Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Text3
DateTime2
Numeric3

Dataset

Description서울특별시 공공자전거 대여이력 정보입니다. 자전거 이동경로에 대한 데이터 분석이 가능하도록 최근 6개월 간 공공자전거의 대여이력 정보 중 위치정보를 포함한 데이터를 제공합니다.
Author서울특별시
URLhttps://www.data.go.kr/data/15099820/fileData.do

Reproduction

Analysis started2023-12-12 15:07:18.010050
Analysis finished2023-12-12 15:07:20.151314
Duration2.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct2547
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:07:20.403134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters14
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

Unique722 ?
Unique (%)7.2%

Sample

1st rowSPB-47079
2nd rowSPB-55778
3rd rowSPB-37707
4th rowSPB-43123
5th rowSPB-36162
ValueCountFrequency (%)
spb-58383 61
 
0.6%
spb-33503 53
 
0.5%
spb-40492 40
 
0.4%
spb-55962 40
 
0.4%
spb-52422 34
 
0.3%
spb-50027 30
 
0.3%
spb-55133 29
 
0.3%
spb-31107 26
 
0.3%
spb-41695 26
 
0.3%
spb-56102 25
 
0.2%
Other values (2537) 9636
96.4%
2023-12-13T00:07:20.903756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
5 8521
9.5%
3 6946
7.7%
4 6560
7.3%
8 4714
 
5.2%
0 4388
 
4.9%
2 4132
 
4.6%
Other values (4) 14739
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
55.6%
Uppercase Letter 30000
33.3%
Dash Punctuation 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 8521
17.0%
3 6946
13.9%
4 6560
13.1%
8 4714
9.4%
0 4388
8.8%
2 4132
8.3%
1 3877
7.8%
6 3772
7.5%
7 3602
7.2%
9 3488
7.0%
Uppercase Letter
ValueCountFrequency (%)
S 10000
33.3%
P 10000
33.3%
B 10000
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
66.7%
Latin 30000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 10000
16.7%
5 8521
14.2%
3 6946
11.6%
4 6560
10.9%
8 4714
7.9%
0 4388
7.3%
2 4132
6.9%
1 3877
 
6.5%
6 3772
 
6.3%
7 3602
 
6.0%
Latin
ValueCountFrequency (%)
S 10000
33.3%
P 10000
33.3%
B 10000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
5 8521
9.5%
3 6946
7.7%
4 6560
7.3%
8 4714
 
5.2%
0 4388
 
4.9%
2 4132
 
4.6%
Other values (4) 14739
16.4%

대여일시
Date

Distinct515
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-03-01 00:00:00
Maximum2022-03-01 10:27:00
2023-12-13T00:07:21.101269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:21.282939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여대여소명
Text

Distinct1536
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:07:21.583644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length15.432
Min length7

Characters and Unicode

Total characters154320
Distinct characters510
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)2.6%

Sample

1st row2172. 나들목공원
2nd row2611. 송파지역자활센터 뒤
3rd row820. 청파동입구 교차로
4th row735. 영도초등학교
5th row215. 여의도고교 앞
ValueCountFrequency (%)
앞 2569
 
8.7%
1번출구 526
 
1.8%
출구 480
 
1.6%
옆 374
 
1.3%
교차로 210
 
0.7%
2번출구 209
 
0.7%
입구 204
 
0.7%
3번출구 192
 
0.6%
뒤 189
 
0.6%
4번출구 183
 
0.6%
Other values (3164) 24492
82.7%
2023-12-13T00:07:22.101086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19807
 
12.8%
. 10033
 
6.5%
1 8603
 
5.6%
2 6150
 
4.0%
4 4376
 
2.8%
3 4152
 
2.7%
0 3715
 
2.4%
구 3648
 
2.4%
5 3634
 
2.4%
7 3487
 
2.3%
Other values (500) 86715
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79267
51.4%
Decimal Number 42239
27.4%
Space Separator 19807
 
12.8%
Other Punctuation 10084
 
6.5%
Uppercase Letter 1031
 
0.7%
Close Punctuation 794
 
0.5%
Open Punctuation 794
 
0.5%
Lowercase Letter 180
 
0.1%
Dash Punctuation 72
 
< 0.1%
Connector Punctuation 33
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3648
 
