Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory996.1 KiB
Average record size in memory102.0 B

Variable types

Text3
DateTime2
Numeric6

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15182/F/1/datasetView.do

Alerts

대여 대여소번호 is highly overall correlated with 반납대여소번호High correlation
대여거치대 is highly overall correlated with 반납거치대 and 1 other fieldsHigh correlation
반납대여소번호 is highly overall correlated with 대여 대여소번호High correlation
반납거치대 is highly overall correlated with 대여거치대 and 1 other fieldsHigh correlation
이용거리 is highly overall correlated with 대여거치대 and 1 other fieldsHigh correlation
대여거치대 has 6595 (66.0%) zerosZeros
반납거치대 has 6595 (66.0%) zerosZeros
이용거리 has 7060 (70.6%) zerosZeros

Reproduction

Analysis started2023-12-11 07:32:04.157542
Analysis finished2023-12-11 07:32:11.172734
Duration7.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자전거번호
Text

Distinct6121
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:32:11.429514image/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

Unique3535 ?
Unique (%)35.4%

Sample

1st rowSPB-30499
2nd rowSPB-31647
3rd rowSPB-16317
4th rowSPB-35804
5th rowSPB-36278
ValueCountFrequency (%)
spb-36382 7
 
0.1%
spb-18428 7
 
0.1%
spb-34449 6
 
0.1%
spb-32609 6
 
0.1%
spb-09631 6
 
0.1%
spb-32316 6
 
0.1%
spb-34183 6
 
0.1%
spb-32633 6
 
0.1%
spb-35130 6
 
0.1%
spb-18502 6
 
0.1%
Other values (6111) 9938
99.4%
2023-12-11T16:32:12.001913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 11001
12.2%
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
1 5943
6.6%
2 5231
 
5.8%
0 4968
 
5.5%
6 4341
 
4.8%
5 4135
 
4.6%
Other values (4) 14381
16.0%

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 (%)
3 11001
22.0%
1 5943
11.9%
2 5231
10.5%
0 4968
9.9%
6 4341
 
8.7%
5 4135
 
8.3%
4 3927
 
7.9%
7 3798
 
7.6%
8 3451
 
6.9%
9 3205
 
6.4%
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 (%)
3 11001
18.3%
- 10000
16.7%
1 5943
9.9%
2 5231
8.7%
0 4968
8.3%
6 4341
 
7.2%
5 4135
 
6.9%
4 3927
 
6.5%
7 3798
 
6.3%
8 3451
 
5.8%
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 (%)
3 11001
12.2%
S 10000
11.1%
P 10000
11.1%
B 10000
11.1%
- 10000
11.1%
1 5943
6.6%
2 5231
 
5.8%
0 4968
 
5.5%
6 4341
 
4.8%
5 4135
 
4.6%
Other values (4) 14381
16.0%

대여일시
Date

Distinct8976
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-06-01 00:00:40
Maximum2020-06-01 20:38:38
2023-12-11T16:32:12.177047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:12.376913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여 대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1572
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1232.7421
Minimum10
Maximum3560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:12.585912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile166
Q1505
median1141
Q31846.25
95-th percentile2701
Maximum3560
Range3550
Interquartile range (IQR)1341.25

Descriptive statistics

Standard deviation860.78753
Coefficient of variation (CV)0.69827057
Kurtosis-0.10759635
Mean1232.7421
Median Absolute Deviation (MAD)665
Skewness0.72043501
Sum12327421
Variance740955.18
MonotonicityNot monotonic
2023-12-11T16:32:12.811240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 54
 
0.5%
502 49
 
0.5%
2701 44
 
0.4%
152 40
 
0.4%
1906 37
 
0.4%
2219 36
 
0.4%
113 36
 
0.4%
2102 35
 
0.4%
1160 35
 
0.4%
2002 34
 
0.3%
Other values (1562) 9600
96.0%
ValueCountFrequency (%)
10 4
 
< 0.1%
101 2
 
< 0.1%
102 17
0.2%
103 11
0.1%
104 13
0.1%
105 2
 
< 0.1%
106 21
0.2%
107 15
0.1%
108 6
 
0.1%
109 12
0.1%
ValueCountFrequency (%)
3560 4
< 0.1%
3559 8
0.1%
3558 4
< 0.1%
3553 3
 
