Overview

Dataset statistics

Number of variables11
Number of observations8489
Missing cells7186
Missing cells (%)7.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory762.8 KiB
Average record size in memory92.0 B

Variable types

DateTime1
Numeric4
Text3
Categorical2
Boolean1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/A/1/datasetView.do

Alerts

대여일자 has constant value ""Constant
대여구분코드 has constant value ""Constant
성별 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
성별 has 7186 (84.7%) missing valuesMissing
이동거리(M) has 145 (1.7%) zerosZeros

Reproduction

Analysis started2024-05-18 04:51:42.660764
Analysis finished2024-05-18 04:51:53.175859
Duration10.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.4 KiB
Minimum2023-02-01 00:00:00
Maximum2023-02-01 00:00:00
2024-05-18T13:51:53.310859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:53.620091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct2451
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2167.762
Minimum102
Maximum6054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2024-05-18T13:51:54.089723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile250.4
Q1905
median1756
Q33547
95-th percentile4819.6
Maximum6054
Range5952
Interquartile range (IQR)2642

Descriptive statistics

Standard deviation1513.0158
Coefficient of variation (CV)0.69796213
Kurtosis-0.97774754
Mean2167.762
Median Absolute Deviation (MAD)1056
Skewness0.5068534
Sum18402132
Variance2289216.9
MonotonicityNot monotonic
2024-05-18T13:51:54.530590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2715 14
 
0.2%
795 10
 
0.1%
1851 10
 
0.1%
3668 10
 
0.1%
1158 10
 
0.1%
1153 10
 
0.1%
781 9
 
0.1%
726 9
 
0.1%
1637 9
 
0.1%
3798 9
 
0.1%
Other values (2441) 8389
98.8%
ValueCountFrequency (%)
102 2
< 0.1%
103 3
< 0.1%
104 3
< 0.1%
105 3
< 0.1%
106 3
< 0.1%
107 4
< 0.1%
108 3
< 0.1%
109 3
< 0.1%
111 2
< 0.1%
112 3
< 0.1%
ValueCountFrequency (%)
6054 1
 
< 0.1%
5865 2
 
< 0.1%
5864 1
 
< 0.1%
5862 3
< 0.1%
5861 2
 
< 0.1%
5860 3
< 0.1%
5859 1
 
< 0.1%
5858 6
0.1%
5857 2
 
< 0.1%
5855 1
 
< 0.1%

대여소
Text

Distinct2451
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size66.4 KiB
2024-05-18T13:51:55.147188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length15.611733
Min length7

Characters and Unicode

Total characters132528
Distinct characters572
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

Unique422 ?
Unique (%)5.0%

Sample

1st row1269. 리센츠아파트
2nd row1446. 중랑전화국 교차로
3rd row1720. 도봉구청 옆(중랑천변)
4th row2059. 보라매공원 정문
5th row1343. 한성대7번출구 앞
ValueCountFrequency (%)
앞 2211
 
8.9%
출구 393
 
1.6%
옆 294
 
1.2%
1번출구 277
 
1.1%
교차로 204
 
0.8%
사거리 176
 
0.7%
뒤 167
 
0.7%
3번출구 166
 
0.7%
입구 156
 
0.6%
2번출구 150
 
0.6%
Other values (4911) 20558
83.1%
2024-05-18T13:51:56.459876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16435
 
12.4%
. 8500
 
6.4%
1 6804
 
5.1%
2 4953
 
3.7%
3 4088
 
3.1%
4 4084
 
3.1%
5 3299
 
2.5%
0 3039
 
2.3%
6 3002
 
2.3%
7 2897
 
2.2%
Other values (562) 75427
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68060
51.4%
Decimal Number 36655
27.7%
Space Separator 16435
 
12.4%
Other Punctuation 8617
 
6.5%
Uppercase Letter 1178
 
0.9%
Close Punctuation 675
 
0.5%
Open Punctuation 675
 
0.5%
Lowercase Letter 143
 
0.1%
Dash Punctuation 63
 
< 0.1%
Connector Punctuation 12
 
< 0.1%
Other values (3) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 2849
 
