.8%
<NA> 1777
17.8%
f 4
 
< 0.1%
m 3
 
< 0.1%

Length

2024-05-03T23:58:28.853999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:58:29.286370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3454
34.5%
m 2685
26.9%
f 2084
20.8%
na 1777
17.8%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2305 
AGE_003
1848 
AGE_004
1586 
AGE_001
1290 
AGE_005
1281 
Other values (3)
1690 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_002
2nd rowAGE_005
3rd rowAGE_008
4th rowAGE_002
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 2305
23.1%
AGE_003 1848
18.5%
AGE_004 1586
15.9%
AGE_001 1290
12.9%
AGE_005 1281
12.8%
AGE_006 750
 
7.5%
AGE_008 620
 
6.2%
AGE_007 320
 
3.2%

Length

2024-05-03T23:58:29.678244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:58:30.022942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2305
23.1%
age_003 1848
18.5%
age_004 1586
15.9%
age_001 1290
12.9%
age_005 1281
12.8%
age_006 750
 
7.5%
age_008 620
 
6.2%
age_007 320
 
3.2%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct205
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.2283
Minimum1
Maximum452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:58:30.474823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q318
95-th percentile70
Maximum452
Range451
Interquartile range (IQR)16

Descriptive statistics

Standard deviation29.26609
Coefficient of variation (CV)1.8033984
Kurtosis39.398245
Mean16.2283
Median Absolute Deviation (MAD)4
Skewness4.8836019
Sum162283
Variance856.50403
MonotonicityNot monotonic
2024-05-03T23:58:31.027275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1918
19.2%
2 1304
 
13.0%
3 821
 
8.2%
4 600
 
6.0%
5 462
 
4.6%
6 379
 
3.8%
7 307
 
3.1%
8 280
 
2.8%
9 236
 
2.4%
10 204
 
2.0%
Other values (195) 3489
34.9%
ValueCountFrequency (%)
1 1918
19.2%
2 1304
13.0%
3 821
8.2%
4 600
 
6.0%
5 462
 
4.6%
6 379
 
3.8%
7 307
 
3.1%
8 280
 
2.8%
9 236
 
2.4%
10 204
 
2.0%
ValueCountFrequency (%)
452 1
< 0.1%
447 1
< 0.1%
431 1
< 0.1%
391 1
< 0.1%
384 1
< 0.1%
361 1
< 0.1%
347 1
< 0.1%
342 1
< 0.1%
318 1
< 0.1%
310 1
< 0.1%

운동량
Text

Distinct9216
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:58:31.893821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.0188
Min length1

Characters and Unicode

Total characters60188
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

Unique8707 ?
Unique (%)87.1%

Sample

1st row250.3
2nd row104.53
3rd row398.62
4th row2788.21
5th row906.59
ValueCountFrequency (%)
0 129
 
1.3%
n 15
 
0.1%
42.21 8
 
0.1%
57.66 6
 
0.1%
17.76 6
 
0.1%
35.52 6
 
0.1%
74.13 6
 
0.1%
82.88 5
 
< 0.1%
45.3 5
 
< 0.1%
61.52 5
 
< 0.1%
Other values (9206) 9809
98.1%
2024-05-03T23:58:33.674603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9757
16.2%
1 7255
12.1%
2 5941
9.9%
3 5414
9.0%
4 5068
8.4%
6 4819
8.0%
5 4803
8.0%
7 4655
7.7%
8 4559
7.6%
9 4413
7.3%
Other values (3) 3504
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50401
83.7%
Other Punctuation 9772
 
16.2%
Uppercase Letter 15
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7255
14.4%
2 5941
11.8%
3 5414
10.7%
4 5068
10.1%
6 4819
9.6%
5 4803
9.5%
7 4655
9.2%
8 4559
9.0%
9 4413
8.8%
0 3474
6.9%
Other Punctuation
ValueCountFrequency (%)
. 9757
99.8%
\ 15
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60173
> 99.9%
Latin 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9757
16.2%
1 7255
12.1%
2 5941
9.9%
3 5414
9.0%
4 5068
8.4%
6 4819
8.0%
5 4803
8.0%
7 4655
7.7%
8 4559
7.6%
9 4413
7.3%
Other values (2) 3489
 
5.8%
Latin
ValueCountFrequency (%)
N 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9757
16.2%
1 7255
12.1%
2 5941
9.9%
3 5414
9.0%
4 5068
8.4%
6 4819
8.0%
5 4803
8.0%
7 4655
7.7%
8 4559
7.6%
9 4413
7.3%
Other values (3) 3504
 