< 0.1%
3552 1
 
< 0.1%
3551 2
 
< 0.1%
3550 2
 
< 0.1%
3545 5
0.1%
3543 2
 
< 0.1%
3542 6
0.1%

대여 대여소명
Text

Distinct1572
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:32:13.201628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length23
Mean length9.9497
Min length2

Characters and Unicode

Total characters99497
Distinct characters523
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

Unique177 ?
Unique (%)1.8%

Sample

1st row하계2동 공항버스정류장 옆
2nd row르네상스호텔사거리 역삼지하보도 2번출구
3rd row삼청동 골목
4th row대명초교 입구 교차로
5th row방학사거리 (봄마당 앞)
ValueCountFrequency (%)
앞 2712
 
12.4%
1번출구 531
 
2.4%
출구 526
 
2.4%
옆 499
 
2.3%
2번출구 288
 
1.3%
3번출구 288
 
1.3%
뒤 274
 
1.2%
사거리 238
 
1.1%
5번출구 229
 
1.0%
교차로 213
 
1.0%
Other values (1885) 16161
73.6%
2023-12-11T16:32:13.820811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11976
 
12.0%
구 4194
 
4.2%
역 3431
 
3.4%
앞 3250
 
3.3%
번 3148
 
3.2%
출 3057
 
3.1%
동 1718
 
1.7%
교 1623
 
1.6%
1 1610
 
1.6%
리 1341
 
1.3%
Other values (513) 64149
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78682
79.1%
Space Separator 11976
 
12.0%
Decimal Number 5254
 
5.3%
Uppercase Letter 1366
 
1.4%
Open Punctuation 891
 
0.9%
Close Punctuation 891
 
0.9%
Lowercase Letter 144
 
0.1%
Dash Punctuation 142
 
0.1%
Other Punctuation 120
 
0.1%
Math Symbol 17
 
< 0.1%
Other values (2) 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4194
 
5.3%
역 3431
 
4.4%
앞 3250
 
4.1%
번 3148
 
4.0%
출 3057
 
3.9%
동 1718
 
2.2%
교 1623
 
2.1%
리 1341
 
1.7%
사 1150
 
1.5%
원 1145
 
1.5%
Other values (457) 54625
69.4%
Uppercase Letter
ValueCountFrequency (%)
K 176
12.9%
S 161
11.8%
C 150
11.0%
L 92
 
6.7%
G 89
 
6.5%
I 81
 
5.9%
B 80
 
5.9%
T 78
 
5.7%
M 75
 
5.5%
A 63
 
4.6%
Other values (14) 321
23.5%
Lowercase Letter
ValueCountFrequency (%)
e 43
29.9%
n 26
18.1%
l 17
 
11.8%
k 14
 
9.7%
y 13
 
9.0%
t 12
 
8.3%
s 6
 
4.2%
o 4
 
2.8%
c 4
 
2.8%
m 4
 
2.8%
Decimal Number
ValueCountFrequency (%)
1 1610
30.6%
2 972
18.5%
3 642
 
12.2%
4 470
 
8.9%
5 423
 
8.1%
8 276
 
5.3%
7 259
 
4.9%
6 219
 
4.2%
0 209
 
4.0%
9 174
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 98
81.7%
& 13
 
10.8%
? 9
 
7.5%
Math Symbol
ValueCountFrequency (%)
~ 14
82.4%
+ 3
 
17.6%
Space Separator
ValueCountFrequency (%)
11976
100.0%
Open Punctuation
ValueCountFrequency (%)
( 891
100.0%
Close Punctuation
ValueCountFrequency (%)
) 891
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 142
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78690
79.1%
Common 19297
 
