Overview

Dataset statistics

Number of variables3
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory704.0 B
Average record size in memory32.0 B

Variable types

Categorical1
Numeric2

Dataset

Description구분,용량,판매가격(원)
Author강북구
URLhttps://data.seoul.go.kr/dataList/OA-11601/S/1/datasetView.do

Alerts

용량 is highly overall correlated with 판매가격(원)High correlation
판매가격(원) is highly overall correlated with 용량High correlation

Reproduction

Analysis started2024-04-06 10:06:00.131917
Analysis finished2024-04-06 10:06:01.194966
Duration1.06 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size308.0 B
생활폐기물용
6 
음식물납부확인증(소형음식점)
5 
음식물쓰레기용
4 
특수규격봉투
3 
재사용
2 

Length

Max length15
Median length14
Mean length8.6818182
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활폐기물용 6
27.3%
음식물납부확인증(소형음식점) 5
22.7%
음식물쓰레기용 4
18.2%
특수규격봉투 3
13.6%
재사용 2
 
9.1%
음식물납부확인증(공동주택) 2
 
9.1%

Length

2024-04-06T19:06:01.311089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:06:01.531350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활폐기물용 6
27.3%
음식물납부확인증(소형음식점 5
22.7%
음식물쓰레기용 4
18.2%
특수규격봉투 3
13.6%
재사용 2
 
9.1%
음식물납부확인증(공동주택 2
 
9.1%

용량
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.090909
Minimum1
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-06T19:06:01.786124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median20
Q350
95-th percentile117.75
Maximum120
Range119
Interquartile range (IQR)43.75

Descriptive statistics

Standard deviation35.828838
Coefficient of variation (CV)1.1164794
Kurtosis1.4966704
Mean32.090909
Median Absolute Deviation (MAD)15
Skewness1.4815891
Sum706
Variance1283.7056
MonotonicityNot monotonic
2024-04-06T19:06:02.546441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 4
18.2%
20 4
18.2%
5 3
13.6%
50 2
9.1%
60 2
9.1%
120 2
9.1%
30 1
 
4.5%
75 1
 
4.5%
1 1
 
4.5%
2 1
 
4.5%
ValueCountFrequency (%)
1 1
 
4.5%
2 1
 
4.5%
3 1
 
4.5%
5 3
13.6%
10 4
18.2%
20 4
18.2%
30 1
 
4.5%
50 2
9.1%
60 2
9.1%
75 1
 
4.5%
ValueCountFrequency (%)
120 2
9.1%
75 1
 
4.5%
60 2
9.1%
50 2
9.1%
30 1
 
4.5%
20 4
18.2%
10 4
18.2%
5 3
13.6%
3 1
 
4.5%
2 1
 
4.5%

판매가격(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2856.3636
Minimum100
Maximum16800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-06T19:06:02.842435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile133.5
Q1347.5
median880
Q32610
95-th percentile11820
Maximum16800
Range16700
Interquartile range (IQR)2262.5

Descriptive statistics

Standard deviation4372.3411
Coefficient of variation (CV)1.5307369
Kurtosis4.5539912
Mean2856.3636
Median Absolute Deviation (MAD)655
Skewness2.1946364
Sum62840
Variance19117367
MonotonicityNot monotonic
2024-04-06T19:06:03.060857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
490 2
 
9.1%
250 2
 
9.1%
130 1
 
4.5%
2800 1
 
4.5%
5100 1
 
4.5%
2040 1
 
4.5%
1020 1
 
4.5%
12000 1
 
4.5%
6000 1
 
4.5%
16800 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
100 1
4.5%
130 1
4.5%
200 1
4.5%
250 2
9.1%
300 1
4.5%
490 2
9.1%
500 1
4.5%
700 1
4.5%
740 1
4.5%
1020 1
4.5%
ValueCountFrequency (%)
16800 1
4.5%
12000 1
4.5%
8400 1
4.5%
6000 1
4.5%
5100 1
4.5%
2800 1
4.5%
2040 1
4.5%
1880 1
4.5%
1400 1
4.5%
1250 1
4.5%

Interactions

2024-04-06T19:06:00.673510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:06:00.371810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:06:00.845182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:06:00.515564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T19:06:03.198942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분용량판매가격(원)
구분1.0000.0000.512
용량0.0001.0000.694
판매가격(원)0.5120.6941.000
2024-04-06T19:06:03.352385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용량판매가격(원)구분
용량1.0000.8430.000
판매가격(원)0.8431.0000.301
구분0.0000.3011.000

Missing values

2024-04-06T19:06:01.026994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T19:06:01.145961image/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

구분용량판매가격(원)
0생활폐기물용5130
1생활폐기물용10250
2생활폐기물용20490
3생활폐기물용30740
4생활폐기물용501250
5생활폐기물용751880
6재사용10250
7재사용20490
8음식물쓰레기용1100
9음식물쓰레기용2200
구분용량판매가격(원)
12음식물납부확인증(소형음식점)5700
13음식물납부확인증(소형음식점)101400
14음식물납부확인증(소형음식점)202800
15음식물납부확인증(소형음식점)608400
16음식물납부확인증(소형음식점)12016800
17음식물납부확인증(공동주택)606000
18음식물납부확인증(공동주택)12012000
19특수규격봉투101020
20특수규격봉투202040
21특수규격봉투505100