Overview

Dataset statistics

Number of variables4
Number of observations4220
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.2 KiB
Average record size in memory34.0 B

Variable types

DateTime1
Text1
Numeric2

Dataset

Description수도권매립지에 반입되는 연탄재 반입정보입니다.개방항목 : 마감년월, 지역명, 반입대수, 반입량(kg) 항목을 제공합니다.
Author수도권매립지관리공사
URLhttps://www.data.go.kr/data/15064379/fileData.do

Alerts

반입대수 is highly overall correlated with 반입량(kg)High correlation
반입량(kg) is highly overall correlated with 반입대수High correlation

Reproduction

Analysis started2024-04-13 11:53:08.006186
Analysis finished2024-04-13 11:53:11.842908
Duration3.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

마감년월
Date

Distinct285
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size33.1 KiB
Minimum1999-08-01 00:00:00
Maximum2024-03-01 00:00:00
2024-04-13T20:53:12.061044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:53:12.511898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

지역명
Text

Distinct51
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size33.1 KiB
2024-04-13T20:53:13.391852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.1291469
Min length5

Characters and Unicode

Total characters25865
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row서울시용산구
2nd row서울시용산구
3rd row서울시성북구
4th row서울시노원구
5th row서울시마포구
ValueCountFrequency (%)
서울시노원구 201
 
4.8%
서울시성북구 178
 
4.2%
경기도부천시 168
 
4.0%
인천시미추홀구 162
 
3.8%
경기도남양주시 160
 
3.8%
경기도과천시 152
 
3.6%
경기도의정부시 138
 
3.3%
경기도평택시 138
 
3.3%
경기도파주시 133
 
3.2%
서울시마포구 128
 
3.0%
Other values (41) 2662
63.1%
2024-04-13T20:53:14.688110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 4341
16.8%
구 2396
 
9.3%
도 2041
 
7.9%
기 1842
 
7.1%
경 1842
 
7.1%
서 1614
 
6.2%
울 1506
 
5.8%
천 1216
 
4.7%
인 753
 
2.9%
동 466
 
1.8%
Other values (57) 7848
30.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25865
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4341
16.8%
구 2396
 
9.3%
도 2041
 
7.9%
기 1842
 
7.1%
경 1842
 
7.1%
서 1614
 
6.2%
울 1506
 
5.8%
천 1216
 
4.7%
인 753
 
2.9%
동 466
 
1.8%
Other values (57) 7848
30.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25865
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4341
16.8%
구 2396
 
9.3%
도 2041
 
7.9%
기 1842
 
7.1%
경 1842
 
7.1%
서 1614
 
6.2%
울 1506
 
5.8%
천 1216
 
4.7%
인 753
 
2.9%
동 466
 
1.8%
Other values (57) 7848
30.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25865
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 4341
16.8%
구 2396
 
9.3%
도 2041
 
7.9%
기 1842
 
7.1%
경 1842
 
7.1%
서 1614
 
6.2%
울 1506
 
5.8%
천 1216
 
4.7%
인 753
 
2.9%
동 466
 
1.8%
Other values (57) 7848
30.3%

반입대수
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5272512
Minimum0
Maximum61
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-04-13T20:53:15.101071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q38
95-th percentile22
Maximum61
Range61
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.5317431
Coefficient of variation (CV)1.153892
Kurtosis5.3116604
Mean6.5272512
Median Absolute Deviation (MAD)2
Skewness2.1364569
Sum27545
Variance56.727155
MonotonicityNot monotonic
2024-04-13T20:53:15.541069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1106
26.2%
2 619
14.7%
3 391
 
9.3%
4 281
 
6.7%
5 242
 
5.7%
6 225
 
5.3%
7 169
 
4.0%
8 136
 
3.2%
9 117
 
2.8%
11 92
 
2.2%
Other values (40) 842
20.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1106
26.2%
2 619
14.7%
3 391
 
9.3%
4 281
 
6.7%
5 242
 
5.7%
6 225
 
5.3%
7 169
 
4.0%
8 136
 
3.2%
9 117
 
2.8%
ValueCountFrequency (%)
61 1
 
< 0.1%
51 1
 
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
46 2
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
42 4
0.1%
41 3
0.1%

반입량(kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct3618
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92168.114
Minimum0
Maximum882030
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size37.2 KiB
2024-04-13T20:53:15.964356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8549
Q117750
median44795
Q3116320
95-th percentile353576.5
Maximum882030
Range882030
Interquartile range (IQR)98570

Descriptive statistics

Standard deviation118291.39
Coefficient of variation (CV)1.2834307
Kurtosis6.9209846
Mean92168.114
Median Absolute Deviation (MAD)32200
Skewness2.4551756
Sum3.8894944 × 108
Variance1.3992853 × 1010
MonotonicityNot monotonic
2024-04-13T20:53:16.390280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11490 6
 
0.1%
10570 5
 
0.1%
16050 5
 
0.1%
13940 5
 
0.1%
11520 5
 
0.1%
16960 5
 
0.1%
13490 4
 
0.1%
9710 4
 
0.1%
12930 4
 
0.1%
10720 4
 
0.1%
Other values (3608) 4173
98.9%
ValueCountFrequency (%)
0 1
< 0.1%
1340 1
< 0.1%
1500 1
< 0.1%
2060 1
< 0.1%
2320 1
< 0.1%
2410 1
< 0.1%
2500 1
< 0.1%
2670 1
< 0.1%
2970 1
< 0.1%
2980 1
< 0.1%
ValueCountFrequency (%)
882030 1
< 0.1%
781410 1
< 0.1%
765210 1
< 0.1%
744440 1
< 0.1%
740630 1
< 0.1%
739180 1
< 0.1%
732100 1
< 0.1%
719760 1
< 0.1%
718420 1
< 0.1%
710930 1
< 0.1%

Interactions

2024-04-13T20:53:10.815045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:53:10.390671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:53:11.082084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:53:10.616208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T20:53:16.644659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명반입대수반입량(kg)
지역명1.0000.5330.559
반입대수0.5331.0000.944
반입량(kg)0.5590.9441.000
2024-04-13T20:53:16.884090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
반입대수반입량(kg)
반입대수1.0000.970
반입량(kg)0.9701.000

Missing values

2024-04-13T20:53:11.411927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T20:53:11.710569image/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

마감년월지역명반입대수반입량(kg)
01999-08서울시용산구236460
11999-10서울시용산구110290
21999-10서울시성북구10133190
31999-10서울시노원구670210
41999-10서울시마포구230040
51999-10서울시관악구989670
61999-10인천시미추홀구112060
71999-10인천시동구343680
81999-11서울시종로구228180
91999-11서울시용산구557090
마감년월지역명반입대수반입량(kg)
42102024-03경기도구리시112050
42112024-03경기도시흥시339400
42122024-03경기도안양시17600
42132024-03경기도오산시116090
42142024-03경기도의정부시17163320
42152024-03경기도파주시12117260
42162024-03경기도김포시19810
42172024-03경기도화성시8113530
42182024-03경기도평택시562920
42192024-03경기도광주시340110