19.4%
Latin 1510
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4194
 
5.3%
역 3431
 
4.4%
앞 3250
 
4.1%
번 3148
 
4.0%
출 3057
 
3.9%
동 1718
 
2.2%
교 1623
 
2.1%
리 1341
 
1.7%
사 1150
 
1.5%
원 1145
 
1.5%
Other values (458) 54633
69.4%
Latin
ValueCountFrequency (%)
K 176
 
11.7%
S 161
 
10.7%
C 150
 
9.9%
L 92
 
6.1%
G 89
 
5.9%
I 81
 
5.4%
B 80
 
5.3%
T 78
 
5.2%
M 75
 
5.0%
A 63
 
4.2%
Other values (25) 465
30.8%
Common
ValueCountFrequency (%)
11976
62.1%
1 1610
 
8.3%
2 972
 
5.0%
( 891
 
4.6%
) 891
 
4.6%
3 642
 
3.3%
4 470
 
2.4%
5 423
 
2.2%
8 276
 
1.4%
7 259
 
1.3%
Other values (10) 887
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78682
79.1%
ASCII 20807
 
20.9%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11976
57.6%
1 1610
 
7.7%
2 972
 
4.7%
( 891
 
4.3%
) 891
 
4.3%
3 642
 
3.1%
4 470
 
2.3%
5 423
 
2.0%
8 276
 
1.3%
7 259
 
1.2%
Other values (45) 2397
 
11.5%
Hangul
ValueCountFrequency (%)
구 4194
 
5.3%
역 3431
 
4.4%
앞 3250
 
4.1%
번 3148
 
4.0%
출 3057
 
3.9%
동 1718
 
2.2%
교 1623
 
2.1%
리 1341
 
1.7%
사 1150
 
1.5%
원 1145
 
1.5%
Other values (457) 54625
69.4%
None
ValueCountFrequency (%)
㈜ 8
100.0%

대여거치대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4685
Minimum0
Maximum39
Zeros6595
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:14.036265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile13
Maximum39
Range39
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.6854672
Coefficient of variation (CV)1.898103
Kurtosis7.5481549
Mean2.4685
Median Absolute Deviation (MAD)0
Skewness2.4754851
Sum24685
Variance21.953603
MonotonicityNot monotonic
2023-12-11T16:32:14.260567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 6595
66.0%
1 369
 
3.7%
2 300
 
3.0%
3 298
 
3.0%
4 286
 
2.9%
5 268
 
2.7%
7 264
 
2.6%
6 251
 
2.5%
10 247
 
2.5%
8 234
 
2.3%
Other values (29) 888
 
8.9%
ValueCountFrequency (%)
0 6595
66.0%
1 369
 
3.7%
2 300
 
3.0%
3 298
 
3.0%
4 286
 
2.9%
5 268
 
2.7%
6 251
 
2.5%
7 264
 
2.6%
8 234
 
2.3%
9 220
 
2.2%
ValueCountFrequency (%)
39 1
 
< 0.1%
37 2
 
< 0.1%
36 1
 
< 0.1%
35 2
 
< 0.1%
34 3
< 0.1%
33 4
< 0.1%
32 4
< 0.1%
31 1
 
< 0.1%
30 4
< 0.1%
29 5
0.1%

반납일시
Date

Distinct8938
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-06-01 00:08:34
Maximum2020-06-01 20:43:10
2023-12-11T16:32:14.480514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:14.710266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반납대여소번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1533
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1239.1167
Minimum10
Maximum3560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:14.943229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile161
Q1512
median1149
Q31834
95-th percentile2702.45
Maximum3560
Range3550
Interquartile range (IQR)1322

Descriptive statistics

Standard deviation860.06225
Coefficient of variation (CV)0.69409302
Kurtosis-0.092359364
Mean1239.1167
Median Absolute Deviation (MAD)647
Skewness0.7194594
Sum12391167
Variance739707.08
MonotonicityNot monotonic
2023-12-11T16:32:15.130846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 61
 