4.2%
앞 2582
 
3.8%
역 2233
 
3.3%
번 1995
 
2.9%
출 1952
 
2.9%
동 1910
 
2.8%
교 1309
 
1.9%
파 1264
 
1.9%
아 1210
 
1.8%
트 1159
 
1.7%
Other values (500) 49597
72.9%
Uppercase Letter
ValueCountFrequency (%)
S 158
13.4%
K 116
9.8%
C 111
9.4%
T 108
9.2%
A 96
 
8.1%
D 81
 
6.9%
B 68
 
5.8%
M 65
 
5.5%
G 64
 
5.4%
P 56
 
4.8%
Other values (14) 255
21.6%
Lowercase Letter
ValueCountFrequency (%)
e 44
30.8%
s 20
14.0%
k 19
13.3%
n 14
 
9.8%
t 7
 
4.9%
y 7
 
4.9%
l 7
 
4.9%
v 5
 
3.5%
r 4
 
2.8%
f 4
 
2.8%
Other values (3) 12
 
8.4%
Decimal Number
ValueCountFrequency (%)
1 6804
18.6%
2 4953
13.5%
3 4088
11.2%
4 4084
11.1%
5 3299
9.0%
0 3039
8.3%
6 3002
8.2%
7 2897
7.9%
8 2402
 
6.6%
9 2087
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 8500
98.6%
, 81
 
0.9%
& 20
 
0.2%
? 9
 
0.1%
· 7
 
0.1%
Math Symbol
ValueCountFrequency (%)
+ 4
57.1%
~ 3
42.9%
Other Number
ValueCountFrequency (%)
① 3
60.0%
② 2
40.0%
Space Separator
ValueCountFrequency (%)
16435
100.0%
Close Punctuation
ValueCountFrequency (%)
) 675
100.0%
Open Punctuation
ValueCountFrequency (%)
( 675
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 12
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68063
51.4%
Common 63144
47.6%
Latin 1321
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 2849
 
4.2%
앞 2582
 
3.8%
역 2233
 
3.3%
번 1995
 
2.9%
출 1952
 
2.9%
동 1910
 
2.8%
교 1309
 
1.9%
파 1264
 
1.9%
아 1210
 
1.8%
트 1159
 
1.7%
Other values (501) 49600
72.9%
Latin
ValueCountFrequency (%)
S 158
 
12.0%
K 116
 
8.8%
C 111
 
8.4%
T 108
 
8.2%
A 96
 
7.3%
D 81
 
6.1%
B 68
 
5.1%
M 65
 
4.9%
G 64
 
4.8%
P 56
 
4.2%
Other values (27) 398
30.1%
Common
ValueCountFrequency (%)
16435
26.0%
. 8500
13.5%
1 6804
10.8%
2 4953
 
7.8%
3 4088
 
6.5%
4 4084
 
6.5%
5 3299
 
5.2%
0 3039
 
4.8%
6 3002
 
4.8%
7 2897
 
4.6%
Other values (14) 6043
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68060
51.4%
ASCII 64453
48.6%
None 10
 
< 0.1%
Enclosed Alphanum 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16435
25.5%
. 8500
13.2%
1 6804
10.6%
2 4953
 
7.7%
3 4088
 
6.3%
4 4084
 
6.3%
5 3299
 
5.1%
0 3039
 
4.7%
6 3002
 
4.7%
7 2897
 
4.5%
Other values (48) 7352
11.4%
Hangul
ValueCountFrequency (%)
구 2849
 
4.2%
앞 2582
 
3.8%
역 2233
 
3.3%
번 1995
 
2.9%
출 1952
 
2.9%
동 1910
 
2.8%
교 1309
 
1.9%
파 1264
 
1.9%
아 1210
 
1.8%
트 1159
 
1.7%
Other values (500) 49597
72.9%
None
ValueCountFrequency (%)
· 7
70.0%
㈜ 3
30.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 3
60.0%
② 2
40.0%

대여구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.4 KiB
정기권
8489 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기권 8489
100.0%

Length

2024-05-18T13:51:57.031833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:51:57.424502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기권 8489
100.0%

성별
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing7186
Missing (%)84.7%
Memory size16.7 KiB
False
1303 
(Missing)
7186 
ValueCountFrequency (%)
False 1303
 
15.3%
(Missing) 7186
84.7%
2024-05-18T13:51:57.729834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

