Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory976.6 KiB
Average record size in memory100.0 B

Variable types

Categorical4
Numeric4
Text3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/A/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 (65.3%)Imbalance
씠룞嫄곕━(M) has 1091 (10.9%) zerosZeros

Reproduction

Analysis started2024-05-18 05:00:14.040243
Analysis finished2024-05-18 05:00:24.802508
Duration10.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-12-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-01
2nd row2021-12-01
3rd row2021-12-01
4th row2021-12-01
5th row2021-12-01

Common Values

ValueCountFrequency (%)
2021-12-01 10000
100.0%

Length

2024-05-18T14:00:25.030172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:00:25.470909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-01 10000
100.0%

대여소번호
Real number (ℝ)

Distinct847
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean625.0552
Minimum102
Maximum1193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:00:25.938552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile147
Q1329
median605
Q3913
95-th percentile1155
Maximum1193
Range1091
Interquartile range (IQR)584

Descriptive statistics

Standard deviation327.52173
Coefficient of variation (CV)0.52398849
Kurtosis-1.2203251
Mean625.0552
Median Absolute Deviation (MAD)297
Skewness0.15165136
Sum6250552
Variance107270.48
MonotonicityNot monotonic
2024-05-18T14:00:26.928298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153 31
 
0.3%
785 30
 
0.3%
1106 27
 
0.3%
262 26
 
0.3%
1166 25
 
0.2%
207 25
 
0.2%
1158 25
 
0.2%
1124 25
 
0.2%
770 25
 
0.2%
1117 25
 
0.2%
Other values (837) 9736
97.4%
ValueCountFrequency (%)
102 18
0.2%
103 14
0.1%
104 10
0.1%
105 6
 
0.1%
106 15
0.1%
107 14
0.1%
108 14
0.1%
109 10
0.1%
111 5
 
0.1%
112 9
0.1%
ValueCountFrequency (%)
1193 12
0.1%
1192 19
0.2%
1191 20
0.2%
1190 12
0.1%
1188 6
 
0.1%
1187 13
0.1%
1186 14
0.1%
1185 12
0.1%
1184 24
0.2%
1183 10
0.1%

대여소
Text

Distinct847
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:00:27.629962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length25
Mean length14.8925
Min length7

Characters and Unicode

Total characters148925
Distinct characters440
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

Unique13 ?
Unique (%)0.1%

Sample

1st row631. 답십리역 1번출구
2nd row950. 구산역 2번 출구
3rd row514. 성수사거리 버스정류장 앞
4th row538. 답십리역 8번출구 앞
5th row411. DMC홍보관
ValueCountFrequency (%)
앞 2886
 
9.5%
옆 523
 
1.7%
출구 386
 
1.3%
1번출구 345
 
1.1%
사거리 333
 
1.1%
2번출구 300
 
1.0%
4번출구 292
 
1.0%
뒤 272
 
0.9%
건너편 222
 
0.7%
버스정류장 197
 
0.6%
Other values (1757) 24667
81.1%
2024-05-18T14:00:28.882320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20567
 
13.8%
. 10000
 
6.7%
1 7452
 
5.0%
2 4324
 
2.9%
3 3569
 
2.4%
4 3524
 
2.4%
구 3446
 
2.3%
앞 3441
 
2.3%
5 3365
 
2.3%
0 3257
 
2.2%
Other values (430) 85980
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77949
52.3%
Decimal Number 36804
24.7%
Space Separator 20567
 
13.8%
Other Punctuation 10082
 
6.8%
Uppercase Letter 1747
 
1.2%
Close Punctuation 834
 
0.6%
Open Punctuation 834
 
0.6%
Dash Punctuation 54
 
< 0.1%
Connector Punctuation 22
 
< 0.1%
Lowercase Letter 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3446
 
4.4%
앞 3441
 
4.4%
역 2892
 
3.7%
번 2601
 
3.3%
출 2548
 
3.3%
동 2185
 
2.8%
리 1535
 
2.0%
교 1521
 
2.0%
사 1231
 
1.6%
아 1182
 
1.5%
Other values (390) 55367
71.0%
Uppercase Letter
ValueCountFrequency (%)
S 239
13.7%
K 233
13.3%
C 197
11.3%
B 141
 