0.6%
2701 51
 
0.5%
502 49
 
0.5%
2219 40
 
0.4%
1210 39
 
0.4%
152 37
 
0.4%
210 35
 
0.4%
1906 34
 
0.3%
1009 32
 
0.3%
113 31
 
0.3%
Other values (1523) 9591
95.9%
ValueCountFrequency (%)
10 4
 
< 0.1%
101 3
 
< 0.1%
102 15
0.1%
103 12
0.1%
104 9
 
0.1%
105 4
 
< 0.1%
106 27
0.3%
107 24
0.2%
108 11
0.1%
109 10
 
0.1%
ValueCountFrequency (%)
3560 2
 
< 0.1%
3559 7
 
0.1%
3553 1
 
< 0.1%
3552 3
 
< 0.1%
3545 5
 
0.1%
3543 3
 
< 0.1%
3542 7
 
0.1%
3541 27
0.3%
3539 1
 
< 0.1%
3538 7
 
0.1%

반납대여소명
Text

Distinct1533
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T16:32:15.439778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length23
Mean length9.9564
Min length2

Characters and Unicode

Total characters99564
Distinct characters519
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

Unique208 ?
Unique (%)2.1%

Sample

1st row과기대 입구(우)
2nd row가락시장역 롯데마트앞
3rd row종로구청 옆
4th row대명초교 입구 교차로
5th row노원역1번출구
ValueCountFrequency (%)
앞 2646
 
12.1%
1번출구 528
 
2.4%
출구 512
 
2.3%
옆 486
 
2.2%
뒤 293
 
1.3%
2번출구 289
 
1.3%
3번출구 270
 
1.2%
사거리 269
 
1.2%
5번출구 238
 
1.1%
4번출구 216
 
1.0%
Other values (1833) 16087
73.7%
2023-12-11T16:32:16.049055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11843
 
11.9%
구 4178
 
4.2%
역 3425
 
3.4%
앞 3186
 
3.2%
번 3151
 
3.2%
출 3081
 
3.1%
동 1753
 
1.8%
1 1637
 
1.6%
교 1532
 
1.5%
리 1427
 
1.4%
Other values (509) 64351
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78823
79.2%
Space Separator 11843
 
11.9%
Decimal Number 5337
 
5.4%
Uppercase Letter 1307
 
1.3%
Open Punctuation 910
 
0.9%
Close Punctuation 910
 
0.9%
Lowercase Letter 149
 
0.1%
Dash Punctuation 134
 
0.1%
Other Punctuation 116
 
0.1%
Math Symbol 18
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4178
 
5.3%
역 3425
 
4.3%
앞 3186
 
4.0%
번 3151
 
4.0%
출 3081
 
3.9%
동 1753
 
2.2%
교 1532
 
1.9%
리 1427
 
1.8%
사 1170
 
1.5%
원 1140
 
1.4%
Other values (453) 54780
69.5%
Uppercase Letter
ValueCountFrequency (%)
S 162
12.4%
K 162
12.4%
C 150
11.5%
G 93
 
7.1%
L 92
 
7.0%
I 82
 
6.3%
T 76
 
5.8%
M 68
 
5.2%
B 66
 
5.0%
D 51
 
3.9%
Other values (13) 305
23.3%
Lowercase Letter
ValueCountFrequency (%)
e 42
28.2%
n 38
25.5%
l 21
14.1%
y 19
12.8%
k 10
 
