Overview

Dataset statistics

Number of variables3
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory782.0 B
Average record size in memory31.3 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description한국가스공사의 역대 일일 최대생산량 순위(Top25)로 날짜별 생산량(톤)의 정보이며 생산량을 기준으로 내림차순으로 정렬되어 있습니다.
Author한국가스공사
URLhttps://www.data.go.kr/data/15049928/fileData.do

Alerts

생산량(Ton) has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:49:39.889502
Analysis finished2023-12-12 03:49:40.274705
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct4
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2021
13 
2018
2020
2022

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2018

Common Values

ValueCountFrequency (%)
2021 13
52.0%
2018 6
24.0%
2020 4
 
16.0%
2022 2
 
8.0%

Length

2023-12-12T12:49:40.352504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:49:40.480695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 13
52.0%
2018 6
24.0%
2020 4
 
16.0%
2022 2
 
8.0%

일자
Text

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T12:49:40.671664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters125
Distinct characters10
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

Unique20 ?
Unique (%)80.0%

Sample

1st row01-08
2nd row01-07
3rd row01-11
4th row01-09
5th row01-26
ValueCountFrequency (%)
01-12 3
 
12.0%
02-06 2
 
8.0%
01-08 1
 
4.0%
02-22 1
 
4.0%
12-15 1
 
4.0%
01-04 1
 
4.0%
01-29 1
 
4.0%
02-07 1
 
4.0%
01-05 1
 
4.0%
12-16 1
 
4.0%
Other values (12) 12
48.0%
2023-12-12T12:49:41.038864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30
24.0%
1 30
24.0%
- 25
20.0%
2 20
16.0%
6 5
 
4.0%
7 5
 
4.0%
9 3
 
2.4%
5 3
 
2.4%
4 2
 
1.6%
8 2
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100
80.0%
Dash Punctuation 25
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
30.0%
1 30
30.0%
2 20
20.0%
6 5
 
5.0%
7 5
 
5.0%
9 3
 
3.0%
5 3
 
3.0%
4 2
 
2.0%
8 2
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
24.0%
1 30
24.0%
- 25
20.0%
2 20
16.0%
6 5
 
4.0%
7 5
 
4.0%
9 3
 
2.4%
5 3
 
2.4%
4 2
 
1.6%
8 2
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
24.0%
1 30
24.0%
- 25
20.0%
2 20
16.0%
6 5
 
4.0%
7 5
 
4.0%
9 3
 
2.4%
5 3
 
2.4%
4 2
 
1.6%
8 2
 
1.6%

생산량(Ton)
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215226.56
Minimum204415
Maximum252357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T12:49:41.234582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum204415
5-th percentile204463
Q1205800
median210775
Q3221135
95-th percentile242317
Maximum252357
Range47942
Interquartile range (IQR)15335

Descriptive statistics

Standard deviation13129.573
Coefficient of variation (CV)0.0610035
Kurtosis1.8224213
Mean215226.56
Median Absolute Deviation (MAD)5684
Skewness1.5375887
Sum5380664
Variance1.723857 × 108
MonotonicityStrictly decreasing
2023-12-12T12:49:41.447435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
252357 1
 
4.0%
244012 1
 
4.0%
204415 1
 
4.0%
204434 1
 
4.0%
204579 1
 
4.0%
204582 1
 
4.0%
205091 1
 
4.0%
205398 1
 
4.0%
205800 1
 
4.0%
205986 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
204415 1
4.0%
204434 1
4.0%
204579 1
4.0%
204582 1
4.0%
205091 1
4.0%
205398 1
4.0%
205800 1
4.0%
205986 1
4.0%
206319 1
4.0%
207547 1
4.0%
ValueCountFrequency (%)
252357 1
4.0%
244012 1
4.0%
235537 1
4.0%
228462 1
4.0%
225595 1
4.0%
222084 1
4.0%
221135 1
4.0%
220198 1
4.0%
213998 1
4.0%
212605 1
4.0%

Interactions

2023-12-12T12:49:39.998839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:49:41.561741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도일자생산량(Ton)
년도1.0000.0000.000
일자0.0001.0000.960
생산량(Ton)0.0000.9601.000
2023-12-12T12:49:41.685051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생산량(Ton)년도
생산량(Ton)1.0000.000
년도0.0001.000

Missing values

2023-12-12T12:49:40.151181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:49:40.239606image/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

년도일자생산량(Ton)
0202101-08252357
1202101-07244012
2202101-11235537
3202101-09228462
4201801-26225595
5202101-06222084
6202101-12221135
7201801-25220198
8202102-17213998
9201801-24212605
년도일자생산량(Ton)
15202012-16207547
16202101-05206319
17201802-07205986
18202101-29205800
19202101-04205398
20202201-12205091
21202012-15204582
22202202-22204579
23202012-17204434
24202002-06204415