Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory986.3 KiB
Average record size in memory101.0 B

Variable types

DateTime1
Text2
Categorical3
Numeric5

Dataset

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

Alerts

'이용건수' is highly overall correlated with '운동량' and 3 other fieldsHigh correlation
'운동량' is highly overall correlated with '이용건수' and 3 other fieldsHigh correlation
'탄소량' is highly overall correlated with '이용건수' and 3 other fieldsHigh correlation
'이동거리(M)' is highly overall correlated with '이용건수' and 3 other fieldsHigh correlation
'이동시간(분)' is highly overall correlated with '이용건수' and 3 other fieldsHigh correlation
'대여구분코드' is highly overall correlated with '성별'High correlation
'성별' is highly overall correlated with '대여구분코드'High correlation
'대여구분코드' is highly imbalanced (61.0%)Imbalance
'운동량' has 121 (1.2%) zerosZeros
'탄소량' has 128 (1.3%) zerosZeros
'이동거리(M)' has 121 (1.2%) zerosZeros

Reproduction

Analysis started2024-05-04 03:16:24.430370
Analysis finished2024-05-04 03:16:33.761794
Duration9.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

'대여일자'
Date

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-12-01 00:00:00
Maximum2017-12-27 00:00:00
2024-05-04T03:16:34.059046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:34.410952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

'대여소번호'
Text

Distinct1002
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T03:16:35.139072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.4037
Min length5

Characters and Unicode

Total characters54037
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)0.4%

Sample

1st row'633'
2nd row'155'
3rd row'154'
4th row'130'
5th row'2314'
ValueCountFrequency (%)
183 32
 
0.3%
816 31
 
0.3%
154 31
 
0.3%
703 29
 
0.3%
378 29
 
0.3%
2102 29
 
0.3%
247 28
 
0.3%
2015 27
 
0.3%
106 26
 
0.3%
502 26
 
0.3%
Other values (992) 9712
97.1%
2024-05-04T03:16:36.019942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 20000
37.0%
1 7380
 
13.7%
2 6036
 
11.2%
3 4203
 
7.8%
0 3466
 
6.4%
5 3184
 
5.9%
4 2592
 
4.8%
6 2273
 
4.2%
8 1767
 
3.3%
9 1643
 
3.0%
Other values (5) 1493
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34025
63.0%
Other Punctuation 20000
37.0%
Other Letter 12
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7380
21.7%
2 6036
17.7%
3 4203
12.4%
0 3466
10.2%
5 3184
9.4%
4 2592
 
7.6%
6 2273
 
6.7%
8 1767
 
5.2%
9 1643
 
4.8%
7 1481
 
4.4%
Other Letter
ValueCountFrequency (%)
중 3
25.0%
랑 3
25.0%
센 3
25.0%
터 3
25.0%
Other Punctuation
ValueCountFrequency (%)
' 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54025
> 99.9%
Hangul 12
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
' 20000
37.0%
1 7380
 
13.7%
2 6036
 
11.2%
3 4203
 
7.8%
0 3466
 
6.4%
5 3184
 
5.9%
4 2592
 
4.8%
6 2273
 
4.2%
8 1767
 
3.3%
9 1643
 
3.0%
Hangul
ValueCountFrequency (%)
중 3
25.0%
랑 3
25.0%
센 3
25.0%
터 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54025
> 99.9%
Hangul 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 20000
37.0%
1 7380
 
13.7%
2 6036
 
11.2%
3 4203
 
7.8%
0 3466
 
6.4%
5 3184
 
5.9%
4 2592
 
4.8%
6 2273
 
4.2%
8 1767
 
3.3%
9 1643
 
3.0%
Hangul
ValueCountFrequency (%)
중 3
25.0%
랑 3
25.0%
센 3
25.0%
터 3
25.0%

'대여소'
Text

Distinct1002
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T03:16:36.400757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length27
Mean length12.8841
Min length6

Characters and Unicode

Total characters128841
Distinct characters461
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

Unique38 ?
Unique (%)0.4%

Sample

1st row' 청량리 기업은행 앞'
2nd row' 가좌역1 번출구 뒤'
3rd row' 마포구청역 '
4th row' 신촌역(2호선) 7번출구 앞'
5th row' 청담나들목입구'
ValueCountFrequency (%)
9941
30.2%
앞 3549
 
10.8%
옆 764
 
2.3%
1번출구 505
 
1.5%
사거리 435
 
1.3%
출구 421
 
1.3%
2번출구 379
 
1.2%
뒤 354
 
1.1%
4번출구 306
 
0.9%
3번출구 236
 
0.7%
Other values (1232) 16033
48.7%
2024-05-04T03:16:37.094678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22948
 