6.7%
t 10
 
6.7%
s 2
 
1.3%
m 2
 
1.3%
o 2
 
1.3%
c 2
 
1.3%
Decimal Number
ValueCountFrequency (%)
1 1637
30.7%
2 1031
19.3%
3 642
 
12.0%
4 499
 
9.3%
5 400
 
7.5%
8 278
 
5.2%
7 256
 
4.8%
6 225
 
4.2%
0 208
 
3.9%
9 161
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 89
76.7%
& 19
 
16.4%
? 7
 
6.0%
· 1
 
0.9%
Math Symbol
ValueCountFrequency (%)
~ 14
77.8%
+ 4
 
22.2%
Space Separator
ValueCountFrequency (%)
11843
100.0%
Open Punctuation
ValueCountFrequency (%)
( 910
100.0%
Close Punctuation
ValueCountFrequency (%)
) 910
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78832
79.2%
Common 19276
 
19.4%
Latin 1456
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4178
 
5.3%
역 3425
 
4.3%
앞 3186
 
4.0%
번 3151
 
4.0%
출 3081
 
3.9%
동 1753
 
2.2%
교 1532
 
1.9%
리 1427
 
1.8%
사 1170
 
1.5%
원 1140
 
1.4%
Other values (454) 54789
69.5%
Latin
ValueCountFrequency (%)
S 162
 
11.1%
K 162
 
11.1%
C 150
 
10.3%
G 93
 
6.4%
L 92
 
6.3%
I 82
 
5.6%
T 76
 
5.2%
M 68
 
4.7%
B 66
 
4.5%
D 51
 
3.5%
Other values (24) 454
31.2%
Common
ValueCountFrequency (%)
11843
61.4%
1 1637
 
8.5%
2 1031
 
5.3%
( 910
 
4.7%
) 910
 
4.7%
3 642
 
3.3%
4 499
 
2.6%
5 400
 
2.1%
8 278
 
1.4%
7 256
 
1.3%
Other values (11) 870
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78823
79.2%
ASCII 20731
 
20.8%
None 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11843
57.1%
1 1637
 
7.9%
2 1031
 
5.0%
( 910
 
4.4%
) 910
 
4.4%
3 642
 
3.1%
4 499
 
2.4%
5 400
 
1.9%
8 278
 
1.3%
7 256
 
1.2%
Other values (44) 2325
 
11.2%
Hangul
ValueCountFrequency (%)
구 4178
 
5.3%
역 3425
 
4.3%
앞 3186
 
4.0%
번 3151
 
4.0%
출 3081
 
3.9%
동 1753
 
2.2%
교 1532
 
1.9%
리 1427
 
1.8%
사 1170
 
1.5%
원 1140
 
1.4%
Other values (453) 54780
69.5%
None
ValueCountFrequency (%)
㈜ 9
90.0%
· 1
 
10.0%

반납거치대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4604
Minimum0
Maximum40
Zeros6595
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:16.256449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile13
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.6619315
Coefficient of variation (CV)1.894786
Kurtosis7.5077756
Mean2.4604
Median Absolute Deviation (MAD)0
Skewness2.4595745
Sum24604
Variance21.733605
MonotonicityNot monotonic
2023-12-11T16:32:16.474011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 6595
66.0%
1 382
 
3.8%
3 308
 
3.1%
2 300
 
3.0%
5 280
 
2.8%
7 270
 
2.7%
8 263
 
2.6%
6 259
 
2.6%
4 234
 
2.3%
9 214
 
2.1%
Other values (28) 895
 
8.9%
ValueCountFrequency (%)
0 6595
66.0%
1 382
 
3.8%
2 300
 
3.0%
3 308
 
3.1%
4 234
 
2.3%
5 280
 
2.8%
6 259
 
2.6%
7 270
 
2.7%
8 263
 
2.6%
9 214
 
2.1%
ValueCountFrequency (%)
40 2
 
< 0.1%
38 1
 
< 0.1%
37 1
 
< 0.1%
36 3
< 0.1%
35 2
 
< 0.1%
33 2
 
< 0.1%
31 2
 
< 0.1%
30 6
0.1%
29 3
< 0.1%
28 2
 
< 0.1%

이용시간
Real number (ℝ)

Distinct172
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.2298
Minimum1
Maximum397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:16.676602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q110
median19
Q340
95-th percentile92
Maximum397
Range396
Interquartile range (IQR)30