연령대
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.4 KiB
20대
3147 
30대
1732 
40대
1267 
50대
1003 
기타
479 
Other values (3)
861 

Length

Max length5
Median length3
Mean length3.001767
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row~10대
2nd row~10대
3rd row~10대
4th row~10대
5th row~10대

Common Values

ValueCountFrequency (%)
20대 3147
37.1%
30대 1732
20.4%
40대 1267
14.9%
50대 1003
 
11.8%
기타 479
 
5.6%
60대 419
 
4.9%
~10대 390
 
4.6%
70대이상 52
 
0.6%

Length

2024-05-18T13:51:58.129124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:51:58.590741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3147
37.1%
30대 1732
20.4%
40대 1267
14.9%
50대 1003
 
11.8%
기타 479
 
5.6%
60대 419
 
4.9%
10대 390
 
4.6%
70대이상 52
 
0.6%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0384026
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2024-05-18T13:51:59.095877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5.6
Maximum26
Range25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8376457
Coefficient of variation (CV)0.90151261
Kurtosis17.055992
Mean2.0384026
Median Absolute Deviation (MAD)0
Skewness3.295941
Sum17304
Variance3.3769417
MonotonicityNot monotonic
2024-05-18T13:51:59.444792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 4784
56.4%
2 1746
 
20.6%
3 825
 
9.7%
4 443
 
5.2%
5 266
 
3.1%
6 145
 
1.7%
7 79
 
0.9%
8 63
 
0.7%
9 51
 
0.6%
10 30
 
0.4%
Other values (11) 57
 
0.7%
ValueCountFrequency (%)
1 4784
56.4%
2 1746
 
20.6%
3 825
 
9.7%
4 443
 
5.2%
5 266
 
3.1%
6 145
 
1.7%
7 79
 
0.9%
8 63
 
0.7%
9 51
 
0.6%
10 30
 
0.4%
ValueCountFrequency (%)
26 1
 
< 0.1%
22 1
 
< 0.1%
19 2
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 4
 
< 0.1%
14 8
0.1%
13 9
0.1%
12 13
0.2%

운동량
Text

Distinct6315
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Memory size66.4 KiB
2024-05-18T13:52:00.334934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.2346566
Min length2

Characters and Unicode

Total characters44437
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

Unique4929 ?
Unique (%)58.1%

Sample

1st row76.19
2nd row40.02
3rd row52.46
4th row34.69
5th row16.73
ValueCountFrequency (%)
0.00 154
 
1.8%
n 22
 
0.3%
37.07 8
 
0.1%
16.73 8
 
0.1%
15.44 8
 
0.1%
32.43 7
 
0.1%
23.76 7
 
0.1%
32.95 7
 
0.1%
21.11 7
 
0.1%
18.02 6
 
0.1%
Other values (6305) 8255
97.2%
2024-05-18T13:52:01.830926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 8467
19.1%
1 5352
12.0%
2 4409
9.9%
3 3787
8.5%
4 3526
7.9%
0 3394
7.6%
5 3280
 
7.4%
6 3160
 
7.1%
8 3051
 
6.9%
7 3033
 
6.8%
Other values (3) 2978
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35926
80.8%
Other Punctuation 8489
 
19.1%
Uppercase Letter 22
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5352
14.9%
2 4409
12.3%
3 3787
10.5%
4 3526
9.8%
0 3394
9.4%
5 3280
9.1%
6 3160
8.8%
8 3051
8.5%
7 3033
8.4%
9 2934
8.2%
Other Punctuation
ValueCountFrequency (%)
. 8467
99.7%
\ 22
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44415
> 99.9%
Latin 22
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 8467
19.1%
1 5352
12.0%
2 4409
9.9%
3 3787
8.5%
4 3526
7.9%
0 3394
7.6%
5 3280
 
7.4%
6 3160
 
7.1%
8 3051
 
6.9%
7 3033
 
6.8%
Other values (2) 2956
 
6.7%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44437
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8467
19.1%
1 5352
12.0%
2 4409
9.9%
3 3787
8.5%
4 3526
7.9%
0 3394
7.6%
5 3280
 
7.4%
6 3160
 
7.1%
8 3051
 
6.9%
7 3033
 
6.8%
Other values (3) 2978
 
6.7%

탄소량
Text

Distinct423
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size66.4 KiB
2024-05-18T13:52:02.939023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.9948168
Min length2