8.1%
D 114
 
6.5%
M 93
 
5.3%
G 87
 
5.0%
T 81
 
4.6%
L 76
 
4.4%
I 75
 
4.3%
Other values (9) 411
23.5%
Decimal Number
ValueCountFrequency (%)
1 7452
20.2%
2 4324
11.7%
3 3569
9.7%
4 3524
9.6%
5 3365
9.1%
0 3257
8.8%
7 3232
8.8%
6 2981
8.1%
8 2585
 
7.0%
9 2515
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 10000
99.2%
, 70
 
0.7%
? 12
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
k 9
50.0%
t 9
50.0%
Space Separator
ValueCountFrequency (%)
20567
100.0%
Close Punctuation
ValueCountFrequency (%)
) 834
100.0%
Open Punctuation
ValueCountFrequency (%)
( 834
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 22
100.0%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77949
52.3%
Common 69211
46.5%
Latin 1765
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3446
 
4.4%
앞 3441
 
4.4%
역 2892
 
3.7%
번 2601
 
3.3%
출 2548
 
3.3%
동 2185
 
2.8%
리 1535
 
2.0%
교 1521
 
2.0%
사 1231
 
1.6%
아 1182
 
1.5%
Other values (390) 55367
71.0%
Latin
ValueCountFrequency (%)
S 239
13.5%
K 233
13.2%
C 197
11.2%
B 141
 
8.0%
D 114
 
6.5%
M 93
 
5.3%
G 87
 
4.9%
T 81
 
4.6%
L 76
 
4.3%
I 75
 
4.2%
Other values (11) 429
24.3%
Common
ValueCountFrequency (%)
20567
29.7%
. 10000
14.4%
1 7452
 
10.8%
2 4324
 
6.2%
3 3569
 
5.2%
4 3524
 
5.1%
5 3365
 
4.9%
0 3257
 
4.7%
7 3232
 
4.7%
6 2981
 
4.3%
Other values (9) 6940
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77949
52.3%
ASCII 70976
47.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20567
29.0%
. 10000
14.1%
1 7452
 
10.5%
2 4324
 
6.1%
3 3569
 
5.0%
4 3524
 
5.0%
5 3365
 
4.7%
0 3257
 
4.6%
7 3232
 
4.6%
6 2981
 
4.2%
Other values (30) 8705
12.3%
Hangul
ValueCountFrequency (%)
구 3446
 
4.4%
앞 3441
 
4.4%
역 2892
 
3.7%
번 2601
 
3.3%
출 2548
 
3.3%
동 2185
 
2.8%
리 1535
 
2.0%
교 1521
 
2.0%
사 1231
 
1.6%
아 1182
 
1.5%
Other values (390) 55367
71.0%

대여구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
8387 
일일(회원)
1520 
일일(비회원)
 
77
단체
 
16

Length

Max length7
Median length2
Mean length2.6465
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 8387
83.9%
일일(회원) 1520
 
15.2%
일일(비회원) 77
 
0.8%
단체 16
 
0.2%

Length

2024-05-18T14:00:29.243476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:00:29.473860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 8387
83.9%
일일(회원 1520
 
15.2%
일일(비회원 77
 
0.8%
단체 16
 
0.2%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3772 
\N
3060 
F
2503 
<NA>
663 
m
 
2

Length

Max length4
Median length1
Mean length1.5049
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd row<NA>
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 3772
37.7%
\N 3060
30.6%
F 2503
25.0%
<NA> 663
 
6.6%
m 2
 
< 0.1%

Length

2024-05-18T14:00:29.832965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:00:30.029375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3774
37.7%
n 3060
30.6%
f 2503
25.0%
na 663
 
6.6%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2595 
AGE_003
2004 
AGE_004
1557 
AGE_008
1228 
AGE_005
1220 
Other values (3)
1396 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_001
2nd rowAGE_002
3rd rowAGE_002
4th rowAGE_008
5th rowAGE_008

Common Values

ValueCountFrequency (%)
AGE_002 2595
25.9%
AGE_003 2004
20.0%
AGE_004 1557
15.6%
AGE_008 1228
12.3%
AGE_005 1220
12.2%
AGE_001 809
 
8.1%
AGE_006 515
 
5.1%
AGE_007 72
 
0.7%

Length

2024-05-18T14:00:30.371328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:00:30.732488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2595
25.9%
age_003 2004
20.0%
age_004 1557
15.6%
age_008 1228
12.3%
age_005 1220
12.2%
age_001 809
 