Descriptive statistics

Standard deviation28.281149
Coefficient of variation (CV)0.96754507
Kurtosis7.5431538
Mean29.2298
Median Absolute Deviation (MAD)12
Skewness2.0704596
Sum292298
Variance799.82337
MonotonicityNot monotonic
2023-12-11T16:32:16.879797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 387
 
3.9%
6 381
 
3.8%
7 368
 
3.7%
10 342
 
3.4%
9 341
 
3.4%
8 341
 
3.4%
11 314
 
3.1%
12 306
 
3.1%
4 303
 
3.0%
13 276
 
2.8%
Other values (162) 6641
66.4%
ValueCountFrequency (%)
1 19
 
0.2%
2 126
 
1.3%
3 233
2.3%
4 303
3.0%
5 387
3.9%
6 381
3.8%
7 368
3.7%
8 341
3.4%
9 341
3.4%
10 342
3.4%
ValueCountFrequency (%)
397 1
< 0.1%
275 1
< 0.1%
255 1
< 0.1%
239 1
< 0.1%
224 1
< 0.1%
218 1
< 0.1%
209 1
< 0.1%
203 2
< 0.1%
201 2
< 0.1%
198 1
< 0.1%

이용거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1015
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1244.2152
Minimum0
Maximum72600
Zeros7060
Zeros (%)70.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T16:32:17.096120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3950
95-th percentile6980.5
Maximum72600
Range72600
Interquartile range (IQR)950

Descriptive statistics

Standard deviation3460.7174
Coefficient of variation (CV)2.781446
Kurtosis78.840478
Mean1244.2152
Median Absolute Deviation (MAD)0
Skewness6.7606608
Sum12442152
Variance11976565
MonotonicityNot monotonic
2023-12-11T16:32:17.342850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7060
70.6%
850.0 17
 
0.2%
1210.0 16
 
0.2%
1030.0 15
 
0.1%
1410.0 14
 
0.1%
870.0 14
 
0.1%
990.0 14
 
0.1%
1100.0 13
 
0.1%
760.0 13
 
0.1%
2460.0 12
 
0.1%
Other values (1005) 2812
 
28.1%
ValueCountFrequency (%)
0.0 7060
70.6%
10.0 1
 
< 0.1%
20.0 1
 
< 0.1%
30.0 1
 
< 0.1%
40.0 3
 
< 0.1%
50.0 1
 
< 0.1%
60.0 1
 
< 0.1%
80.0 1
 
< 0.1%
90.0 1
 
< 0.1%
100.0 1
 
< 0.1%
ValueCountFrequency (%)
72600.0 1
< 0.1%
64800.0 1
< 0.1%
62140.0 1
< 0.1%
55930.0 1
< 0.1%
50530.0 1
< 0.1%
50300.0 1
< 0.1%
49110.0 1
< 0.1%
46750.0 1
< 0.1%
44150.0 1
< 0.1%
43680.0 1
< 0.1%

Interactions

2023-12-11T16:32:09.962294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:05.853293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:06.560201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:07.341136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:08.048151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:08.828991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:10.090033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:05.964550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:06.695680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:07.459366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:08.174228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:08.961742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:10.259559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:06.075967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:06.811033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:07.579741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:08.307353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:09.084372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:10.397382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:06.177350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:06.952185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:07.702731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:08.436933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:09.224581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:10.535052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:06.306321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:07.088974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:07.830465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:08.550498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:09.360528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:10.665343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:06.443399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:07.244983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:07.940491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:08.692091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:32:09.811419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:32:17.949322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.0000.1170.9230.0690.0720.056
대여거치대0.1171.0000.1020.6140.0000.264
반납대여소번호0.9230.1021.0000.0940.0590.030
반납거치대0.0690.6140.0941.0000.0400.348
이용시간0.0720.0000.0590.0401.0000.406
이용거리0.0560.2640.0300.3480.4061.000
2023-12-11T16:32:18.121230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 대여소번호대여거치대반납대여소번호반납거치대이용시간이용거리
대여 대여소번호1.000-0.0660.579-0.0630.006-0.054
대여거치대-0.0661.000-0.0610.948-0.0160.858
반납대여소번호0.579-0.0611.000-0.0580.015-0.045
반납거치대-0.0630.948-0.0581.000-0.0160.857
이용시간0.006-0.0160.015-0.0161.0000.086
이용거리-0.0540.858-0.0450.8570.0861.000