Characters and Unicode

Total characters33912
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

Unique84 ?
Unique (%)1.0%

Sample

1st row0.69
2nd row0.41
3rd row0.32
4th row0.27
5th row0.15
ValueCountFrequency (%)
0.22 139
 
1.6%
0.00 131
 
1.5%
0.16 126
 
1.5%
0.15 125
 
1.5%
0.20 119
 
1.4%
0.18 118
 
1.4%
0.27 115
 
1.4%
0.23 115
 
1.4%
0.24 112
 
1.3%
0.21 110
 
1.3%
Other values (413) 7279
85.7%
2024-05-18T13:52:04.435264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 8467
25.0%
0 7977
23.5%
1 3722
11.0%
2 2731
 
8.1%
3 2160
 
6.4%
4 1769
 
5.2%
5 1665
 
4.9%
6 1510
 
4.5%
7 1360
 
4.0%
8 1259
 
3.7%
Other values (3) 1292
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25401
74.9%
Other Punctuation 8489
 
25.0%
Uppercase Letter 22
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7977
31.4%
1 3722
14.7%
2 2731
 
10.8%
3 2160
 
8.5%
4 1769
 
7.0%
5 1665
 
6.6%
6 1510
 
5.9%
7 1360
 
5.4%
8 1259
 
5.0%
9 1248
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 8467
99.7%
\ 22
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33890
99.9%
Latin 22
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 8467
25.0%
0 7977
23.5%
1 3722
11.0%
2 2731
 
8.1%
3 2160
 
6.4%
4 1769
 
5.2%
5 1665
 
4.9%
6 1510
 
4.5%
7 1360
 
4.0%
8 1259
 
3.7%
Other values (2) 1270
 
3.7%
Latin
ValueCountFrequency (%)
N 22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8467
25.0%
0 7977
23.5%
1 3722
11.0%
2 2731
 
8.1%
3 2160
 
6.4%
4 1769
 
5.2%
5 1665
 
4.9%
6 1510
 
4.5%
7 1360
 
4.0%
8 1259
 
3.7%
Other values (3) 1292
 
3.8%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6476
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3306.4526
Minimum0
Maximum36382.27
Zeros145
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2024-05-18T13:52:04.961993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile430
Q11109.86
median2170.92
Q34350
95-th percentile10123.804
Maximum36382.27
Range36382.27
Interquartile range (IQR)3240.14

Descriptive statistics

Standard deviation3361.9053
Coefficient of variation (CV)1.0167711
Kurtosis8.7373023
Mean3306.4526
Median Absolute Deviation (MAD)1299.34
Skewness2.4178149
Sum28068476
Variance11302407
MonotonicityNot monotonic
2024-05-18T13:52:05.518814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 145
 
1.7%
930.0 18
 
0.2%
750.0 18
 
0.2%
860.0 17
 
0.2%
650.0 17
 
0.2%
680.0 17
 
0.2%
940.0 16
 
0.2%
790.0 16
 
0.2%
1590.0 15
 
0.2%
1230.0 14
 
0.2%
Other values (6466) 8196
96.5%
ValueCountFrequency (%)
0.0 145
1.7%
0.1 5
 
0.1%
10.0 1
 
< 0.1%
20.0 1
 
< 0.1%
22.15 1
 
< 0.1%
30.0 2
 
< 0.1%
40.0 1
 
< 0.1%
50.0 3
 
< 0.1%
60.0 2
 
< 0.1%
75.64 1
 
< 0.1%
ValueCountFrequency (%)
36382.27 1
< 0.1%
29964.49 1
< 0.1%
29527.41 1
< 0.1%
28725.43 1
< 0.1%
27794.18 1
< 0.1%
27397.13 1
< 0.1%
26790.49 1
< 0.1%
25535.17 1
< 0.1%
25476.05 1
< 0.1%
24851.83 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct193
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.55672
Minimum0
Maximum495
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2024-05-18T13:52:06.307449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median16
Q333
95-th percentile78
Maximum495
Range495
Interquartile range (IQR)25