17.8%
' 20000
 
15.5%
구 4204
 
3.3%
앞 3964
 
3.1%
역 3472
 
2.7%
번 3155
 
2.4%
출 3146
 
2.4%
동 1628
 
1.3%
교 1490
 
1.2%
리 1414
 
1.1%
Other values (451) 63420
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78261
60.7%
Space Separator 22948
 
17.8%
Other Punctuation 20039
 
15.6%
Decimal Number 4850
 
3.8%
Uppercase Letter 1351
 
1.0%
Close Punctuation 597
 
0.5%
Open Punctuation 597
 
0.5%
Dash Punctuation 92
 
0.1%
Lowercase Letter 84
 
0.1%
Math Symbol 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4204
 
5.4%
앞 3964
 
5.1%
역 3472
 
4.4%
번 3155
 
4.0%
출 3146
 
4.0%
동 1628
 
2.1%
교 1490
 
1.9%
리 1414
 
1.8%
사 1272
 
1.6%
대 1197
 
1.5%
Other values (401) 53319
68.1%
Uppercase Letter
ValueCountFrequency (%)
K 199
14.7%
C 172
12.7%
S 146
10.8%
M 110
8.1%
T 91
 
6.7%
G 80
 
5.9%
D 80
 
5.9%
A 71
 
5.3%
L 66
 
4.9%
B 65
 
4.8%
Other values (13) 271
20.1%
Decimal Number
ValueCountFrequency (%)
1 1341
27.6%
2 950
19.6%
4 587
12.1%
3 582
12.0%
5 327
 
6.7%
8 282
 
5.8%
7 257
 
5.3%
6 232
 
4.8%
0 179
 
3.7%
9 113
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
e 24
28.6%
m 12
14.3%
l 12
14.3%
t 12
14.3%
c 12
14.3%
o 12
14.3%
Other Punctuation
ValueCountFrequency (%)
' 20000
99.8%
, 32
 
0.2%
@ 5
 
< 0.1%
& 2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 16
94.1%
+ 1
 
5.9%
Space Separator
ValueCountFrequency (%)
22948
100.0%
Close Punctuation
ValueCountFrequency (%)
) 597
100.0%
Open Punctuation
ValueCountFrequency (%)
( 597
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78261
60.7%
Common 49145
38.1%
Latin 1435
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4204
 
5.4%
앞 3964
 
5.1%
역 3472
 
4.4%
번 3155
 
4.0%
출 3146
 
4.0%
동 1628
 
2.1%
교 1490
 
1.9%
리 1414
 
1.8%
사 1272
 
1.6%
대 1197
 
1.5%
Other values (401) 53319
68.1%
Latin
ValueCountFrequency (%)
K 199
13.9%
C 172
12.0%
S 146
10.2%
M 110
 
7.7%
T 91
 
6.3%
G 80
 
5.6%
D 80
 
5.6%
A 71
 
4.9%
L 66
 
4.6%
B 65
 
4.5%
Other values (19) 355
24.7%
Common
ValueCountFrequency (%)
22948
46.7%
' 20000
40.7%
1 1341
 
2.7%
2 950
 
1.9%
) 597
 
1.2%
( 597
 
1.2%
4 587
 
1.2%
3 582
 
1.2%
5 327
 
0.7%
8 282
 
0.6%
Other values (11) 934
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78261
60.7%
ASCII 50580
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22948
45.4%
' 20000
39.5%
1 1341
 
2.7%
2 950
 
1.9%
) 597
 
1.2%
( 597
 
1.2%
4 587
 
1.2%
3 582
 
1.2%
5 327
 
0.6%
8 282
 
0.6%
Other values (40) 2369
 
4.7%
Hangul
ValueCountFrequency (%)
구 4204
 
5.4%
앞 3964
 
5.1%
역 3472
 
4.4%
번 3155
 
4.0%
출 3146
 
4.0%
동 1628
 
2.1%
교 1490
 
1.9%
리 1414
 
1.8%
사 1272
 
1.6%
대 1197
 
1.5%
Other values (401) 53319
68.1%

'대여구분코드'
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'정기'
8327 
'일일(회원)'
1352 
'일일(비회원)'
 
304
'단체'
 
17

Length

Max length9
Median length4
Mean length4.6928
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'일일(회원)'
2nd row'정기'
3rd row'정기'
4th row'정기'
5th row'정기'

Common Values

ValueCountFrequency (%)
'정기' 8327
83.3%
'일일(회원)' 1352
 
13.5%
'일일(비회원)' 304
 
3.0%
'단체' 17
 
0.2%

Length

2024-05-04T03:16:37.332664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:16:37.509841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 8327
83.3%
일일(회원 1352
 
