Overview

Dataset statistics

Number of variables5
Number of observations215
Missing cells645
Missing cells (%)60.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory43.6 B

Variable types

DateTime1
Text1
Unsupported3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-14994/F/1/datasetView.do

Alerts

Unnamed: 2 has 215 (100.0%) missing valuesMissing
Unnamed: 3 has 215 (100.0%) missing valuesMissing
Unnamed: 4 has 215 (100.0%) missing valuesMissing
대여일시 has unique valuesUnique
대여건수 has unique valuesUnique
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 06:51:49.988389
Analysis finished2023-12-11 06:51:50.191821
Duration0.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일시
Date

UNIQUE 

Distinct215
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Minimum2020-07-01 00:00:00
Maximum2021-01-31 00:00:00
2023-12-11T15:51:50.493595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:51:50.623664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대여건수
Text

UNIQUE 

Distinct215
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T15:51:51.019515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.1488372
Min length7

Characters and Unicode

Total characters1752
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)100.0%

Sample

1st row 105,196
2nd row 107,842
3rd row 102,753
4th row 98,488
5th row 104,664
ValueCountFrequency (%)
105,196 1
 
0.5%
53,421 1
 
0.5%
69,098 1
 
0.5%
87,583 1
 
0.5%
81,258 1
 
0.5%
44,452 1
 
0.5%
34,590 1
 
0.5%
60,332 1
 
0.5%
60,290 1
 
0.5%
34,828 1
 
0.5%
Other values (205) 205
95.3%
2023-12-11T15:51:51.514012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
430
24.5%
, 215
12.3%
1 150
 
8.6%
2 127
 
7.2%
4 119
 
6.8%
9 116
 
6.6%
8 108
 
6.2%
0 107
 
6.1%
5 106
 
6.1%
7 92
 
5.3%
Other values (2) 182
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1107
63.2%
Space Separator 430
 
24.5%
Other Punctuation 215
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 150
13.6%
2 127
11.5%
4 119
10.7%
9 116
10.5%
8 108
9.8%
0 107
9.7%
5 106
9.6%
7 92
8.3%
6 91
8.2%
3 91
8.2%
Space Separator
ValueCountFrequency (%)
430
100.0%
Other Punctuation
ValueCountFrequency (%)
, 215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1752
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
430
24.5%
, 215
12.3%
1 150
 
8.6%
2 127
 
7.2%
4 119
 
6.8%
9 116
 
6.6%
8 108
 
6.2%
0 107
 
6.1%
5 106
 
6.1%
7 92
 
5.3%
Other values (2) 182
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
430
24.5%
, 215
12.3%
1 150
 
8.6%
2 127
 
7.2%
4 119
 
6.8%
9 116
 
6.6%
8 108
 
6.2%
0 107
 
6.1%
5 106
 
6.1%
7 92
 
5.3%
Other values (2) 182
10.4%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing215
Missing (%)100.0%
Memory size2.0 KiB

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing215
Missing (%)100.0%
Memory size2.0 KiB

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing215
Missing (%)100.0%
Memory size2.0 KiB

Missing values

2023-12-11T15:51:50.073174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:51:50.157605image/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

대여일시대여건수Unnamed: 2Unnamed: 3Unnamed: 4
02020-07-01105,196<NA><NA><NA>
12020-07-02107,842<NA><NA><NA>
22020-07-03102,753<NA><NA><NA>
32020-07-0498,488<NA><NA><NA>
42020-07-05104,664<NA><NA><NA>
52020-07-06106,825<NA><NA><NA>
62020-07-0791,407<NA><NA><NA>
72020-07-08105,876<NA><NA><NA>
82020-07-09102,828<NA><NA><NA>
92020-07-1090,880<NA><NA><NA>
대여일시대여건수Unnamed: 2Unnamed: 3Unnamed: 4
2052021-01-2234,963<NA><NA><NA>
2062021-01-2343,368<NA><NA><NA>
2072021-01-2450,024<NA><NA><NA>
2082021-01-2554,669<NA><NA><NA>
2092021-01-2626,272<NA><NA><NA>
2102021-01-2745,639<NA><NA><NA>
2112021-01-2822,745<NA><NA><NA>
2122021-01-2924,435<NA><NA><NA>
2132021-01-3028,379<NA><NA><NA>
2142021-01-3138,533<NA><NA><NA>