Overview

Dataset statistics

Number of variables11
Number of observations9066
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory814.6 KiB
Average record size in memory92.0 B

Variable types

DateTime1
Numeric4
Text3
Categorical3

Dataset

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

Alerts

대여일자 has constant value ""Constant
씠슜嫄댁닔 is highly overall correlated with 씠룞嫄곕━(M) and 1 other fieldsHigh correlation
씠룞嫄곕━(M) is highly overall correlated with 씠슜嫄댁닔 and 1 other fieldsHigh correlation
씠슜떆媛(遺 is highly overall correlated with 씠슜嫄댁닔 and 1 other fieldsHigh correlation
대여구분코드 is highly imbalanced (50.4%)Imbalance
씠룞嫄곕━(M) has 818 (9.0%) zerosZeros

Reproduction

Analysis started2024-04-20 17:43:00.920379
Analysis finished2024-04-20 17:43:07.415261
Duration6.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
Minimum2021-11-01 00:00:00
Maximum2021-11-01 00:00:00
2024-04-21T02:43:07.541192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:07.827213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct448
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.68034
Minimum5
Maximum636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2024-04-21T02:43:08.324787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile121
Q1223
median345
Q3498
95-th percentile609
Maximum636
Range631
Interquartile range (IQR)275

Descriptive statistics

Standard deviation156.69771
Coefficient of variation (CV)0.43932251
Kurtosis-1.2219064
Mean356.68034
Median Absolute Deviation (MAD)135
Skewness0.12321115
Sum3233664
Variance24554.171
MonotonicityIncreasing
2024-04-21T02:43:08.573478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207 51
 
0.6%
565 40
 
0.4%
210 40
 
0.4%
502 39
 
0.4%
583 38
 
0.4%
602 38
 
0.4%
113 37
 
0.4%
117 36
 
0.4%
202 35
 
0.4%
147 35
 
0.4%
Other values (438) 8677
95.7%
ValueCountFrequency (%)
5 1
 
< 0.1%
102 30
0.3%
103 23
0.3%
104 25
0.3%
105 15
0.2%
106 34
0.4%
107 26
0.3%
108 24
0.3%
109 23
0.3%
111 22
0.2%
ValueCountFrequency (%)
636 1
 
< 0.1%
635 22
0.2%
634 29
0.3%
633 19
0.2%
631 25
0.3%
630 25
0.3%
628 15
0.2%
627 22
0.2%
626 20
0.2%
625 21
0.2%

대여소
Text

Distinct448
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
2024-04-21T02:43:09.400961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length14.450474
Min length7

Characters and Unicode

Total characters131008
Distinct characters362
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row상암센터 정비실
2nd row102. 망원역 1번출구 앞
3rd row102. 망원역 1번출구 앞
4th row102. 망원역 1번출구 앞
5th row102. 망원역 1번출구 앞
ValueCountFrequency (%)
앞 3587
 
12.3%
옆 802
 
2.8%
1번출구 471
 
1.6%
사거리 405
 
1.4%
2번출구 357
 
1.2%
출구 323
 
1.1%
뒤 312
 
1.1%
3번출구 251
 
0.9%
4번출구 238
 
0.8%
건너편 206
 
0.7%
Other values (949) 22164
76.1%
2024-04-21T02:43:10.751347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20135
 
15.4%
. 9065
 
6.9%
2 4974
 
3.8%
1 4757
 
3.6%
3 3946
 
3.0%
4 3736
 
2.9%
앞 3723
 
2.8%
5 3658
 
2.8%
구 3368
 
2.6%
역 2753
 
2.1%
Other values (352) 70893
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68258
52.1%
Decimal Number 31135
23.8%
Space Separator 20135
 
15.4%
Other Punctuation 9065
 
6.9%
Uppercase Letter 1583
 
1.2%
Close Punctuation 397
 
0.3%
Open Punctuation 397
 
0.3%
Dash Punctuation 38
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3723
 
5.5%
구 3368
 
4.9%
역 2753
 
4.0%
번 2592
 
3.8%
출 2592
 
3.8%
동 1476
 
2.2%
리 1287
 
1.9%
교 1267
 
1.9%
원 1116
 
1.6%
사 1082
 
1.6%
Other values (318) 47002
68.9%
Uppercase Letter
ValueCountFrequency (%)
K 264
16.7%
C 212
13.4%
S 205
13.0%
D 170
10.7%
M 132
8.3%
T 86
 