8.1%
age_006 515
 
5.1%
age_007 72
 
0.7%

씠슜嫄댁닔
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2231
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:00:31.061919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum26
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.040231
Coefficient of variation (CV)0.91774145
Kurtosis14.836923
Mean2.2231
Median Absolute Deviation (MAD)0
Skewness3.0721179
Sum22231
Variance4.1625426
MonotonicityNot monotonic
2024-05-18T14:00:31.436538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 5233
52.3%
2 2087
 
20.9%
3 1039
 
10.4%
4 581
 
5.8%
5 374
 
3.7%
6 244
 
2.4%
7 159
 
1.6%
8 85
 
0.9%
9 57
 
0.6%
10 46
 
0.5%
Other values (11) 95
 
0.9%
ValueCountFrequency (%)
1 5233
52.3%
2 2087
 
20.9%
3 1039
 
10.4%
4 581
 
5.8%
5 374
 
3.7%
6 244
 
2.4%
7 159
 
1.6%
8 85
 
0.9%
9 57
 
0.6%
10 46
 
0.5%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 1
 
< 0.1%
22 2
 
< 0.1%
21 2
 
< 0.1%
17 4
 
< 0.1%
16 10
0.1%
15 6
 
0.1%
14 9
0.1%
13 13
0.1%
12 16
0.2%

슫룞
Text

Distinct6742
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:00:32.234216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1696
Min length2

Characters and Unicode

Total characters51696
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

Unique5353 ?
Unique (%)53.5%

Sample

1st row8.54
2nd row41.92
3rd row196.42
4th row25.72
5th row0.00
ValueCountFrequency (%)
0.00 1060
 
10.6%
n 35
 
0.4%
21.62 16
 
0.2%
28.83 12
 
0.1%
36.55 11
 
0.1%
15.44 9
 
0.1%
27.03 9
 
0.1%
45.56 8
 
0.1%
44.27 8
 
0.1%
38.61 8
 
0.1%
Other values (6732) 8824
88.2%
2024-05-18T14:00:33.558111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9965
19.3%
0 6404
12.4%
1 5649
10.9%
2 4776
9.2%
3 4189
8.1%
4 3790
 
7.3%
5 3613
 
7.0%
6 3499
 
6.8%
7 3369
 
6.5%
9 3206
 
6.2%
Other values (3) 3236
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41661
80.6%
Other Punctuation 10000
 
19.3%
Uppercase Letter 35
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6404
15.4%
1 5649
13.6%
2 4776
11.5%
3 4189
10.1%
4 3790
9.1%
5 3613
8.7%
6 3499
8.4%
7 3369
8.1%
9 3206
7.7%
8 3166
7.6%
Other Punctuation
ValueCountFrequency (%)
. 9965
99.7%
\ 35
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51661
99.9%
Latin 35
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9965
19.3%
0 6404
12.4%
1 5649
10.9%
2 4776
9.2%
3 4189
8.1%
4 3790
 
7.3%
5 3613
 
7.0%
6 3499
 
6.8%
7 3369
 
6.5%
9 3206
 
6.2%
Other values (2) 3201
 
6.2%
Latin
ValueCountFrequency (%)
N 35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9965
19.3%
0 6404
12.4%
1 5649
10.9%
2 4776
9.2%
3 4189
8.1%
4 3790
 
7.3%
5 3613
 
7.0%
6 3499
 
6.8%
7 3369
 
6.5%
9 3206
 
6.2%
Other values (3) 3236
 
6.3%

깂냼
Text

Distinct535
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:00:34.338162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9932
Min length2

Characters and Unicode

Total characters39932
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

Unique132 ?
Unique (%)1.3%

Sample

1st row0.07
2nd row0.33
3rd row1.83
4th row0.26
5th row0.00
ValueCountFrequency (%)
0.00 1067
 
10.7%
0.19 132
 
1.3%
0.27 127
 
1.3%
0.16 116
 
1.2%
0.17 115
 
1.1%
0.21 114
 
1.1%
0.23 114
 
1.1%
0.25 112
 
1.1%
0.24 111
 
1.1%
0.29 110
 
1.1%
Other values (525) 7882
78.8%
2024-05-18T14:00:35.493303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10954
27.4%
. 9965
25.0%
1 3848
 
