Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Categorical6
Numeric5

Alerts

배출년도 has constant value ""Constant
배출월 has constant value ""Constant
지자체 시도명 has constant value ""Constant
지자체 시군구명 is highly overall correlated with 일평균배출량(g) and 2 other fieldsHigh correlation
지자체코드 is highly overall correlated with 일평균배출량(g) and 2 other fieldsHigh correlation
배출요일 is highly overall correlated with 요일갯수High correlation
배출량(g) is highly overall correlated with 일평균배출량(g) and 2 other fieldsHigh correlation
일평균배출량(g) is highly overall correlated with 배출량(g) and 4 other fieldsHigh correlation
배출횟수 is highly overall correlated with 배출량(g) and 2 other fieldsHigh correlation
일평균배출횟수 is highly overall correlated with 배출량(g) and 4 other fieldsHigh correlation
요일갯수 is highly overall correlated with 배출요일High correlation
배출량(g) has unique valuesUnique
일평균배출량(g) has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:12:28.244070
Analysis finished2023-12-10 13:12:33.218679
Duration4.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

배출년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 100
100.0%

Length

2023-12-10T22:12:33.319003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:33.451899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 100
100.0%

배출월
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
12
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row12
4th row12
5th row12

Common Values

ValueCountFrequency (%)
12 100
100.0%

Length

2023-12-10T22:12:33.601786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:33.746388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12 100
100.0%

배출요일
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.95
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:33.863281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0219752
Coefficient of variation (CV)0.51189246
Kurtosis-1.2695622
Mean3.95
Median Absolute Deviation (MAD)2
Skewness0.032074997
Sum395
Variance4.0883838
MonotonicityNot monotonic
2023-12-10T22:12:34.045102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 15
15.0%
1 15
15.0%
3 14
14.0%
4 14
14.0%
5 14
14.0%
6 14
14.0%
7 14
14.0%
ValueCountFrequency (%)
1 15
15.0%
2 15
15.0%
3 14
14.0%
4 14
14.0%
5 14
14.0%
6 14
14.0%
7 14
14.0%
ValueCountFrequency (%)
7 14
14.0%
6 14
14.0%
5 14
14.0%
4 14
14.0%
3 14
14.0%
2 15
15.0%
1 15
15.0%

요일갯수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4
56 
5
44 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 56
56.0%
5 44
44.0%

Length

2023-12-10T22:12:34.259341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:34.466506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 56
56.0%
5 44
44.0%

지자체코드
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
W01
7 
W02
7 
W03
7 
W04
7 
W05
7 
Other values (10)
65 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW01
2nd rowW01
3rd rowW01
4th rowW01
5th rowW01

Common Values

ValueCountFrequency (%)
W01 7
 
7.0%
W02 7
 
7.0%
W03 7
 
7.0%
W04 7
 
7.0%
W05 7
 
7.0%
W06 7
 
7.0%
W07 7
 
7.0%
W08 7
 
7.0%
W09 7
 
7.0%
W0A 7
 
7.0%
Other values (5) 30
30.0%

Length

2023-12-10T22:12:34.646290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w01 7
 
7.0%
w02 7
 
7.0%
w03 7
 
7.0%
w04 7
 
7.0%
w05 7
 
7.0%
w06 7
 
7.0%
w07 7
 
7.0%
w08 7
 
7.0%
w09 7
 
7.0%
w0a 7
 
7.0%
Other values (5) 30
30.0%

지자체 시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 100
100.0%

Length

2023-12-10T22:12:34.877314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:35.027877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 100
100.0%

지자체 시군구명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로구
7 
중구
7 
용산구
7 
성동구
7 
광진구
7 
Other values (10)
65 

Length

Max length4
Median length3
Mean length3.07
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
종로구 7
 
7.0%
중구 7
 
7.0%
용산구 7
 
7.0%
성동구 7
 
7.0%
광진구 7
 
7.0%
동대문구 7
 
7.0%
중랑구 7
 
7.0%
성북구 7
 
7.0%
강북구 7
 
7.0%
도봉구 7
 
7.0%
Other values (5) 30
30.0%

Length

2023-12-10T22:12:35.189631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 7
 