4.6%
앞 3037
 
3.8%
역 2864
 
3.6%
번 2571
 
3.2%
출 2527
 
3.2%
동 2180
 
2.8%
교 1749
 
2.2%
파 1335
 
1.7%
원 1294
 
1.6%
트 1247
 
1.6%
Other values (448) 56815
71.7%
Uppercase Letter
ValueCountFrequency (%)
K 181
17.6%
S 130
12.6%
C 102
9.9%
B 88
8.5%
T 78
7.6%
D 75
7.3%
M 67
 
6.5%
A 60
 
5.8%
P 47
 
4.6%
G 40
 
3.9%
Other values (11) 163
15.8%
Lowercase Letter
ValueCountFrequency (%)
e 55
30.6%
s 30
16.7%
k 18
 
10.0%
f 16
 
8.9%
r 16
 
8.9%
h 16
 
8.9%
t 8
 
4.4%
v 5
 
2.8%
m 4
 
2.2%
o 4
 
2.2%
Other values (2) 8
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 8603
20.4%
2 6150
14.6%
4 4376
10.4%
3 4152
9.8%
0 3715
8.8%
5 3634
8.6%
7 3487
8.3%
6 3152
 
7.5%
8 2626
 
6.2%
9 2344
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 10033
99.5%
, 48
 
0.5%
& 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
19807
100.0%
Close Punctuation
ValueCountFrequency (%)
) 794
100.0%
Open Punctuation
ValueCountFrequency (%)
( 794
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 33
100.0%
Math Symbol
ValueCountFrequency (%)
~ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79267
51.4%
Common 73842
47.8%
Latin 1211
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3648
 
4.6%
앞 3037
 
3.8%
역 2864
 
3.6%
번 2571
 
3.2%
출 2527
 
3.2%
동 2180
 
2.8%
교 1749
 
2.2%
파 1335
 
1.7%
원 1294
 
1.6%
트 1247
 
1.6%
Other values (448) 56815
71.7%
Latin
ValueCountFrequency (%)
K 181
14.9%
S 130
 
10.7%
C 102
 
8.4%
B 88
 
7.3%
T 78
 
6.4%
D 75
 
6.2%
M 67
 
5.5%
A 60
 
5.0%
e 55
 
4.5%
P 47
 
3.9%
Other values (23) 328
27.1%
Common
ValueCountFrequency (%)
19807
26.8%
. 10033
13.6%
1 8603
11.7%
2 6150
 
8.3%
4 4376
 
5.9%
3 4152
 
5.6%
0 3715
 
5.0%
5 3634
 
4.9%
7 3487
 
4.7%
6 3152
 
4.3%
Other values (9) 6733
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79267
51.4%
ASCII 75053
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19807
26.4%
. 10033
13.4%
1 8603
11.5%
2 6150
 
8.2%
4 4376
 
5.8%
3 4152
 
5.5%
0 3715
 
4.9%
5 3634
 
4.8%
7 3487
 
4.6%
6 3152
 
4.2%
Other values (42) 7944
10.6%
Hangul
ValueCountFrequency (%)
구 3648
 
4.6%
앞 3037
 
3.8%
역 2864
 
3.6%
번 2571
 
3.2%
출 2527
 
3.2%
동 2180
 
2.8%
교 1749
 
2.2%
파 1335
 
1.7%
원 1294
 
1.6%
트 1247
 
1.6%
Other values (448) 56815
71.7%

반납일시
Date

Distinct525
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-03-01 00:03:00
Maximum2022-03-01 10:28:00
2023-12-13T00:07:22.280077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:22.450260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반납대여소명
Text

Distinct1485
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:07:22.762486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length15.1966
Min length7

Characters and Unicode

Total characters151966
Distinct characters516
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

Unique261 ?
Unique (%)2.6%

Sample

1st row247. 당산역 10번출구 앞
2nd row1204. 거여역 3번출구
3rd row820. 청파동입구 교차로
4th row735. 영도초등학교
5th row1297. 석촌호수교차로(동호 팔각정 앞)
ValueCountFrequency (%)
앞 2631
 
9.0%
1번출구 541
 
1.8%
출구 523
 
1.8%
교차로 333
 
1.1%
옆 332
 
1.1%
뒤 248
 
0.8%
사거리 226
 
0.8%
5번출구 222
 
0.8%
3번출구 189
 
0.6%
2번출구 186
 
0.6%
Other values (3046) 23849
81.5%
2023-12-13T00:07:23.195652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19409
 