5.4%
B 85
 
5.4%
N 59
 
3.7%
P 54
 
3.4%
E 53
 
3.3%
Other values (9) 263
16.6%
Decimal Number
ValueCountFrequency (%)
2 4974
16.0%
1 4757
15.3%
3 3946
12.7%
4 3736
12.0%
5 3658
11.7%
6 2304
7.4%
0 2149
6.9%
8 2056
6.6%
9 1780
 
5.7%
7 1775
 
5.7%
Space Separator
ValueCountFrequency (%)
20135
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9065
100.0%
Close Punctuation
ValueCountFrequency (%)
) 397
100.0%
Open Punctuation
ValueCountFrequency (%)
( 397
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68258
52.1%
Common 61167
46.7%
Latin 1583
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3723
 
5.5%
구 3368
 
4.9%
역 2753
 
4.0%
번 2592
 
3.8%
출 2592
 
3.8%
동 1476
 
2.2%
리 1287
 
1.9%
교 1267
 
1.9%
원 1116
 
1.6%
사 1082
 
1.6%
Other values (318) 47002
68.9%
Latin
ValueCountFrequency (%)
K 264
16.7%
C 212
13.4%
S 205
13.0%
D 170
10.7%
M 132
8.3%
T 86
 
5.4%
B 85
 
5.4%
N 59
 
3.7%
P 54
 
3.4%
E 53
 
3.3%
Other values (9) 263
16.6%
Common
ValueCountFrequency (%)
20135
32.9%
. 9065
14.8%
2 4974
 
8.1%
1 4757
 
7.8%
3 3946
 
6.5%
4 3736
 
6.1%
5 3658
 
6.0%
6 2304
 
3.8%
0 2149
 
3.5%
8 2056
 
3.4%
Other values (5) 4387
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68258
52.1%
ASCII 62750
47.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20135
32.1%
. 9065
14.4%
2 4974
 
7.9%
1 4757
 
7.6%
3 3946
 
6.3%
4 3736
 
6.0%
5 3658
 
5.8%
6 2304
 
3.7%
0 2149
 
3.4%
8 2056
 
3.3%
Other values (24) 5970
 
9.5%
Hangul
ValueCountFrequency (%)
앞 3723
 
5.5%
구 3368
 
4.9%
역 2753
 
4.0%
번 2592
 
3.8%
출 2592
 
3.8%
동 1476
 
2.2%
리 1287
 
1.9%
교 1267
 
1.9%
원 1116
 
1.6%
사 1082
 
1.6%
Other values (318) 47002
68.9%

대여구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
정기
6533 
일일(회원)
2320 
일일(비회원)
 
148
단체
 
65

Length

Max length7
Median length2
Mean length3.1052283
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 6533
72.1%
일일(회원) 2320
 
25.6%
일일(비회원) 148
 
1.6%
단체 65
 
0.7%

Length

2024-04-21T02:43:11.167302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:43:11.482281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 6533
72.1%
일일(회원 2320
 
25.6%
일일(비회원 148
 
1.6%
단체 65
 
0.7%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
M
3225 
\N
2691 
F
2483 
<NA>
665 
m
 
2

Length

Max length4
Median length1
Mean length1.5168762
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 3225
35.6%
\N 2691
29.7%
F 2483
27.4%
<NA> 665
 
7.3%
m 2
 
< 0.1%

Length

2024-04-21T02:43:11.844762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:43:12.171629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3227
35.6%
n 2691
29.7%
f 2483
27.4%
na 665
 
7.3%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
AGE_002
2355 
AGE_003
1798 
AGE_004
1371 
AGE_008
1291 
AGE_005
1052 
Other values (3)
1199 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_004
2nd rowAGE_004
3rd rowAGE_002
4th rowAGE_008
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 2355
26.0%
AGE_003 1798
19.8%
AGE_004 1371
15.1%
AGE_008 1291
14.2%
AGE_005 1052
11.6%
AGE_001 637
 
7.0%
AGE_006 476
 
5.3%
AGE_007 86
 
0.9%

Length

2024-04-21T02:43:12.518398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:43:12.842266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2355
26.0%
age_003 1798
19.8%
age_004 1371
15.1%
age_008 1291
14.2%
age_005 1052
11.6%
age_001 637
 