13.5%
일일(비회원 304
 
3.0%
단체 17
 
0.2%

'성별'
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'M'
6524 
'F'
3193 
''
 
283

Length

Max length3
Median length3
Mean length2.9717
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'F'
2nd row'M'
3rd row'F'
4th row'M'
5th row'F'

Common Values

ValueCountFrequency (%)
'M' 6524
65.2%
'F' 3193
31.9%
'' 283
 
2.8%

Length

2024-05-04T03:16:37.758205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:16:38.019791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 6524
65.2%
f 3193
31.9%
283
 
2.8%

'연령대코드'
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
'20대'
3427 
'30대'
2530 
'40대'
1845 
'50대'
1037 
'~10대'
420 
Other values (3)
741 

Length

Max length6
Median length5
Mean length5.0234
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'30대'
2nd row'60대'
3rd row'40대'
4th row'50대'
5th row'40대'

Common Values

ValueCountFrequency (%)
'20대' 3427
34.3%
'30대' 2530
25.3%
'40대' 1845
18.4%
'50대' 1037
 
10.4%
'~10대' 420
 
4.2%
'60대' 321
 
3.2%
<NA> 303
 
3.0%
'70대~' 117
 
1.2%

Length

2024-05-04T03:16:38.380748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:16:38.740566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 3427
34.3%
30대 2530
25.3%
40대 1845
18.4%
50대 1037
 
10.4%
10대 420
 
4.2%
60대 321
 
3.2%
na 303
 
3.0%
70대 117
 
1.2%

'이용건수'
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7682
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:16:39.023428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum21
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4974288
Coefficient of variation (CV)0.84686619
Kurtosis22.934518
Mean1.7682
Median Absolute Deviation (MAD)0
Skewness3.7551506
Sum17682
Variance2.242293
MonotonicityNot monotonic
2024-05-04T03:16:39.392200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 6330
63.3%
2 1940
 
19.4%
3 845
 
8.5%
4 351
 
3.5%
5 223
 
2.2%
6 118
 
1.2%
7 80
 
0.8%
8 39
 
0.4%
9 26
 
0.3%
10 14
 
0.1%
Other values (10) 34
 
0.3%
ValueCountFrequency (%)
1 6330
63.3%
2 1940
 
19.4%
3 845
 
8.5%
4 351
 
3.5%
5 223
 
2.2%
6 118
 
1.2%
7 80
 
0.8%
8 39
 
0.4%
9 26
 
0.3%
10 14
 
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
18 3
 
< 0.1%
17 3
 
< 0.1%
16 2
 
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 8
0.1%
11 11
0.1%

'운동량'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6641
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.02665
Minimum0
Maximum4306.84
Zeros121
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:16:39.807871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.19
Q138.37
median76.045
Q3156.225
95-th percentile446.096
Maximum4306.84
Range4306.84
Interquartile range (IQR)117.855

Descriptive statistics

Standard deviation229.19669
Coefficient of variation (CV)1.6137584
Kurtosis53.876694
Mean142.02665
Median Absolute Deviation (MAD)46.105
Skewness5.9671209
Sum1420266.5
Variance52531.121
MonotonicityNot monotonic
2024-05-04T03:16:40.238036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 121
 
1.2%
43.24 13
 
0.1%
62.29 12
 
0.1%
38.61 12
 
0.1%
18.02 12
 
0.1%
21.62 12
 
0.1%
30.63 12
 
0.1%
28.83 11
 
0.1%
32.43 11
 
0.1%
18.53 11
 
0.1%
Other values (6631) 9773
97.7%
ValueCountFrequency (%)
0.0 121
1.2%
0.21 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 2
 
< 0.1%
0.48 1
 
< 0.1%
0.63 1
 
< 0.1%
0.74 1
 
< 0.1%
1.39 1
 
< 0.1%
1.47 1
 
< 0.1%
ValueCountFrequency (%)
4306.84 1
< 0.1%
3795.24 1
< 0.1%
3364.85 1
< 0.1%
3288.72 1
< 0.1%
2683.73 1
< 0.1%
2649.59 1
< 0.1%
2632.52 1
< 0.1%
2509.02 1
< 0.1%
2481.86 1
< 0.1%
2427.57 1
< 0.1%

'탄소량'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct703
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.194852
Minimum0
Maximum51.47
Zeros128
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:16:40.837850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14
Q10.34
median0.65
Q31.33
95-th percentile3.66
Maximum51.47
Range51.47
Interquartile range (IQR)0.99