12.8%
. 10014
 
6.6%
1 8289
 
5.5%
2 6034
 
4.0%
3 4392
 
2.9%
4 4177
 
2.7%
0 4012
 
2.6%
구 3904
 
2.6%
5 3669
 
2.4%
7 3268
 
2.2%
Other values (506) 84798
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77930
51.3%
Decimal Number 41769
27.5%
Space Separator 19409
 
12.8%
Other Punctuation 10086
 
6.6%
Uppercase Letter 993
 
0.7%
Close Punctuation 773
 
0.5%
Open Punctuation 773
 
0.5%
Lowercase Letter 111
 
0.1%
Dash Punctuation 85
 
0.1%
Math Symbol 20
 
< 0.1%
Other values (3) 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3904
 
5.0%
역 3034
 
3.9%
앞 3007
 
3.9%
번 2803
 
3.6%
출 2784
 
3.6%
동 2273
 
2.9%
교 1876
 
2.4%
리 1308
 
1.7%
원 1184
 
1.5%
파 1167
 
1.5%
Other values (452) 54590
70.1%
Uppercase Letter
ValueCountFrequency (%)
S 177
17.8%
K 120
12.1%
T 98
9.9%
C 85
8.6%
D 67
 
6.7%
B 64
 
6.4%
A 60
 
6.0%
G 49
 
4.9%
I 44
 
4.4%
L 41
 
4.1%
Other values (10) 188
18.9%
Lowercase Letter
ValueCountFrequency (%)
e 40
36.0%
s 21
18.9%
k 15
 
13.5%
v 8
 
7.2%
f 7
 
6.3%
r 7
 
6.3%
h 7
 
6.3%
t 2
 
1.8%
c 1
 
0.9%
m 1
 
0.9%
Other values (2) 2
 
1.8%
Decimal Number
ValueCountFrequency (%)
1 8289
19.8%
2 6034
14.4%
3 4392
10.5%
4 4177
10.0%
0 4012
9.6%
5 3669
8.8%
7 3268
 
7.8%
6 3143
 
7.5%
8 2551
 
6.1%
9 2234
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 10014
99.3%
, 53
 
0.5%
& 14
 
0.1%
· 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
19409
100.0%
Close Punctuation
ValueCountFrequency (%)
) 773
100.0%
Open Punctuation
ValueCountFrequency (%)
( 773
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 85
100.0%
Math Symbol
ValueCountFrequency (%)
~ 20
100.0%
Other Number
ValueCountFrequency (%)
① 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77932
51.3%
Common 72930
48.0%
Latin 1104
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3904
 
5.0%
역 3034
 
3.9%
앞 3007
 
3.9%
번 2803
 
3.6%
출 2784
 
3.6%
동 2273
 
2.9%
교 1876
 
2.4%
리 1308
 
1.7%
원 1184
 
1.5%
파 1167
 
1.5%
Other values (453) 54592
70.1%
Latin
ValueCountFrequency (%)
S 177
16.0%
K 120
10.9%
T 98
 
8.9%
C 85
 
7.7%
D 67
 
6.1%
B 64
 
5.8%
A 60
 
5.4%
G 49
 
4.4%
I 44
 
4.0%
L 41
 
3.7%
Other values (22) 299
27.1%
Common
ValueCountFrequency (%)
19409
26.6%
. 10014
13.7%
1 8289
11.4%
2 6034
 
8.3%
3 4392
 
6.0%
4 4177
 
5.7%
0 4012
 
5.5%
5 3669
 
5.0%
7 3268
 
4.5%
6 3143
 
4.3%
Other values (11) 6523
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77930
51.3%
ASCII 74020
48.7%
Enclosed Alphanum 9
 
< 0.1%
None 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19409
26.2%
. 10014
13.5%
1 8289
11.2%
2 6034
 
8.2%
3 4392
 
5.9%
4 4177
 
5.6%
0 4012
 
5.4%
5 3669
 
5.0%
7 3268
 
4.4%
6 3143
 
4.2%
Other values (41) 7613
 
10.3%
Hangul
ValueCountFrequency (%)
구 3904
 
5.0%
역 3034
 
3.9%
앞 3007
 
3.9%
번 2803
 
3.6%
출 2784
 
3.6%
동 2273
 
2.9%
교 1876
 
2.4%
리 1308
 
1.7%
원 1184
 
1.5%
파 1167
 
1.5%
Other values (452) 54590
70.1%
Enclosed Alphanum
ValueCountFrequency (%)
① 9
100.0%
None
ValueCountFrequency (%)
· 5
71.4%
㈜ 2
 
28.6%

이동순서
Real number (ℝ)