Missing values

2023-12-11T16:32:10.870361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:32:11.076716image/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

자전거번호대여일시대여 대여소번호대여 대여소명대여거치대반납일시반납대여소번호반납대여소명반납거치대이용시간이용거리
9320SPB-304992020-06-01 07:55:251616하계2동 공항버스정류장 옆02020-06-01 08:18:261611과기대 입구(우)0230.0
44774SPB-316472020-06-01 16:38:482329르네상스호텔사거리 역삼지하보도 2번출구02020-06-01 17:29:491257가락시장역 롯데마트앞0510.0
14235SPB-163172020-06-01 08:55:21464삼청동 골목92020-06-01 09:02:43305종로구청 옆1371480.0
22636SPB-358042020-06-01 11:58:071026대명초교 입구 교차로02020-06-01 12:06:571026대명초교 입구 교차로090.0
32521SPB-362782020-06-01 14:40:561733방학사거리 (봄마당 앞)02020-06-01 14:58:451653노원역1번출구0180.0
69268SPB-160142020-06-01 19:55:341214오금역 7번 출구 인근12020-06-01 20:05:501282송파소방서 맞은편(성내4교)491670.0
58257SPB-317492020-06-01 18:36:191925동양미래대학교 정문 옆02020-06-01 18:55:411925동양미래대학교 정문 옆0190.0
13952SPB-322012020-06-01 08:42:333535중곡사거리(국민은행)02020-06-01 08:58:39668서울축산농협(장안지점)0160.0
52050SPB-373572020-06-01 18:19:02565옥수역 3번출구02020-06-01 18:21:11565옥수역 3번출구020.0
71746SPB-116942020-06-01 20:14:262228뒷벌공원 옆192020-06-01 20:24:082220반포본동 주민센터 앞791270.0
자전거번호대여일시대여 대여소번호대여 대여소명대여거치대반납일시반납대여소번호반납대여소명반납거치대이용시간이용거리
73572SPB-331402020-06-01 20:31:55907CJ 드림시티02020-06-01 20:37:40956응암시장교차로060.0
54257SPB-079022020-06-01 18:26:541221삼전사거리 포스코더샵32020-06-01 18:33:392619석촌고분역 4번출구86770.0
41386SPB-065342020-06-01 16:22:18593자양중앙나들목42020-06-01 16:57:213534건대입구역 5번출구 뒤5344820.0
57431SPB-344562020-06-01 18:21:48151망원1동주민센터02020-06-01 18:51:14118광흥창역 2번출구 앞0290.0
21878SPB-207582020-06-01 11:30:21417DMC역 2번출구 옆122020-06-01 11:50:39182망원2빗물펌프장 앞1194150.0
22705SPB-352852020-06-01 12:02:201332석계역 5번출구 건너편02020-06-01 12:08:181663동해문화예술관앞060.0
24415SPB-307452020-06-01 12:31:021211방이삼거리02020-06-01 12:41:551210롯데월드타워(잠실역2번출구 쪽)0110.0
41073SPB-330522020-06-01 15:51:021028포레스 주상복합 빌딩02020-06-01 16:54:121208풍납현대아파트쉼터0630.0
21843SPB-316842020-06-01 11:43:591733방학사거리 (봄마당 앞)02020-06-01 11:49:581686온수골사거리(스타벅스앞)060.0
24405SPB-081832020-06-01 10:52:28508성수아카데미타워 앞72020-06-01 12:41:42587유니베라 앞6105790.0