Overview

Dataset statistics

Number of variables4
Number of observations58
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory37.2 B

Variable types

DateTime1
Numeric3

Dataset

Description서대문구 2019년 ~ 2023년 10월 5년간 구역별(1~3구역) 쓰레기(폐기물) 배출량에 대한 정보를 제공합니다.
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15107076/fileData.do

Alerts

2구역 is highly overall correlated with 3구역High correlation
3구역 is highly overall correlated with 2구역High correlation
구분 has unique valuesUnique
1구역 has unique valuesUnique
2구역 has unique valuesUnique
3구역 has unique valuesUnique

Reproduction

Analysis started2024-03-14 23:58:44.908096
Analysis finished2024-03-14 23:58:47.464205
Duration2.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size592.0 B
Minimum2019-01-01 00:00:00
Maximum2023-10-01 00:00:00
2024-03-15T08:58:47.592109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:47.964868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

1구역
Real number (ℝ)

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.6188
Minimum1670.32
Maximum2336.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size650.0 B
2024-03-15T08:58:48.315782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1670.32
5-th percentile1754.0135
Q11918.245
median1972.89
Q32121.0025
95-th percentile2293.029
Maximum2336.75
Range666.43
Interquartile range (IQR)202.7575

Descriptive statistics

Standard deviation158.17569
Coefficient of variation (CV)0.078905619
Kurtosis-0.31737914
Mean2004.6188
Median Absolute Deviation (MAD)93.455
Skewness0.29385605
Sum116267.89
Variance25019.548
MonotonicityNot monotonic
2024-03-15T08:58:48.759979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2151.6 1
 
1.7%
1918.05 1
 
1.7%
1882.1 1
 
1.7%
1920.49 1
 
1.7%
2075.91 1
 
1.7%
1976.73 1
 
1.7%
1842.63 1
 
1.7%
1670.32 1
 
1.7%
1946.72 1
 
1.7%
1864.96 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
1670.32 1
1.7%
1709.85 1
1.7%
1721.11 1
1.7%
1759.82 1
1.7%
1810.52 1
1.7%
1826.97 1
1.7%
1827.11 1
1.7%
1842.63 1
1.7%
1847.71 1
1.7%
1864.96 1
1.7%
ValueCountFrequency (%)
2336.75 1
1.7%
2320.92 1
1.7%
2303.45 1
1.7%
2291.19 1
1.7%
2272.05 1
1.7%
2264.5 1
1.7%
2215.99 1
1.7%
2211.7 1
1.7%
2194.65 1
1.7%
2183.64 1
1.7%

2구역
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1784.5412
Minimum1501.17
Maximum2043.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size650.0 B
2024-03-15T08:58:49.202171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1501.17
5-th percentile1602.178
Q11706.925
median1770.41
Q31871.6
95-th percentile1973.64
Maximum2043.42
Range542.25
Interquartile range (IQR)164.675

Descriptive statistics

Standard deviation119.44788
Coefficient of variation (CV)0.066934785
Kurtosis-0.27385329
Mean1784.5412
Median Absolute Deviation (MAD)84.46
Skewness-0.089380867
Sum103503.39
Variance14267.796
MonotonicityNot monotonic
2024-03-15T08:58:49.566071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1609.75 1
 
1.7%
1737.05 1
 
1.7%
1832.22 1
 
1.7%
1768.59 1
 
1.7%
1865.41 1
 
1.7%
1763.46 1
 
1.7%
1692.3 1
 
1.7%
1551.26 1
 
1.7%
1761.15 1
 
1.7%
1657.9 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
1501.17 1
1.7%
1551.26 1
1.7%
1559.27 1
1.7%
1609.75 1
1.7%
1612.2 1
1.7%
1620.93 1
1.7%
1629.48 1
1.7%
1645.61 1
1.7%
1655.33 1
1.7%
1657.9 1
1.7%
ValueCountFrequency (%)
2043.42 1
1.7%
2030.02 1
1.7%
1976.87 1
1.7%
1973.07 1
1.7%
1952.59 1
1.7%
1949.82 1
1.7%
1913.78 1
1.7%
1907.91 1
1.7%
1901.59 1
1.7%
1892.58 1
1.7%

3구역
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2321.4867
Minimum1967.54
Maximum2618.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size650.0 B
2024-03-15T08:58:49.825811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1967.54
5-th percentile2082.702
Q12214.9725
median2333.935
Q32442.465
95-th percentile2532.4865
Maximum2618.37
Range650.83
Interquartile range (IQR)227.4925

Descriptive statistics

Standard deviation149.15834
Coefficient of variation (CV)0.064251215
Kurtosis-0.45030556
Mean2321.4867
Median Absolute Deviation (MAD)115.085
Skewness-0.26126874
Sum134646.23
Variance22248.211
MonotonicityNot monotonic
2024-03-15T08:58:50.253838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2132.26 1
 
1.7%
2339.42 1
 
1.7%
2452.74 1
 
1.7%
2361.52 1
 
1.7%
2497.55 1
 
1.7%
2352.53 1
 
1.7%
2218.61 1
 
1.7%
2008.84 1
 
1.7%
2328.13 1
 
1.7%
2152.11 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
1967.54 1
1.7%
2008.84 1
1.7%
2040.1 1
1.7%
2090.22 1
1.7%
2123.52 1
1.7%
2125.23 1
1.7%
2125.35 1
1.7%
2132.26 1
1.7%
2152.11 1
1.7%
2153.72 1
1.7%
ValueCountFrequency (%)
2618.37 1
1.7%
2580.36 1
1.7%
2554.17 1
1.7%
2528.66 1
1.7%
2518.85 1
1.7%
2516.15 1
1.7%
2503.52 1
1.7%
2497.55 1
1.7%
2486.13 1
1.7%
2484.94 1
1.7%

Interactions

2024-03-15T08:58:46.417814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:45.077185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:45.821398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:46.657792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:45.328393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:46.070449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:46.966017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:45.573441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:58:46.213523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:58:50.585252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1구역2구역3구역
구분1.0001.0001.0001.000
1구역1.0001.0000.6200.561
2구역1.0000.6201.0000.967
3구역1.0000.5610.9671.000
2024-03-15T08:58:50.834848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1구역2구역3구역
1구역1.0000.4440.371
2구역0.4441.0000.929
3구역0.3710.9291.000

Missing values

2024-03-15T08:58:47.240084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:58:47.407575image/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

구분1구역2구역3구역
02019-012151.61609.752132.26
12019-021913.731501.171967.54
22019-032211.71655.332153.72
32019-042132.651629.482123.52
42019-052320.921824.32342.63
52019-062177.521705.082168.79
62019-072336.751872.142368.16
72019-082272.051837.022387.78
82019-092215.991730.832256.63
92019-102264.51728.172291.19
구분1구역2구역3구역
482023-011827.111764.982312.97
492023-021709.851559.272040.1
502023-031945.181712.462288.46
512023-041810.521620.932090.22
522023-052023.141821.632419.51
532023-061953.741780.232367.94
542023-072025.311880.02484.94
552023-081956.751852.312391.73
562023-091826.971612.22125.23
572023-102035.841874.842486.13