Distinct229
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5615
Minimum1
Maximum509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:07:23.339703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median13
Q329
95-th percentile75
Maximum509
Range508
Interquartile range (IQR)23

Descriptive statistics

Standard deviation34.48144
Coefficient of variation (CV)1.4634654
Kurtosis47.874936
Mean23.5615
Median Absolute Deviation (MAD)9
Skewness5.5316454
Sum235615
Variance1188.9697
MonotonicityNot monotonic
2023-12-13T00:07:23.487351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 518
 
5.2%
3 517
 
5.2%
4 491
 
4.9%
2 491
 
4.9%
5 472
 
4.7%
6 413
 
4.1%
7 385
 
3.9%
8 361
 
3.6%
10 350
 
3.5%
9 323
 
3.2%
Other values (219) 5679
56.8%
ValueCountFrequency (%)
1 518
5.2%
2 491
4.9%
3 517
5.2%
4 491
4.9%
5 472
4.7%
6 413
4.1%
7 385
3.9%
8 361
3.6%
9 323
3.2%
10 350
3.5%
ValueCountFrequency (%)
509 1
< 0.1%
492 1
< 0.1%
472 1
< 0.1%
449 1
< 0.1%
441 1
< 0.1%
434 1
< 0.1%
418 1
< 0.1%
417 1
< 0.1%
413 1
< 0.1%
401 1
< 0.1%

위도
Real number (ℝ)

Distinct8684
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.549718
Minimum37.432236
Maximum37.688595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:07:23.620963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.432236
5-th percentile37.482325
Q137.516043
median37.545998
Q337.574679
95-th percentile37.642896
Maximum37.688595
Range0.256359
Interquartile range (IQR)0.05863575

Descriptive statistics

Standard deviation0.046140914
Coefficient of variation (CV)0.0012287952
Kurtosis-0.14828799
Mean37.549718
Median Absolute Deviation (MAD)0.0294475
Skewness0.50262117
Sum375497.18
Variance0.0021289839
MonotonicityNot monotonic
2023-12-13T00:07:23.755831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.554611 5
 
0.1%
37.50935 5
 
0.1%
37.554565 5
 
0.1%
37.505104 5
 
0.1%
37.556862 5
 
0.1%
37.530254 5
 
0.1%
37.505116 5
 
0.1%
37.509281 4
 
< 0.1%
37.505119 4
 
< 0.1%
37.543053 4
 
< 0.1%
Other values (8674) 9953
99.5%
ValueCountFrequency (%)
37.432236 1
< 0.1%
37.439243 1
< 0.1%
37.444752 1
< 0.1%
37.444759 1
< 0.1%
37.445683 1
< 0.1%
37.4482 1
< 0.1%
37.448944 1
< 0.1%
37.450581 1
< 0.1%
37.450809 1
< 0.1%
37.451004 1
< 0.1%
ValueCountFrequency (%)
37.688595 1
< 0.1%
37.686985 1
< 0.1%
37.685638 1
< 0.1%
37.682575 1
< 0.1%
37.68248 1
< 0.1%
37.682404 1
< 0.1%
37.680538 1
< 0.1%
37.680443 1
< 0.1%
37.680035 1
< 0.1%
37.679993 1
< 0.1%

경도
Real number (ℝ)

Distinct8550
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98801
Minimum126.79851
Maximum127.1846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:07:24.158999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.79851
5-th percentile126.84145
Q1126.90847
median126.99786
Q3127.0666
95-th percentile127.12997
Maximum127.1846
Range0.386086
Interquartile range (IQR)0.15813125

Descriptive statistics

Standard deviation0.092440912
Coefficient of variation (CV)0.00072794994
Kurtosis-1.1880687
Mean126.98801
Median Absolute Deviation (MAD)0.078186
Skewness-0.015966145
Sum1269880.1
Variance0.0085453222
MonotonicityNot monotonic
2023-12-13T00:07:24.293045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.944 7
 
0.1%
126.943993 7
 
0.1%
127.145813 7
 
0.1%
127.071159 6
 
0.1%
127.093025 6
 
0.1%
126.910789 5
 
0.1%
127.145798 5
 
0.1%
127.071136 5
 
0.1%
127.066605 5
 
0.1%
127.081947 4
 
< 0.1%
Other values (8540) 9943
99.4%
ValueCountFrequency (%)
126.798515 1
< 0.1%
126.798843 1
< 0.1%
126.799706 1
< 0.1%
126.801414 1
< 0.1%
126.804695 1
< 0.1%
126.805313 1
< 0.1%
126.805672 1
< 0.1%
126.807137 1
< 0.1%
126.807434 1
< 0.1%
126.807625 1
< 0.1%
ValueCountFrequency (%)
127.184601 1
< 0.1%
127.184593 1
< 0.1%
127.18457 1
< 0.1%
127.184563 2
< 0.1%
127.18454 1
< 0.1%
127.184532 1
< 0.1%
127.184486 1
< 0.1%
127.184479 1
< 0.1%
127.184471 1
< 0.1%
127.184441 1
< 0.1%