Descriptive statistics

Standard deviation28.053077
Coefficient of variation (CV)1.0976791
Kurtosis22.237534
Mean25.55672
Median Absolute Deviation (MAD)10
Skewness3.274625
Sum216951
Variance786.97513
MonotonicityNot monotonic
2024-05-18T13:52:06.906066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 373
 
4.4%
5 358
 
4.2%
7 352
 
4.1%
6 350
 
4.1%
10 331
 
3.9%
8 326
 
3.8%
3 325
 
3.8%
9 310
 
3.7%
12 249
 
2.9%
11 244
 
2.9%
Other values (183) 5271
62.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 71
 
0.8%
2 210
2.5%
3 325
3.8%
4 373
4.4%
5 358
4.2%
6 350
4.1%
7 352
4.1%
8 326
3.8%
9 310
3.7%
ValueCountFrequency (%)
495 1
< 0.1%
373 1
< 0.1%
288 1
< 0.1%
276 1
< 0.1%
265 1
< 0.1%
259 1
< 0.1%
254 1
< 0.1%
245 1
< 0.1%
242 1
< 0.1%
241 1
< 0.1%

Interactions

2024-05-18T13:51:50.771484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:45.739427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:48.027566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:49.396628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:51.128313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:46.349364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:48.402548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:49.757587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:51.487388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:46.894958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:48.704536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:50.168059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:51.786109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:47.557136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:49.067395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:51:50.487909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T13:52:07.259988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호연령대이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0620.1260.0770.061
연령대0.0621.0000.1980.1520.169
이용건수0.1260.1981.0000.7890.587
이동거리(M)0.0770.1520.7891.0000.633
이용시간(분)0.0610.1690.5870.6331.000
2024-05-18T13:52:07.595195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)연령대
대여소번호1.000-0.068-0.057-0.0680.030
이용건수-0.0681.0000.6810.6620.096
이동거리(M)-0.0570.6811.0000.8820.073
이용시간(분)-0.0680.6620.8821.0000.057
연령대0.0300.0960.0730.0571.000

Missing values

2024-05-18T13:51:52.218428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T13:51:52.950913image/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

대여일자대여소번호대여소대여구분코드성별연령대이용건수운동량탄소량이동거리(M)이용시간(분)
02023-02-0112691269. 리센츠아파트정기권<NA>~10대176.190.692960.025
12023-02-0114461446. 중랑전화국 교차로정기권<NA>~10대140.020.411772.8113
22023-02-0117201720. 도봉구청 옆(중랑천변)정기권<NA>~10대152.460.321380.06
32023-02-0120592059. 보라매공원 정문정기권<NA>~10대134.690.271183.728
42023-02-0113431343. 한성대7번출구 앞정기권<NA>~10대116.730.15650.08
52023-02-0119621962. 가리봉동주민센터정기권<NA>~10대153.540.482080.028
62023-02-0119661966. 한마을아파트 정문상가정기권<NA>~10대143.500.391690.010
72023-02-0111581158. 가양역 8번출구정기권<NA>~10대174.080.622672.3715
82023-02-0111581158. 가양역 8번출구정기권<NA>~10대140.390.391700.06
92023-02-0121652165. JK장평타워정기권<NA>~10대131.930.311343.855
대여일자대여소번호대여소대여구분코드성별연령대이용건수운동량탄소량이동거리(M)이용시간(분)
84792023-02-0150575057. 마곡역4번출구정기권F20대278.180.833553.737
84802023-02-0150615061. 우장산동 가곡어린이공원앞정기권F20대248.430.492096.7413
84812023-02-0150625062. 마곡 MICE 복합단지정기권F20대3156.091.667150.051
84822023-02-01240240. 문래역 4번출구 앞정기권F20대6177.981.737445.2361
84832023-02-0138823882. 자양2동 주민센터정기권F20대113.120.17720.06
84842023-02-0139143914. 신도림쌍용플래티넘노블아파트 앞정기권F20대365.970.612617.3317
84852023-02-0139693969. 독산역 롯데캐슬 104동 앞정기권F20대271.600.562396.4221
84862023-02-0140414041. 공릉역3번출구정기권F20대495.040.843610.4323
84872023-02-0142554255. 래미안 루센티아 아파트 108동 앞정기권F20대118.600.20870.07
84882023-02-01241241. 신길우성1차아파트 앞 공원정기권F20대16.400.08329.845