9.6%
2 2931
 
7.3%
3 2331
 
5.8%
4 1957
 
4.9%
5 1739
 
4.4%
6 1695
 
4.2%
7 1601
 
4.0%
8 1444
 
3.6%
Other values (3) 1467
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29897
74.9%
Other Punctuation 10000
 
25.0%
Uppercase Letter 35
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10954
36.6%
1 3848
 
12.9%
2 2931
 
9.8%
3 2331
 
7.8%
4 1957
 
6.5%
5 1739
 
5.8%
6 1695
 
5.7%
7 1601
 
5.4%
8 1444
 
4.8%
9 1397
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 9965
99.7%
\ 35
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39897
99.9%
Latin 35
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10954
27.5%
. 9965
25.0%
1 3848
 
9.6%
2 2931
 
7.3%
3 2331
 
5.8%
4 1957
 
4.9%
5 1739
 
4.4%
6 1695
 
4.2%
7 1601
 
4.0%
8 1444
 
3.6%
Other values (2) 1432
 
3.6%
Latin
ValueCountFrequency (%)
N 35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10954
27.4%
. 9965
25.0%
1 3848
 
9.6%
2 2931
 
7.3%
3 2331
 
5.8%
4 1957
 
4.9%
5 1739
 
4.4%
6 1695
 
4.2%
7 1601
 
4.0%
8 1444
 
3.6%
Other values (3) 1467
 
3.7%

씠룞嫄곕━(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5903
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3566.6712
Minimum0
Maximum48323.52
Zeros1091
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:00:35.933204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1910
median2089.055
Q34527.1325
95-th percentile12242.665
Maximum48323.52
Range48323.52
Interquartile range (IQR)3617.1325

Descriptive statistics

Standard deviation4422.3004
Coefficient of variation (CV)1.2398957
Kurtosis12.498107
Mean3566.6712
Median Absolute Deviation (MAD)1459.055
Skewness2.9024747
Sum35666712
Variance19556741
MonotonicityNot monotonic
2024-05-18T14:00:36.373283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1091
 
10.9%
840.0 22
 
0.2%
1160.0 22
 
0.2%
1190.0 21
 
0.2%
940.0 21
 
0.2%
1300.0 20
 
0.2%
1060.0 20
 
0.2%
570.0 19
 
0.2%
1150.0 19
 
0.2%
660.0 19
 
0.2%
Other values (5893) 8726
87.3%
ValueCountFrequency (%)
0.0 1091
10.9%
0.1 4
 
< 0.1%
0.26 1
 
< 0.1%
10.0 5
 
0.1%
20.0 1
 
< 0.1%
50.0 1
 
< 0.1%
60.0 2
 
< 0.1%
70.0 1
 
< 0.1%
88.08 1
 
< 0.1%
88.13 2
 
< 0.1%
ValueCountFrequency (%)
48323.52 1
< 0.1%
45032.57 1
< 0.1%
42976.05 1
< 0.1%
42117.8 1
< 0.1%
41101.65 1
< 0.1%
38896.15 1
< 0.1%
38059.14 1
< 0.1%
37850.0 1
< 0.1%
35996.55 1
< 0.1%
35666.26 1
< 0.1%

씠슜떆媛(遺
Real number (ℝ)

HIGH CORRELATION 

Distinct310
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.1345
Minimum0
Maximum677
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:00:36.781747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median22
Q351
95-th percentile134
Maximum677
Range677
Interquartile range (IQR)41

Descriptive statistics

Standard deviation50.544245
Coefficient of variation (CV)1.2593715
Kurtosis19.751668
Mean40.1345
Median Absolute Deviation (MAD)15
Skewness3.3904924
Sum401345
Variance2554.7207
MonotonicityNot monotonic
2024-05-18T14:00:37.328737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 364
 
3.6%
6 354
 
3.5%
4 329
 
3.3%
7 328
 
3.3%
8 307
 
3.1%
9 293
 
2.9%
3 292
 
2.9%
10 289
 
2.9%
13 255
 
2.5%
12 245
 
2.5%
Other values (300) 6944
69.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 42
 