7.0%
중구 7
 
7.0%
용산구 7
 
7.0%
성동구 7
 
7.0%
광진구 7
 
7.0%
동대문구 7
 
7.0%
중랑구 7
 
7.0%
성북구 7
 
7.0%
강북구 7
 
7.0%
도봉구 7
 
7.0%
Other values (5) 30
30.0%

배출량(g)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71260905
Minimum662200
Maximum3.8314145 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:35.461812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum662200
5-th percentile991022.5
Q123453538
median49101444
Q387421825
95-th percentile2.1815348 × 108
Maximum3.8314145 × 108
Range3.8247925 × 108
Interquartile range (IQR)63968288

Descriptive statistics

Standard deviation73652758
Coefficient of variation (CV)1.0335647
Kurtosis3.523092
Mean71260905
Median Absolute Deviation (MAD)29245730
Skewness1.8307663
Sum7.1260905 × 109
Variance5.4247288 × 1015
MonotonicityNot monotonic
2023-12-10T22:12:35.735554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18905900 1
 
1.0%
286129100 1
 
1.0%
163143100 1
 
1.0%
170258450 1
 
1.0%
186128200 1
 
1.0%
225440650 1
 
1.0%
256422949 1
 
1.0%
287031300 1
 
1.0%
217769950 1
 
1.0%
212979000 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
662200 1
1.0%
701000 1
1.0%
703700 1
1.0%
886400 1
1.0%
970550 1
1.0%
992100 1
1.0%
1255050 1
1.0%
8253510 1
1.0%
8434500 1
1.0%
8818060 1
1.0%
ValueCountFrequency (%)
383141450 1
1.0%
287031300 1
1.0%
286129100 1
1.0%
256422949 1
1.0%
225440650 1
1.0%
217769950 1
1.0%
216794649 1
1.0%
212979000 1
1.0%
196942489 1
1.0%
186128200 1
1.0%

일평균배출량(g)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15925610
Minimum165550
Maximum76628290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:35.971199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum165550
5-th percentile220441
Q15626739.5
median11137898
Q317484365
95-th percentile53292446
Maximum76628290
Range76462740
Interquartile range (IQR)11857626

Descriptive statistics

Standard deviation16006295
Coefficient of variation (CV)1.0050664
Kurtosis2.1757525
Mean15925610
Median Absolute Deviation (MAD)5657491
Skewness1.6231365
Sum1.592561 × 109
Variance2.5620148 × 1014
MonotonicityNot monotonic
2023-12-10T22:12:36.224502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3781180 1
 
1.0%
57225820 1
 
1.0%
40785775 1
 
1.0%
42564613 1
 
1.0%
46532050 1
 
1.0%
45088130 1
 
1.0%
51284590 1
 
1.0%
57406260 1
 
1.0%
54442488 1
 
1.0%
53244750 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
165550 1
1.0%
175250 1
1.0%
175925 1
1.0%
194110 1
1.0%
198420 1
1.0%
221600 1
1.0%
251010 1
1.0%
2063378 1
1.0%
2108625 1
1.0%
2111142 1
1.0%
ValueCountFrequency (%)
76628290 1
1.0%
57406260 1
1.0%
57225820 1
1.0%
54442488 1
1.0%
54198662 1
1.0%
53244750 1
1.0%
51284590 1
1.0%
46532050 1
1.0%
45133200 1
1.0%
45088130 1
1.0%

배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41206.13
Minimum528
Maximum185766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:36.805695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum528
5-th percentile808.9
Q116403.75
median30433.5
Q354834.75
95-th percentile125918.35
Maximum185766
Range185238
Interquartile range (IQR)38431

Descriptive statistics

Standard deviation38492.161
Coefficient of variation (CV)0.93413676
Kurtosis3.4335602
Mean41206.13
Median Absolute Deviation (MAD)18413.5
Skewness1.7705361
Sum4120613
Variance1.4816464 × 109
MonotonicityNot monotonic
2023-12-10T22:12:37.094733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
528 2
 
