Overview

Dataset statistics

Number of variables3
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory27.7 B

Variable types

DateTime1
Numeric2

Dataset

Description서울특별시 중랑구의 중랑자원재활용선별센터 처리현황을 나타냅니다. 2017년도부터 2022년도 6월까지의 반입량과 재활용량을 나타냅니다.
Author서울특별시 중랑구
URLhttps://www.data.go.kr/data/15102587/fileData.do

Alerts

날짜 has unique valuesUnique
반입량(킬로그램) has unique valuesUnique
재활용량(킬로그램) has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:06:44.469389
Analysis finished2023-12-12 13:06:45.019070
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

날짜
Date

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
Minimum2017-01-01 00:00:00
Maximum2023-07-01 00:00:00
2023-12-12T22:06:45.081959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:06:45.212952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

반입량(킬로그램)
Real number (ℝ)

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1225427.8
Minimum842810
Maximum1499010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-12T22:06:45.340899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum842810
5-th percentile972211
Q11124640
median1231730
Q31347355
95-th percentile1472073
Maximum1499010
Range656200
Interquartile range (IQR)222715

Descriptive statistics

Standard deviation159565.69
Coefficient of variation (CV)0.13021223
Kurtosis-0.39852551
Mean1225427.8
Median Absolute Deviation (MAD)107850
Skewness-0.30218451
Sum96808798
Variance2.5461208 × 1010
MonotonicityNot monotonic
2023-12-12T22:06:45.513829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1308340 1
 
1.3%
975150 1
 
1.3%
1449180 1
 
1.3%
1466620 1
 
1.3%
1357780 1
 
1.3%
1256460 1
 
1.3%
1383190 1
 
1.3%
1323720 1
 
1.3%
1430870 1
 
1.3%
1482780 1
 
1.3%
Other values (69) 69
87.3%
ValueCountFrequency (%)
842810 1
1.3%
859410 1
1.3%
861940 1
1.3%
945760 1
1.3%
975150 1
1.3%
980990 1
1.3%
988730 1
1.3%
992960 1
1.3%
1003610 1
1.3%
1008280 1
1.3%
ValueCountFrequency (%)
1499010 1
1.3%
1483600 1
1.3%
1482780 1
1.3%
1473810 1
1.3%
1471880 1
1.3%
1466620 1
1.3%
1449180 1
1.3%
1437790 1
1.3%
1430870 1
1.3%
1425770 1
1.3%

재활용량(킬로그램)
Real number (ℝ)

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean453847.62
Minimum207781
Maximum873753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-12T22:06:45.670372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum207781
5-th percentile297554.4
Q1401185.5
median435537
Q3475816.5
95-th percentile725524.8
Maximum873753
Range665972
Interquartile range (IQR)74631

Descriptive statistics

Standard deviation121452.45
Coefficient of variation (CV)0.26760624
Kurtosis3.9808594
Mean453847.62
Median Absolute Deviation (MAD)37338
Skewness1.5667847
Sum35853962
Variance1.4750698 × 1010
MonotonicityNot monotonic
2023-12-12T22:06:45.843765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
401280 1
 
1.3%
394440 1
 
1.3%
463736 1
 
1.3%
422414 1
 
1.3%
421382 1
 
1.3%
412347 1
 
1.3%
439705 1
 
1.3%
430338 1
 
1.3%
426014 1
 
1.3%
477263 1
 
1.3%
Other values (69) 69
87.3%
ValueCountFrequency (%)
207781 1
1.3%
209823 1
1.3%
256174 1
1.3%
281718 1
1.3%
299314 1
1.3%
307048 1
1.3%
309021 1
1.3%
342774 1
1.3%
343315 1
1.3%
354765 1
1.3%
ValueCountFrequency (%)
873753 1
1.3%
853465 1
1.3%
831660 1
1.3%
791808 1
1.3%
718160 1
1.3%
695800 1
1.3%
645849 1
1.3%
546840 1
1.3%
526730 1
1.3%
525580 1
1.3%

Interactions

2023-12-12T22:06:44.728932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:06:44.542801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:06:44.818281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:06:44.627638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:06:45.960817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜반입량(킬로그램)재활용량(킬로그램)
날짜1.0001.0001.000
반입량(킬로그램)1.0001.0000.395
재활용량(킬로그램)1.0000.3951.000
2023-12-12T22:06:46.050810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
반입량(킬로그램)재활용량(킬로그램)
반입량(킬로그램)1.0000.344
재활용량(킬로그램)0.3441.000

Missing values

2023-12-12T22:06:44.923401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:06:44.990971image/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

날짜반입량(킬로그램)재활용량(킬로그램)
02017-01-011308340401280
12017-02-01975150394440
22017-03-011034730414760
32017-04-01945760379000
42017-05-011099870462240
52017-06-011095320435537
62017-07-011123880395860
72017-08-011151620441990
82017-09-011146190452680
92017-10-011093260441310
날짜반입량(킬로그램)재활용량(킬로그램)
692022-10-011270120428938
702022-11-011294140453814
712022-12-011231730436525
722023-01-011235230695800
732023-02-01992960645849
742023-03-011248040791808
752023-04-011180370718160
762023-05-011406650873753
772023-06-011241130853465
782023-07-011292290831660