7.0%
age_006 476
 
5.3%
age_007 86
 
0.9%

씠슜嫄댁닔
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0545996
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2024-04-21T02:43:13.226574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum39
Range38
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3051127
Coefficient of variation (CV)1.0820118
Kurtosis16.622661
Mean3.0545996
Median Absolute Deviation (MAD)1
Skewness3.2723422
Sum27693
Variance10.92377
MonotonicityNot monotonic
2024-04-21T02:43:13.648175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 3817
42.1%
2 1838
20.3%
3 1003
 
11.1%
4 651
 
7.2%
5 473
 
5.2%
6 304
 
3.4%
7 225
 
2.5%
8 163
 
1.8%
9 133
 
1.5%
10 116
 
1.3%
Other values (24) 343
 
3.8%
ValueCountFrequency (%)
1 3817
42.1%
2 1838
20.3%
3 1003
 
11.1%
4 651
 
7.2%
5 473
 
5.2%
6 304
 
3.4%
7 225
 
2.5%
8 163
 
1.8%
9 133
 
1.5%
10 116
 
1.3%
ValueCountFrequency (%)
39 1
 
< 0.1%
37 2
 
< 0.1%
33 2
 
< 0.1%
32 2
 
< 0.1%
31 2
 
< 0.1%
30 2
 
< 0.1%
28 2
 
< 0.1%
27 2
 
< 0.1%
26 3
< 0.1%
25 5
0.1%

슫룞
Text

Distinct7051
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
2024-04-21T02:43:14.899182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.3902493
Min length2

Characters and Unicode

Total characters48868
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

Unique6098 ?
Unique (%)67.3%

Sample

1st row128.45
2nd row32.50
3rd row191.72
4th row41.18
5th row546.21
ValueCountFrequency (%)
0.00 791
 
8.7%
n 27
 
0.3%
37.07 6
 
0.1%
35.01 6
 
0.1%
27.03 6
 
0.1%
18.28 5
 
0.1%
46.85 5
 
0.1%
24.95 5
 
0.1%
27.80 5
 
0.1%
33.46 5
 
0.1%
Other values (7041) 8205
90.5%
2024-04-21T02:43:16.532242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9039
18.5%
1 5407
11.1%
0 5400
11.1%
2 4476
9.2%
3 4147
8.5%
4 3853
7.9%
5 3529
 
7.2%
6 3417
 
7.0%
7 3278
 
6.7%
8 3148
 
6.4%
Other values (3) 3174
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39775
81.4%
Other Punctuation 9066
 
18.6%
Uppercase Letter 27
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5407
13.6%
0 5400
13.6%
2 4476
11.3%
3 4147
10.4%
4 3853
9.7%
5 3529
8.9%
6 3417
8.6%
7 3278
8.2%
8 3148
7.9%
9 3120
7.8%
Other Punctuation
ValueCountFrequency (%)
. 9039
99.7%
\ 27
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48841
99.9%
Latin 27
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9039
18.5%
1 5407
11.1%
0 5400
11.1%
2 4476
9.2%
3 4147
8.5%
4 3853
7.9%
5 3529
 
7.2%
6 3417
 
7.0%
7 3278
 
6.7%
8 3148
 
6.4%
Other values (2) 3147
 
6.4%
Latin
ValueCountFrequency (%)
N 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9039
18.5%
1 5407
11.1%
0 5400
11.1%
2 4476
9.2%
3 4147
8.5%
4 3853
7.9%
5 3529
 
7.2%
6 3417
 
7.0%
7 3278
 
6.7%
8 3148
 
6.4%
Other values (3) 3174
 
6.5%

깂냼
Text

Distinct875
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size71.0 KiB
2024-04-21T02:43:17.981473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0048533
Min length2

Characters and Unicode

Total characters36308
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

Unique272 ?
Unique (%)3.0%

Sample

1st row1.11
2nd row0.33
3rd row1.60
4th row0.37
5th row4.89
ValueCountFrequency (%)
0.00 796
 
8.8%
0.16 82
 
0.9%
0.19 75
 
0.8%
0.26 71
 
0.8%
0.24 70
 
0.8%
0.35 67
 
0.7%
0.52 66
 
0.7%
0.23 65
 
0.7%
0.58 65
 
0.7%
0.42 65
 
0.7%
Other values (865) 7644
84.3%
2024-04-21T02:43:19.617117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9039
24.9%
0 8051
22.2%
1 3860
10.6%
2 2840
 
7.8%
3 2391
 
6.6%
4 2032
 
5.6%
5 1894
 
5.2%
6 1640
 
4.5%
7 1577
 
4.3%
8 1505
 
4.1%
Other values (3) 1479
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27215
75.0%
Other Punctuation 9066
 