Descriptive statistics

Standard deviation1.8890357
Coefficient of variation (CV)1.5809788
Kurtosis85.708719
Mean1.194852
Median Absolute Deviation (MAD)0.39
Skewness6.6936571
Sum11948.52
Variance3.5684558
MonotonicityNot monotonic
2024-05-04T03:16:41.281035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26 147
 
1.5%
0.32 133
 
1.3%
0.0 128
 
1.3%
0.29 120
 
1.2%
0.35 118
 
1.2%
0.19 117
 
1.2%
0.24 114
 
1.1%
0.27 107
 
1.1%
0.34 107
 
1.1%
0.28 104
 
1.0%
Other values (693) 8805
88.0%
ValueCountFrequency (%)
0.0 128
1.3%
0.01 2
 
< 0.1%
0.02 3
 
< 0.1%
0.03 9
 
0.1%
0.04 5
 
0.1%
0.05 8
 
0.1%
0.06 16
 
0.2%
0.07 12
 
0.1%
0.08 30
 
0.3%
0.09 35
 
0.4%
ValueCountFrequency (%)
51.47 1
< 0.1%
31.15 1
< 0.1%
26.35 1
< 0.1%
24.69 1
< 0.1%
20.46 1
< 0.1%
19.54 1
< 0.1%
19.37 1
< 0.1%
19.06 1
< 0.1%
18.48 1
< 0.1%
18.27 1
< 0.1%

'이동거리(M)'
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1803
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5149.996
Minimum0
Maximum221780
Zeros121
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:16:41.696775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile600
Q11460
median2810
Q35730
95-th percentile15761.5
Maximum221780
Range221780
Interquartile range (IQR)4270

Descriptive statistics

Standard deviation8142.3559
Coefficient of variation (CV)1.5810412
Kurtosis85.646665
Mean5149.996
Median Absolute Deviation (MAD)1670
Skewness6.6923563
Sum51499960
Variance66297960
MonotonicityNot monotonic
2024-05-04T03:16:42.109948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 121
 
1.2%
1140 36
 
0.4%
1190 35
 
0.4%
930 34
 
0.3%
1380 32
 
0.3%
1020 32
 
0.3%
1520 31
 
0.3%
1500 31
 
0.3%
860 31
 
0.3%
1240 30
 
0.3%
Other values (1793) 9587
95.9%
ValueCountFrequency (%)
0 121
1.2%
10 5
 
0.1%
20 2
 
< 0.1%
30 1
 
< 0.1%
50 1
 
< 0.1%
70 2
 
< 0.1%
90 1
 
< 0.1%
110 5
 
0.1%
120 1
 
< 0.1%
130 1
 
< 0.1%
ValueCountFrequency (%)
221780 1
< 0.1%
134270 1
< 0.1%
113580 1
< 0.1%
106390 1
< 0.1%
88200 1
< 0.1%
84260 1
< 0.1%
83480 1
< 0.1%
82130 1
< 0.1%
79680 1
< 0.1%
78740 1
< 0.1%

'이동시간(분)'
Real number (ℝ)

HIGH CORRELATION 

Distinct258
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.9255
Minimum0
Maximum542
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:16:42.579150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median19
Q342
95-th percentile106
Maximum542
Range542
Interquartile range (IQR)33

Descriptive statistics

Standard deviation39.752081
Coefficient of variation (CV)1.2073342
Kurtosis23.355463
Mean32.9255
Median Absolute Deviation (MAD)12
Skewness3.6422333
Sum329255
Variance1580.228
MonotonicityNot monotonic
2024-05-04T03:16:43.024306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 372
 
3.7%
6 372
 
3.7%
9 350
 
3.5%
8 350
 
3.5%
7 335
 
3.4%
4 309
 
3.1%
10 308
 
3.1%
11 297
 
3.0%
12 287
 
2.9%
13 286
 
2.9%
Other values (248) 6734
67.3%
ValueCountFrequency (%)
0 17
 
0.2%
1 32
 
0.3%
2 144
 
1.4%
3 283
2.8%
4 309
3.1%
5 372
3.7%
6 372
3.7%
7 335
3.4%
8 350
3.5%
9 350
3.5%
ValueCountFrequency (%)
542 1
< 0.1%
527 1
< 0.1%
519 1
< 0.1%
472 1
< 0.1%
466 1
< 0.1%
454 1
< 0.1%
446 1
< 0.1%
444 1
< 0.1%
425 1
< 0.1%
383 1
< 0.1%

