Overview

Dataset statistics

Number of variables4
Number of observations4384
Missing cells19
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory141.4 KiB
Average record size in memory33.0 B

Variable types

DateTime1
Text2
Numeric1

Dataset

Description국립중앙박물관 국립춘천박물관 2007년부터 2018년까지 관람객현황 "일자별, 대인 소인 구분, 이용객 수"을 제공합니다.
Author문화체육관광부 국립중앙박물관
URLhttps://www.data.go.kr/data/15036476/fileData.do

Alerts

계 has 209 (4.8%) zerosZeros

Reproduction

Analysis started2023-12-16 15:22:56.412842
Analysis finished2023-12-16 15:22:58.511860
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

Distinct4383
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Memory size34.4 KiB
Minimum2007-01-01 00:00:00
Maximum2018-12-31 00:00:00
2023-12-16T15:22:58.843121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:22:59.620676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대인
Text

Distinct885
Distinct (%)20.2%
Missing10
Missing (%)0.2%
Memory size34.4 KiB
2023-12-16T15:23:00.672054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.7471422
Min length1

Characters and Unicode

Total characters12016
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique224 ?
Unique (%)5.1%

Sample

1st row0
2nd row59
3rd row55
4th row70
5th row82
ValueCountFrequency (%)
0 218
 
5.0%
180 21
 
0.5%
55 18
 
0.4%
51 18
 
0.4%
47 17
 
0.4%
308 17
 
0.4%
137 16
 
0.4%
152 16
 
0.4%
131 16
 
0.4%
315 16
 
0.4%
Other values (875) 4001
91.5%
2023-12-16T15:23:02.219835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1790
14.9%
2 1723
14.3%
3 1551
12.9%
4 1226
10.2%
5 1076
9.0%
0 1075
8.9%
6 936
7.8%
7 893
7.4%
8 884
7.4%
9 860
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12014
> 99.9%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1790
14.9%
2 1723
14.3%
3 1551
12.9%
4 1226
10.2%
5 1076
9.0%
0 1075
8.9%
6 936
7.8%
7 893
7.4%
8 884
7.4%
9 860
7.2%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12016
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1790
14.9%
2 1723
14.3%
3 1551
12.9%
4 1226
10.2%
5 1076
9.0%
0 1075
8.9%
6 936
7.8%
7 893
7.4%
8 884
7.4%
9 860
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1790
14.9%
2 1723
14.3%
3 1551
12.9%
4 1226
10.2%
5 1076
9.0%
0 1075
8.9%
6 936
7.8%
7 893
7.4%
8 884
7.4%
9 860
7.2%

소인
Text

Distinct863
Distinct (%)19.7%
Missing8
Missing (%)0.2%
Memory size34.4 KiB
2023-12-16T15:23:03.510875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.5157678
Min length1

Characters and Unicode

Total characters11009
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)4.9%

Sample

1st row0
2nd row34
3rd row85
4th row116
5th row75
ValueCountFrequency (%)
0 388
 
8.9%
2 39
 
0.9%
3 34
 
0.8%
8 28
 
0.6%
11 27
 
0.6%
5 26
 
0.6%
22 25
 
0.6%
1 24
 
0.5%
7 23
 
0.5%
17 22
 
0.5%
Other values (853) 3740
85.5%
2023-12-16T15:23:05.335951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1820
16.5%
2 1440
13.1%
3 1222
11.1%
0 1135
10.3%
4 1061
9.6%
5 952
8.6%
6 904
8.2%
7 881
8.0%
8 826
7.5%
9 760
6.9%
Other values (3) 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11001
99.9%
Space Separator 4
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1820
16.5%
2 1440
13.1%
3 1222
11.1%
0 1135
10.3%
4 1061
9.6%
5 952
8.7%
6 904
8.2%
7 881
8.0%
8 826
7.5%
9 760
6.9%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1820
16.5%
2 1440
13.1%
3 1222
11.1%
0 1135
10.3%
4 1061
9.6%
5 952
8.6%
6 904
8.2%
7 881
8.0%
8 826
7.5%
9 760
6.9%
Other values (3) 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1820
16.5%
2 1440
13.1%
3 1222
11.1%
0 1135
10.3%
4 1061
9.6%
5 952
8.6%
6 904
8.2%
7 881
8.0%
8 826
7.5%
9 760
6.9%
Other values (3) 8
 
0.1%

계
Real number (ℝ)

ZEROS 

Distinct1321
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean563.97172
Minimum0
Maximum10765
Zeros209
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2023-12-16T15:23:06.231896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.15
Q1212
median451
Q3762.25
95-th percentile1443.1
Maximum10765
Range10765
Interquartile range (IQR)550.25

Descriptive statistics

Standard deviation550.43089
Coefficient of variation (CV)0.97599024
Kurtosis49.650106
Mean563.97172
Median Absolute Deviation (MAD)263
Skewness4.4682651
Sum2472452
Variance302974.16
MonotonicityNot monotonic
2023-12-16T15:23:07.130567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 209
 
4.8%
185 13
 
0.3%
430 12
 
0.3%
363 12
 
0.3%
400 12
 
0.3%
183 11
 
0.3%
482 11
 
0.3%
525 11
 
0.3%
35 10
 
0.2%
281 10
 
0.2%
Other values (1311) 4073
92.9%
ValueCountFrequency (%)
0 209
4.8%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
12 3
 
0.1%
13 3
 
0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
10765 1
< 0.1%
8396 1
< 0.1%
6373 1
< 0.1%
6060 1
< 0.1%
6011 1
< 0.1%
5709 1
< 0.1%
4453 2
< 0.1%
4274 1
< 0.1%
4251 1
< 0.1%
4090 1
< 0.1%

Interactions

2023-12-16T15:22:56.752383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-16T15:22:57.228431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:22:57.616821image/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.
2023-12-16T15:22:58.243302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분대인소인계
02007-01-01000
12007-01-02593493
22007-01-035585140
32007-01-0470116186
42007-01-058275157
52007-01-06130129259
62007-01-07165116281
72007-01-08000
82007-01-0963276339
92007-01-1043132175
구분대인소인계
43742018-12-22267231498
43752018-12-23423243666
43762018-12-242370237
43772018-12-256106631273
43782018-12-26502232734
43792018-12-27307114421
43802018-12-28225134359
43812018-12-29315317632
43822018-12-30488500988
43832018-12-31000