5.8%

탄소량
Text

Distinct3510
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:58:34.432426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.2017
Min length1

Characters and Unicode

Total characters42017
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

Unique2092 ?
Unique (%)20.9%

Sample

1st row1.85
2nd row0.97
3rd row4.1
4th row23.62
5th row9.15
ValueCountFrequency (%)
0 133
 
1.3%
0.48 40
 
0.4%
0.34 36
 
0.4%
0.26 36
 
0.4%
0.42 36
 
0.4%
0.32 35
 
0.4%
0.28 33
 
0.3%
0.55 33
 
0.3%
0.25 32
 
0.3%
0.9 31
 
0.3%
Other values (3500) 9555
95.5%
2024-05-03T23:58:35.608256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9757
23.2%
1 5275
12.6%
2 3944
9.4%
0 3465
 
8.2%
3 3406
 
8.1%
4 3038
 
7.2%
5 2884
 
6.9%
6 2689
 
6.4%
7 2583
 
6.1%
8 2558
 
6.1%
Other values (3) 2418
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32230
76.7%
Other Punctuation 9772
 
23.3%
Uppercase Letter 15
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5275
16.4%
2 3944
12.2%
0 3465
10.8%
3 3406
10.6%
4 3038
9.4%
5 2884
8.9%
6 2689
8.3%
7 2583
8.0%
8 2558
7.9%
9 2388
7.4%
Other Punctuation
ValueCountFrequency (%)
. 9757
99.8%
\ 15
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42002
> 99.9%
Latin 15
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9757
23.2%
1 5275
12.6%
2 3944
9.4%
0 3465
 
8.2%
3 3406
 
8.1%
4 3038
 
7.2%
5 2884
 
6.9%
6 2689
 
6.4%
7 2583
 
6.1%
8 2558
 
6.1%
Other values (2) 2403
 
5.7%
Latin
ValueCountFrequency (%)
N 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9757
23.2%
1 5275
12.6%
2 3944
9.4%
0 3465
 
8.2%
3 3406
 
8.1%
4 3038
 
7.2%
5 2884
 
6.9%
6 2689
 
6.4%
7 2583
 
6.1%
8 2558
 
6.1%
Other values (3) 2418
 
5.8%

이동거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5813
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67536.8
Minimum0
Maximum2073030
Zeros144
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:58:36.088939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1090
Q15020
median17035
Q369972.5
95-th percentile310614.5
Maximum2073030
Range2073030
Interquartile range (IQR)64952.5

Descriptive statistics

Standard deviation127540.6
Coefficient of variation (CV)1.8884609
Kurtosis30.826687
Mean67536.8
Median Absolute Deviation (MAD)14885
Skewness4.3709529
Sum6.75368 × 108
Variance1.6266605 × 1010
MonotonicityNot monotonic
2024-05-03T23:58:36.562551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 144
 
1.4%
1190 14
 
0.1%
3830 13
 
0.1%
1760 13
 
0.1%
1110 13
 
0.1%
2390 12
 
0.1%
3720 11
 
0.1%
680 11
 
0.1%
2190 11
 
0.1%
1470 11
 
0.1%
Other values (5803) 9747
97.5%
ValueCountFrequency (%)
0 144
1.4%
10 3
 
< 0.1%
20 1
 
< 0.1%
30 1
 
< 0.1%
40 2
 
< 0.1%
70 1
 
< 0.1%
130 1
 
< 0.1%
150 2
 
< 0.1%
170 1
 
< 0.1%
200 1
 
< 0.1%
ValueCountFrequency (%)
2073030 1
< 0.1%
1775940 1
< 0.1%
1560020 1
< 0.1%
1523780 1
< 0.1%
1435970 1
< 0.1%
1330540 1
< 0.1%
1308670 1
< 0.1%
1279010 1
< 0.1%
1273420 1
< 0.1%
1246510 1
< 0.1%

이용시간
Real number (ℝ)

HIGH CORRELATION 

Distinct1530
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316.7726
Minimum0
Maximum6811
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:58:37.000839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q140
median119
Q3351.25
95-th percentile1333.05
Maximum6811
Range6811
Interquartile range (IQR)311.25

Descriptive statistics

Standard deviation532.19717
Coefficient of variation (CV)1.6800606
Kurtosis24.293488
Mean316.7726
Median Absolute Deviation (MAD)98
Skewness4.0107518
Sum3167726
Variance283233.82
MonotonicityNot monotonic
2024-05-03T23:58:37.440254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 101
 