Interactions

2024-05-04T03:16:31.251471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:26.824961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:27.963622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:28.966448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:29.878227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:31.560288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:27.018619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:28.155787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:29.152855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:30.211744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:31.815336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:27.216053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:28.308457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:29.308249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:30.467694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:32.086283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:27.492705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:28.467808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:29.475570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:30.733785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:32.501967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:27.758669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:28.793438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:29.635915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:16:30.987043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T03:16:43.294188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'대여일자''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
'대여일자'1.0000.1900.1520.0210.1030.0000.0000.0000.034
'대여구분코드'0.1901.0000.6560.1810.1320.0000.0000.0000.153
'성별'0.1520.6561.0000.0960.1650.1070.0420.0420.117
'연령대코드'0.0210.1810.0961.0000.1390.0360.0000.0000.047
'이용건수'0.1030.1320.1650.1391.0000.4920.3940.3940.623
'운동량'0.0000.0000.1070.0360.4921.0000.9320.9320.625
'탄소량'0.0000.0000.0420.0000.3940.9321.0001.0000.535
'이동거리(M)'0.0000.0000.0420.0000.3940.9321.0001.0000.535
'이동시간(분)'0.0340.1530.1170.0470.6230.6250.5350.5351.000
2024-05-04T03:16:43.593565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'성별''대여구분코드''연령대코드'
'성별'1.0000.6810.103
'대여구분코드'0.6811.0000.125
'연령대코드'0.1030.1251.000
2024-05-04T03:16:43.790904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
'이용건수''운동량''탄소량''이동거리(M)''이동시간(분)''대여구분코드''성별''연령대코드'
'이용건수'1.0000.5590.5570.5570.5260.0600.0850.056
'운동량'0.5591.0000.9870.9870.8290.0000.0630.018
'탄소량'0.5570.9871.0001.0000.8450.0000.0260.000
'이동거리(M)'0.5570.9871.0001.0000.8450.0000.0260.000
'이동시간(분)'0.5260.8290.8450.8451.0000.0920.0700.024
'대여구분코드'0.0600.0000.0000.0000.0921.0000.6810.125
'성별'0.0850.0630.0260.0260.0700.6811.0000.103
'연령대코드'0.0560.0180.0000.0000.0240.1250.1031.000

Missing values

2024-05-04T03:16:32.899672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T03:16:33.504935image/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

'대여일자''대여소번호''대여소''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
89948'2017-12-24''633'' 청량리 기업은행 앞''일일(회원)''F''30대'1203.151.98855052
31939'2017-12-07''155'' 가좌역1 번출구 뒤''정기''M''60대'276.210.65283019
66721'2017-12-17''154'' 마포구청역 ''정기''F''40대'1133.641.51649036
76028'2017-12-20''130'' 신촌역(2호선) 7번출구 앞''정기''M''50대'128.040.2711807
33896'2017-12-08''2314'' 청담나들목입구''정기''F''40대'2302.593.181371090
63164'2017-12-16''137'' NH농협 신촌지점 앞''정기''F''20대'151.960.56243011
19385'2017-12-04''378'' 청계7가 사거리''일일(비회원)'''<NA>2140.791.275470126
12357'2017-12-03''1707'' 도봉구민회관''정기''M''30대'166.810.53228018
77464'2017-12-21''199'' 서울 월드컵 경기장''정기''F''50대'165.640.59255014
29722'2017-12-07''362'' 청계8가 사거리''정기''M''20대'4198.751.61692031
'대여일자''대여소번호''대여소''대여구분코드''성별''연령대코드''이용건수''운동량''탄소량''이동거리(M)''이동시간(분)'
2334'2017-12-01''2354'' 청담역 2번출구''정기''M''20대'120.790.177504
72864'2017-12-19''633'' 청량리 기업은행 앞''정기''M''40대'2115.190.8344039
61255'2017-12-15''2341'' 일원역 4~5번 출구 사이''정기''M''30대'1215.271.4604038
49063'2017-12-12''514'' 성수사거리 버스정류장 앞''정기''M''20대'7348.653.051316067
80039'2017-12-21''207'' 여의나루역 1번출구 앞''일일(회원)''M''40대'126.140.28806
91750'2017-12-25''825'' 서빙고동 주민센터 앞''정기''M''30대'51333.119.3240170195
1282'2017-12-01''408'' LG CNS앞''정기''F''40대'11.470.02701
41282'2017-12-09''2013'' 장승배기역 5번출구''일일(회원)''M''20대'6426.223.6315690133
62027'2017-12-15''185'' 마포 신수공원 앞''정기''M''50대'151.910.52226011
83407'2017-12-22''2108'' 은천치안센터''정기''M''50대'142.130.3515207