0.4%
2 151
1.5%
3 292
2.9%
4 329
3.3%
5 364
3.6%
6 354
3.5%
7 328
3.3%
8 307
3.1%
9 293
2.9%
ValueCountFrequency (%)
677 1
< 0.1%
645 1
< 0.1%
627 2
< 0.1%
537 1
< 0.1%
521 1
< 0.1%
518 1
< 0.1%
516 1
< 0.1%
471 1
< 0.1%
452 1
< 0.1%
449 1
< 0.1%

Interactions

2024-05-18T14:00:21.657734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:16.897430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:18.718876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:19.964980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:22.074039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:17.271202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:19.000004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:20.314861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:22.477974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:17.745526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:19.283140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:20.694528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:22.875245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:18.204157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:19.612683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:00:21.028527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:00:37.640149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드씠슜嫄댁닔씠룞嫄곕━(M)씠슜떆媛(遺
대여소번호1.0000.0230.0090.0680.0670.0940.067
대여구분코드0.0231.0000.1880.4100.1530.0950.109
성별0.0090.1881.0000.1650.0770.0680.027
연령대코드0.0680.4100.1651.0000.1800.1040.088
씠슜嫄댁닔0.0670.1530.0770.1801.0000.5710.678
씠룞嫄곕━(M)0.0940.0950.0680.1040.5711.0000.546
씠슜떆媛(遺0.0670.1090.0270.0880.6780.5461.000
2024-05-18T14:00:37.917261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0750.193
성별0.0751.0000.075
대여구분코드0.1930.0751.000
2024-05-18T14:00:38.129430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호씠슜嫄댁닔씠룞嫄곕━(M)씠슜떆媛(遺대여구분코드성별연령대코드
대여소번호1.000-0.023-0.041-0.0670.0140.0050.033
씠슜嫄댁닔-0.0231.0000.5990.6320.0970.0470.088
씠룞嫄곕━(M)-0.0410.5991.0000.7380.0570.0410.049
씠슜떆媛(遺-0.0670.6320.7381.0000.0700.0170.043
대여구분코드0.0140.0970.0570.0701.0000.0750.193
성별0.0050.0470.0410.0170.0751.0000.075
연령대코드0.0330.0880.0490.0430.1930.0751.000

Missing values

2024-05-18T14:00:23.565069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:00:24.499115image/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
60872021-12-01631631. 답십리역 1번출구정기MAGE_00118.540.07283.784
90502021-12-01950950. 구산역 2번 출구정기<NA>AGE_002141.920.331411.398
48432021-12-01514514. 성수사거리 버스정류장 앞정기FAGE_0026196.421.837833.0669
50442021-12-01538538. 답십리역 8번출구 앞정기FAGE_008125.720.261120.05
37672021-12-01411411. DMC홍보관일일(회원)MAGE_00810.000.000.041
68922021-12-01719719. 홍익병원앞 교차로일일(회원)FAGE_00210.000.000.08
97632021-12-0110291029. 롯데 시네마정기FAGE_002479.740.813501.8826
99912021-12-0110511051. 양지시장 (용성약국앞) 입구정기MAGE_001273.360.662850.0240
49322021-12-01524524. 래미안금호하이리버 아파트 102동 옆정기MAGE_003128.950.22937.216
63042021-12-01650650. 중랑교사거리정기MAGE_00227.040.07286.7612
대여일자대여소번호대여소대여구분코드성별연령대코드씠슜嫄댁닔슫룞깂냼씠룞嫄곕━(M)씠슜떆媛(遺
91532021-12-01962962. 은평뉴타운 힐데스하임정기FAGE_004244.380.431867.8147
17502021-12-01240240. 문래역 4번출구 앞정기FAGE_0083106.611.004283.053
65182021-12-01670670.삼육서울병원 버스정류장정기FAGE_0032152.241.596862.9139
16352021-12-01234234. 영등포구민체육센터 앞정기\NAGE_005236.670.321386.598
43462021-12-01466466.롯데호텔정기\NAGE_002194.180.793397.4315
78382021-12-01788788.양천구청역 2번출구 옆정기FAGE_003271.510.713050.022
108862021-12-0111391139. 용문사 버스정류장정기MAGE_008176.060.642743.7118
17372021-12-01240240. 문래역 4번출구 앞일일(회원)MAGE_004164.590.542330.029
43452021-12-01465465. 삼청공원 앞정기MAGE_006149.570.371604.9211
15392021-12-01228228. 선유도역 3번출구 앞정기MAGE_0035179.501.426131.1293