1.0%
11 93
 
0.9%
6 88
 
0.9%
15 86
 
0.9%
8 85
 
0.9%
14 80
 
0.8%
9 79
 
0.8%
16 78
 
0.8%
10 78
 
0.8%
13 74
 
0.7%
Other values (1520) 9158
91.6%
ValueCountFrequency (%)
0 12
 
0.1%
1 4
 
< 0.1%
2 39
 
0.4%
3 59
0.6%
4 65
0.7%
5 57
0.6%
6 88
0.9%
7 101
1.0%
8 85
0.9%
9 79
0.8%
ValueCountFrequency (%)
6811 1
< 0.1%
6677 1
< 0.1%
6201 1
< 0.1%
6030 1
< 0.1%
5950 1
< 0.1%
5786 1
< 0.1%
5544 1
< 0.1%
5399 1
< 0.1%
5245 1
< 0.1%
5040 1
< 0.1%

Interactions

2024-05-03T23:58:19.219312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:15.040507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:16.273261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:17.663573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:19.657680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:15.322455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:16.626481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:18.033000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:20.045195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:15.619610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:16.915188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:18.390572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:20.444090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:15.912437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:17.261977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:58:18.769153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T23:58:37.739905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리이용시간
대여소번호1.0000.0000.0000.0130.0000.0000.000
대여구분코드0.0001.0000.1470.7570.2280.2000.231
성별0.0000.1471.0000.2550.1410.1460.142
연령대코드0.0130.7570.2551.0000.1840.1640.171
이용건수0.0000.2280.1410.1841.0000.9010.893
이동거리0.0000.2000.1460.1640.9011.0000.868
이용시간0.0000.2310.1420.1710.8930.8681.000
2024-05-03T23:58:38.025635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드대여구분코드성별
연령대코드1.0000.4240.159
대여구분코드0.4241.0000.121
성별0.1590.1211.000
2024-05-03T23:58:38.284891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리이용시간대여구분코드성별연령대코드
대여소번호1.000-0.045-0.023-0.0460.0000.0000.010
이용건수-0.0451.0000.8790.8990.1380.0590.088
이동거리-0.0230.8791.0000.9090.1210.0610.079
이용시간-0.0460.8990.9091.0000.1400.0590.082
대여구분코드0.0000.1380.1210.1401.0000.1210.424
성별0.0000.0590.0610.0590.1211.0000.159
연령대코드0.0100.0880.0790.0820.4240.1591.000

Missing values

2024-05-03T23:58:21.126277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T23:58:21.764056image/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
25075Dec-1911301130. 화곡본동시장 버스정류장일일(회원)MAGE_0022250.31.85796043
83Dec-19103103. 망원역 2번출구 앞일일(회원)\NAGE_0051104.530.97419024
14654Dec-19583583. 청계천 생태교실 앞일일(회원)\NAGE_0082398.624.117660133
9777Dec-19390390. 충무로역 1번출구일일(회원)MAGE_002212788.2123.62101830696
4320Dec-19226226. 샛강역 1번출구 앞일일(회원)FAGE_0028906.599.1539440347
42631Dec-1920252025. 흑석역 1번출구정기\NAGE_006268225.2968.23294040773
41731Dec-1919841984. 구로구청단체\NAGE_0012137.811.01435029
42300Dec-1920072007. 유한양행앞일일(회원)MAGE_0027141.141.22523071
39926Dec-1918571857. 주공14단지정기FAGE_0032251.242.841224056
13642Dec-19552552. 대림아크로리버 앞일일(회원)\NAGE_0012194.282.07892076
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리이용시간
43908Dec-1921112111. 서울대입구역 1번출구일일(회원)MAGE_0036905.87.5132350184
11083Dec-19430430. KEB하나은행 장충동지점정기\NAGE_0012374.582.741179087
45104Dec-1921782178. 서울대학교 정문일일(회원)\NAGE_00351363.6711.9751620137
3913Dec-19214214. 금융감독원 앞정기MAGE_00691994.9719.0582060236
12293Dec-19507507. 성수아이에스비즈타워 앞정기MAGE_004252793.8419.6184460455
9441Dec-19380380. CJ제일제당 앞정기FAGE_0078674.895.0621840228
46047Dec-1922292229.로고스교회 맞은 편일일(비회원)\NAGE_00861760.115.8768380203
53263Dec-1935253525. 금호스포츠센터앞정기MAGE_001100021
14029Dec-19563563. 성동세무서 건너편정기<NA>AGE_0054177.771.48637039
37020Dec-1916751675. 월계문화체육센터정기\NAGE_002211268.1211.3949200169