25.0%
Uppercase Letter 27
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8051
29.6%
1 3860
14.2%
2 2840
 
10.4%
3 2391
 
8.8%
4 2032
 
7.5%
5 1894
 
7.0%
6 1640
 
6.0%
7 1577
 
5.8%
8 1505
 
5.5%
9 1425
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 9039
99.7%
\ 27
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36281
99.9%
Latin 27
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9039
24.9%
0 8051
22.2%
1 3860
10.6%
2 2840
 
7.8%
3 2391
 
6.6%
4 2032
 
5.6%
5 1894
 
5.2%
6 1640
 
4.5%
7 1577
 
4.3%
8 1505
 
4.1%
Other values (2) 1452
 
4.0%
Latin
ValueCountFrequency (%)
N 27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9039
24.9%
0 8051
22.2%
1 3860
10.6%
2 2840
 
7.8%
3 2391
 
6.6%
4 2032
 
5.6%
5 1894
 
5.2%
6 1640
 
4.5%
7 1577
 
4.3%
8 1505
 
4.1%
Other values (3) 1479
 
4.1%

씠룞嫄곕━(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6294
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6893.3968
Minimum0
Maximum147214.7
Zeros818
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2024-04-21T02:43:19.865613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11360
median3649.405
Q38819.865
95-th percentile24166.903
Maximum147214.7
Range147214.7
Interquartile range (IQR)7459.865

Descriptive statistics

Standard deviation9461.1016
Coefficient of variation (CV)1.3724876
Kurtosis27.713783
Mean6893.3968
Median Absolute Deviation (MAD)2829.405
Skewness3.8858568
Sum62495536
Variance89512443
MonotonicityNot monotonic
2024-04-21T02:43:20.115812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 818
 
9.0%
1050.0 14
 
0.2%
680.0 12
 
0.1%
880.0 11
 
0.1%
990.0 11
 
0.1%
1360.0 11
 
0.1%
510.0 11
 
0.1%
1440.0 11
 
0.1%
690.0 11
 
0.1%
1190.0 11
 
0.1%
Other values (6284) 8145
89.8%
ValueCountFrequency (%)
0.0 818
9.0%
0.67 1
 
< 0.1%
10.0 4
 
< 0.1%
30.0 5
 
0.1%
50.0 1
 
< 0.1%
60.0 1
 
< 0.1%
88.14 1
 
< 0.1%
88.18 1
 
< 0.1%
88.19 2
 
< 0.1%
88.2 1
 
< 0.1%
ValueCountFrequency (%)
147214.7 1
< 0.1%
135226.08 1
< 0.1%
127724.26 1
< 0.1%
122528.81 1
< 0.1%
109137.65 1
< 0.1%
104363.26 1
< 0.1%
95162.85 1
< 0.1%
87570.59 1
< 0.1%
85747.56 1
< 0.1%
83309.07 1
< 0.1%

씠슜떆媛(遺
Real number (ℝ)

HIGH CORRELATION 

Distinct460
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.813258
Minimum1
Maximum2351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2024-04-21T02:43:20.444102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median44
Q395
95-th percentile230.75
Maximum2351
Range2350
Interquartile range (IQR)79

Descriptive statistics

Standard deviation90.765261
Coefficient of variation (CV)1.2639067
Kurtosis68.002905
Mean71.813258
Median Absolute Deviation (MAD)32
Skewness5.0348384
Sum651059
Variance8238.3326
MonotonicityNot monotonic
2024-04-21T02:43:20.755111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 188
 
2.1%
6 186
 
2.1%
10 182
 
2.0%
7 174
 
1.9%
13 162
 
1.8%
11 157
 
1.7%
12 155
 
1.7%
8 151
 
1.7%
9 150
 
1.7%
4 149
 
1.6%
Other values (450) 7412
81.8%
ValueCountFrequency (%)
1 29
 
0.3%
2 89
1.0%
3 135
1.5%
4 149
1.6%
5 188
2.1%
6 186
2.1%
7 174
1.9%
8 151
1.7%
9 150
1.7%
10 182
2.0%
ValueCountFrequency (%)
2351 1
< 0.1%
1575 1
< 0.1%
1467 1
< 0.1%
966 1
< 0.1%
895 1
< 0.1%
853 1
< 0.1%
828 1
< 0.1%
815 1
< 0.1%
792 1
< 0.1%
783 1
< 0.1%