Interactions

2023-12-13T00:07:19.522054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:18.900302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:19.231692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:19.649037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:19.015523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:19.337377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:19.752318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:19.121498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:07:19.422959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:07:24.385251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이동순서위도경도
이동순서1.0000.1410.200
위도0.1411.0000.623
경도0.2000.6231.000
2023-12-13T00:07:24.478225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이동순서위도경도
이동순서1.000-0.042-0.004
위도-0.0421.0000.104
경도-0.0040.1041.000

Missing values

2023-12-13T00:07:19.930673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:07:20.092647image/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

자전거번호대여일시대여대여소명반납일시반납대여소명이동순서위도경도
61915SPB-470792022-03-01 07:492172. 나들목공원2022-03-01 08:56247. 당산역 10번출구 앞2137.501865126.894341
14868SPB-557782022-03-01 00:262611. 송파지역자활센터 뒤2022-03-01 01:111204. 거여역 3번출구4037.499123127.141426
37290SPB-377072022-03-01 00:49820. 청파동입구 교차로2022-03-01 02:19820. 청파동입구 교차로2337.523071126.96183
14889SPB-431232022-03-01 00:20735. 영도초등학교2022-03-01 01:11735. 영도초등학교437.535625126.876511
50105SPB-361622022-03-01 02:22215. 여의도고교 앞2022-03-01 04:171297. 석촌호수교차로(동호 팔각정 앞)2137.51762126.947723
18380SPB-359152022-03-01 00:591901. 신도림동주민센터 앞2022-03-01 01:212009. 보라매역 8번출구1637.50016126.910873
12870SPB-411822022-03-01 00:02207. 여의나루역 1번출구 앞2022-03-01 01:05207. 여의나루역 1번출구 앞3837.51923126.941673
73673SPB-530492022-03-01 08:50609. 제기2교2022-03-01 09:524113. 한국국방연구원 앞537.586349127.032242
19767SPB-583932022-03-01 00:16502. 뚝섬유원지역 1번출구 앞2022-03-01 01:253528. 광진정보도서관5137.561192127.115288
68820SPB-342622022-03-01 09:22400. 상암한화오벨리스크 1차 앞2022-03-01 09:34408. LG CNS앞1237.580853126.886238
자전거번호대여일시대여대여소명반납일시반납대여소명이동순서위도경도
63166SPB-586582022-03-01 08:531221. 삼전역 4번출구2022-03-01 09:042420.학여울역 사거리(LG베스트샵 앞)1037.497314127.072273
32862SPB-563352022-03-01 01:49626. 군자교 서측 녹지대2022-03-01 02:041452. 겸재교 진입부1537.585949127.074478
30753SPB-578852022-03-01 01:021249. 아주중학교건너편2022-03-01 01:572324. 천주교 대치 2동 교회 옆2537.499893127.102364
39699SPB-389262022-03-01 01:56576. 광나루역 3번 출구2022-03-01 02:323658. 프라이어팰리스아파트1237.547722127.128815
14844SPB-557782022-03-01 00:262611. 송파지역자활센터 뒤2022-03-01 01:111204. 거여역 3번출구1637.519169127.11393
16603SPB-336412022-03-01 00:283782. 겸재정선미술관2022-03-01 01:164561. 양평역 1번출구637.576694126.837814
37526SPB-525312022-03-01 02:094011. 월계 센트럴 아이파크2022-03-01 02:191605. 헬스케어437.631348127.058891
22681SPB-378232022-03-01 01:223504. 원일교회2022-03-01 01:33588. 뚝섬 유원지역537.539165127.072304
41343SPB-426002022-03-01 02:244565. 영등포 신세계백화점2022-03-01 02:42225. 앙카라공원 앞137.517807126.903412
58565SPB-470742022-03-01 08:17285. 대림3동사거리(하나은행)2022-03-01 08:301812. 갑을그레이트밸리 앞1137.481831126.886871