Overview

Dataset statistics

Number of variables6
Number of observations96
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory54.4 B

Variable types

Categorical3
Numeric3

Dataset

Description한국남동발전 분당발전 대기오염물질 배출 현황입니다. 대기오염물질의 허용기준 및 자체기준 등의 데이터를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15042780/fileData.do

Alerts

사업소명 has constant value ""Constant
허용기준(Nox)(ppm) has constant value ""Constant
자체기준(Nox)(ppm) has constant value ""Constant

Reproduction

Analysis started2023-12-12 04:44:09.705896
Analysis finished2023-12-12 04:44:11.551874
Duration1.85 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업소명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
분당발전
96 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
분당발전 96
100.0%

Length

2023-12-12T13:44:11.626848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:44:11.716486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분당발전 96
100.0%

호기
Real number (ℝ)

Distinct8
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:44:11.797702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median4.5
Q36.25
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3033157
Coefficient of variation (CV)0.51184793
Kurtosis-1.2398819
Mean4.5
Median Absolute Deviation (MAD)2
Skewness0
Sum432
Variance5.3052632
MonotonicityIncreasing
2023-12-12T13:44:11.896388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 12
12.5%
2 12
12.5%
3 12
12.5%
4 12
12.5%
5 12
12.5%
6 12
12.5%
7 12
12.5%
8 12
12.5%
ValueCountFrequency (%)
1 12
12.5%
2 12
12.5%
3 12
12.5%
4 12
12.5%
5 12
12.5%
6 12
12.5%
7 12
12.5%
8 12
12.5%
ValueCountFrequency (%)
8 12
12.5%
7 12
12.5%
6 12
12.5%
5 12
12.5%
4 12
12.5%
3 12
12.5%
2 12
12.5%
1 12
12.5%

월
Real number (ℝ)

Distinct12
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:44:12.025347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4701737
Coefficient of variation (CV)0.53387287
Kurtosis-1.2174168
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum624
Variance12.042105
MonotonicityNot monotonic
2023-12-12T13:44:12.135452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 8
8.3%
2 8
8.3%
3 8
8.3%
4 8
8.3%
5 8
8.3%
6 8
8.3%
7 8
8.3%
8 8
8.3%
9 8
8.3%
10 8
8.3%
Other values (2) 16
16.7%
ValueCountFrequency (%)
1 8
8.3%
2 8
8.3%
3 8
8.3%
4 8
8.3%
5 8
8.3%
6 8
8.3%
7 8
8.3%
8 8
8.3%
9 8
8.3%
10 8
8.3%
ValueCountFrequency (%)
12 8
8.3%
11 8
8.3%
10 8
8.3%
9 8
8.3%
8 8
8.3%
7 8
8.3%
6 8
8.3%
5 8
8.3%
4 8
8.3%
3 8
8.3%

허용기준(Nox)(ppm)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
100
96 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 96
100.0%

Length

2023-12-12T13:44:12.288401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:44:12.376474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 96
100.0%

자체기준(Nox)(ppm)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
55
96 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row55
2nd row55
3rd row55
4th row55
5th row55

Common Values

ValueCountFrequency (%)
55 96
100.0%

Length

2023-12-12T13:44:12.467275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:44:12.563128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
55 96
100.0%

평균(Nox)(ppm)
Real number (ℝ)

Distinct95
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.116875
Minimum29.29
Maximum66.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-12T13:44:12.681418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.29
5-th percentile32.3375
Q143.375
median49.54
Q355.1375
95-th percentile61.855
Maximum66.66
Range37.37
Interquartile range (IQR)11.7625

Descriptive statistics

Standard deviation8.8552997
Coefficient of variation (CV)0.18029037
Kurtosis-0.51326714
Mean49.116875
Median Absolute Deviation (MAD)6
Skewness-0.25450075
Sum4715.22
Variance78.416333
MonotonicityNot monotonic
2023-12-12T13:44:12.822601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.67 2
 
2.1%
61.73 1
 
1.0%
51.71 1
 
1.0%
53.44 1
 
1.0%
45.33 1
 
1.0%
46.44 1
 
1.0%
42.88 1
 
1.0%
48.13 1
 
1.0%
43.27 1
 
1.0%
52.85 1
 
1.0%
Other values (85) 85
88.5%
ValueCountFrequency (%)
29.29 1
1.0%
31.46 1
1.0%
31.87 1
1.0%
31.91 1
1.0%
32.12 1
1.0%
32.41 1
1.0%
32.8 1
1.0%
34.8 1
1.0%
34.9 1
1.0%
36.48 1
1.0%
ValueCountFrequency (%)
66.66 1
1.0%
66.65 1
1.0%
64.58 1
1.0%
63.83 1
1.0%
62.23 1
1.0%
61.73 1
1.0%
61.66 1
1.0%
61.58 1
1.0%
61.29 1
1.0%
60.47 1
1.0%

Interactions

2023-12-12T13:44:10.539647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:44:09.841712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:44:10.177311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:44:10.679619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:44:09.944565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:44:10.301301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:44:10.818910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:44:10.054347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:44:10.415397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:44:12.936807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호기월평균(Nox)(ppm)
호기1.0000.0000.312
월0.0001.0000.417
평균(Nox)(ppm)0.3120.4171.000
2023-12-12T13:44:13.054732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호기월평균(Nox)(ppm)
호기1.0000.000-0.054
월0.0001.000-0.406
평균(Nox)(ppm)-0.054-0.4061.000

Missing values

2023-12-12T13:44:10.981888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:44:11.491941image/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

사업소명호기월허용기준(Nox)(ppm)자체기준(Nox)(ppm)평균(Nox)(ppm)
0분당발전111005561.73
1분당발전121005564.58
2분당발전131005561.66
3분당발전141005558.3
4분당발전151005552.26
5분당발전161005550.8
6분당발전171005543.67
7분당발전181005536.48
8분당발전191005543.09
9분당발전1101005553.73
사업소명호기월허용기준(Nox)(ppm)자체기준(Nox)(ppm)평균(Nox)(ppm)
86분당발전831005545.64
87분당발전841005550.43
88분당발전851005548.27
89분당발전861005544.69
90분당발전871005531.91
91분당발전881005532.41
92분당발전891005539.14
93분당발전8101005540.57
94분당발전8111005548.42
95분당발전8121005555.71