Interactions

2024-04-21T02:43:05.520281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:02.372011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:03.440598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:04.418623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:05.779757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:02.633935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:03.712136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:04.691445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:06.052536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:02.914084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:03.984528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:04.979437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:06.322923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:03.186612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:04.203135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:43:05.261761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T02:43:20.956854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드씠슜嫄댁닔씠룞嫄곕━(M)씠슜떆媛(遺
대여소번호1.0000.0420.0000.0350.1050.1040.069
대여구분코드0.0421.0000.2690.4780.2300.0930.094
성별0.0000.2691.0000.1480.0990.0680.034
연령대코드0.0350.4780.1481.0000.2130.1130.077
씠슜嫄댁닔0.1050.2300.0990.2131.0000.7720.644
씠룞嫄곕━(M)0.1040.0930.0680.1130.7721.0000.806
씠슜떆媛(遺0.0690.0940.0340.0770.6440.8061.000
2024-04-21T02:43:21.174011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드대여구분코드성별
연령대코드1.0000.2300.067
대여구분코드0.2301.0000.108
성별0.0670.1081.000
2024-04-21T02:43:21.367012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호씠슜嫄댁닔씠룞嫄곕━(M)씠슜떆媛(遺대여구분코드성별연령대코드
대여소번호1.000-0.019-0.038-0.0460.0250.0000.017
씠슜嫄댁닔-0.0191.0000.6760.7290.1390.0580.103
씠룞嫄곕━(M)-0.0380.6761.0000.8020.0560.0410.054
씠슜떆媛(遺-0.0460.7290.8021.0000.0650.0230.041
대여구분코드0.0250.1390.0560.0651.0000.1080.230
성별0.0000.0580.0410.0230.1081.0000.067
연령대코드0.0170.1030.0540.0410.2300.0671.000

Missing values

2024-04-21T02:43:06.677998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T02:43:07.190228image/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

대여일자대여소번호대여소대여구분코드성별연령대코드씠슜嫄댁닔슫룞깂냼씠룞嫄곕━(M)씠슜떆媛(遺
02021-11-015상암센터 정비실일일(회원)MAGE_0041128.451.114770.025
12021-11-01102102. 망원역 1번출구 앞단체FAGE_004132.500.331440.042
22021-11-01102102. 망원역 1번출구 앞단체MAGE_0022191.721.606916.2491
32021-11-01102102. 망원역 1번출구 앞일일(비회원)\NAGE_008141.180.371600.036
42021-11-01102102. 망원역 1번출구 앞일일(회원)\NAGE_0025546.214.8921095.96192
52021-11-01102102. 망원역 1번출구 앞일일(회원)\NAGE_003136.830.291240.010
62021-11-01102102. 망원역 1번출구 앞일일(회원)\NAGE_0083211.022.038750.3753
72021-11-01102102. 망원역 1번출구 앞일일(회원)<NA>AGE_0022118.921.004290.048
82021-11-01102102. 망원역 1번출구 앞일일(회원)FAGE_0022266.372.5110809.056
92021-11-01102102. 망원역 1번출구 앞일일(회원)FAGE_0032104.111.114780.039
대여일자대여소번호대여소대여구분코드성별연령대코드씠슜嫄댁닔슫룞깂냼씠룞嫄곕━(M)씠슜떆媛(遺
90562021-11-01635635. 시조사 앞 (청량고정문 옆)정기<NA>AGE_002149.600.391670.011
90572021-11-01635635. 시조사 앞 (청량고정문 옆)정기FAGE_00210543.845.2022379.07282
90582021-11-01635635. 시조사 앞 (청량고정문 옆)정기FAGE_003233.820.271169.8415
90592021-11-01635635. 시조사 앞 (청량고정문 옆)정기FAGE_0041215.502.109070.0105
90602021-11-01635635. 시조사 앞 (청량고정문 옆)정기FAGE_0084253.642.4910789.8990
90612021-11-01635635. 시조사 앞 (청량고정문 옆)정기MAGE_002111212.789.1639473.11229
90622021-11-01635635. 시조사 앞 (청량고정문 옆)정기MAGE_003327.200.231010.0175
90632021-11-01635635. 시조사 앞 (청량고정문 옆)정기MAGE_0052128.441.154940.043
90642021-11-01635635. 시조사 앞 (청량고정문 옆)정기MAGE_0084166.891.536578.3846
90652021-11-01636636. 세종대왕기념관 교차로일일(회원)MAGE_008168.080.441910.3220