Overview

Dataset statistics

Number of variables4
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory40.6 B

Variable types

Numeric1
Categorical3

Dataset

Description년도,발령횟수,발령일수,최대농도(ppm)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2229/S/1/datasetView.do

Alerts

발령횟수 has constant value ""Constant
발령일수 has constant value ""Constant
최대농도(ppm) is highly imbalanced (78.4%)Imbalance
년도 has unique valuesUnique

Reproduction

Analysis started2024-05-11 06:54:32.893329
Analysis finished2024-05-11 06:54:34.035824
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009
Minimum1995
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2024-05-11T15:54:34.182332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1996.4
Q12002
median2009
Q32016
95-th percentile2021.6
Maximum2023
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5146932
Coefficient of variation (CV)0.0042382744
Kurtosis-1.2
Mean2009
Median Absolute Deviation (MAD)7
Skewness0
Sum58261
Variance72.5
MonotonicityStrictly increasing
2024-05-11T15:54:34.410846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1995 1
 
3.4%
1996 1
 
3.4%
2023 1
 
3.4%
2022 1
 
3.4%
2021 1
 
3.4%
2020 1
 
3.4%
2019 1
 
3.4%
2018 1
 
3.4%
2017 1
 
3.4%
2016 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
1995 1
3.4%
1996 1
3.4%
1997 1
3.4%
1998 1
3.4%
1999 1
3.4%
2000 1
3.4%
2001 1
3.4%
2002 1
3.4%
2003 1
3.4%
2004 1
3.4%
ValueCountFrequency (%)
2023 1
3.4%
2022 1
3.4%
2021 1
3.4%
2020 1
3.4%
2019 1
3.4%
2018 1
3.4%
2017 1
3.4%
2016 1
3.4%
2015 1
3.4%
2014 1
3.4%

발령횟수
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
29 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29
100.0%

Length

2024-05-11T15:54:34.644781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:54:34.847920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
100.0%

발령일수
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
29 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29
100.0%

Length

2024-05-11T15:54:35.048613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:54:35.246145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
100.0%

최대농도(ppm)
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
<NA>
28 
0
 
1

Length

Max length4
Median length4
Mean length3.8965517
Min length1

Unique

Unique1 ?
Unique (%)3.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 28
96.6%
0 1
 
3.4%

Length

2024-05-11T15:54:35.430454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:54:35.636557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
96.6%
0 1
 
3.4%

Interactions

2024-05-11T15:54:33.396213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:54:35.742322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도
년도1.000
2024-05-11T15:54:35.879460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도최대농도(ppm)
년도1.000NaN
최대농도(ppm)NaN1.000

Missing values

2024-05-11T15:54:33.631075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:54:33.851454image/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

년도발령횟수발령일수최대농도(ppm)
0199500<NA>
1199600<NA>
2199700<NA>
3199800<NA>
4199900<NA>
5200000<NA>
6200100<NA>
7200200<NA>
8200300<NA>
9200400<NA>
년도발령횟수발령일수최대농도(ppm)
19201400<NA>
20201500<NA>
21201600<NA>
22201700<NA>
23201800<NA>
242019000
25202000<NA>
26202100<NA>
27202200<NA>
28202300<NA>