2.0%
12261 1
 
1.0%
67902 1
 
1.0%
122656 1
 
1.0%
126704 1
 
1.0%
135805 1
 
1.0%
164153 1
 
1.0%
178078 1
 
1.0%
185766 1
 
1.0%
65568 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
528 2
2.0%
535 1
1.0%
591 1
1.0%
731 1
1.0%
813 1
1.0%
884 1
1.0%
5525 1
1.0%
5540 1
1.0%
5544 1
1.0%
5985 1
1.0%
ValueCountFrequency (%)
185766 1
1.0%
178078 1
1.0%
164153 1
1.0%
135805 1
1.0%
126704 1
1.0%
125877 1
1.0%
122656 1
1.0%
103732 1
1.0%
97760 1
1.0%
93415 1
1.0%

일평균배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9232.55
Minimum132
Maximum37153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:37.328775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132
5-th percentile162.25
Q13839.5
median6086.5
Q311492.25
95-th percentile31479.35
Maximum37153
Range37021
Interquartile range (IQR)7652.75

Descriptive statistics

Standard deviation8418.613
Coefficient of variation (CV)0.9118405
Kurtosis2.5849609
Mean9232.55
Median Absolute Deviation (MAD)3913
Skewness1.6355395
Sum923255
Variance70873044
MonotonicityNot monotonic
2023-12-10T22:12:37.564446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1385 2
 
2.0%
132 2
 
2.0%
2452 1
 
1.0%
16976 1
 
1.0%
30664 1
 
1.0%
31676 1
 
1.0%
33951 1
 
1.0%
32831 1
 
1.0%
35616 1
 
1.0%
37153 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
132 2
2.0%
134 1
1.0%
146 1
1.0%
148 1
1.0%
163 1
1.0%
177 1
1.0%
1381 1
1.0%
1385 2
2.0%
1386 1
1.0%
1496 1
1.0%
ValueCountFrequency (%)
37153 1
1.0%
35616 1
1.0%
33951 1
1.0%
32831 1
1.0%
31676 1
1.0%
31469 1
1.0%
30664 1
1.0%
20746 1
1.0%
19552 1
1.0%
18683 1
1.0%

Interactions

2023-12-10T22:12:31.863240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:28.704232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:29.503927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:30.276107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:31.039632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:32.031765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:28.832547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:29.647583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:30.421753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:31.191513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:32.201547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:29.057842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:29.826470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:30.573948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:31.356931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:32.385980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:29.220157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:29.981986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:30.745451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:31.513041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:32.574750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:29.359875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:30.129687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:30.891113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:31.673539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:12:37.739867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출요일요일갯수지자체코드지자체 시군구명배출량(g)일평균배출량(g)배출횟수일평균배출횟수
배출요일1.0001.0000.0000.0000.0000.0000.0000.000
요일갯수1.0001.0000.0000.0000.2630.0000.3870.242
지자체코드0.0000.0001.0001.0000.7580.8240.8220.912
지자체 시군구명0.0000.0001.0001.0000.7580.8240.8220.912
배출량(g)0.0000.2630.7580.7581.0000.9800.8680.834
일평균배출량(g)0.0000.0000.8240.8240.9801.0000.8510.877
배출횟수0.0000.3870.8220.8220.8680.8511.0000.946
일평균배출횟수0.0000.2420.9120.9120.8340.8770.9461.000
2023-12-10T22:12:37.939574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체 시군구명지자체코드요일갯수
지자체 시군구명1.0001.0000.000
지자체코드1.0001.0000.000
요일갯수0.0000.0001.000
2023-12-10T22:12:38.073692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출요일배출량(g)일평균배출량(g)배출횟수일평균배출횟수요일갯수지자체코드지자체 시군구명
배출요일1.000-0.176-0.113-0.173-0.1230.9740.0000.000
배출량(g)-0.1761.0000.9940.9870.9820.2580.4200.420
일평균배출량(g)-0.1130.9941.0000.9810.9860.0000.5030.503
배출횟수-0.1730.9870.9811.0000.9940.2950.4680.468
일평균배출횟수-0.1230.9820.9860.9941.0000.1790.6790.679
요일갯수0.9740.2580.0000.2950.1791.0000.0000.000
지자체코드0.0000.4200.5030.4680.6790.0001.0001.000
지자체 시군구명0.0000.4200.5030.4680.6790.0001.0001.000

Missing values

2023-12-10T22:12:32.808118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:12:33.104977image/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

배출년도배출월배출요일요일갯수지자체코드지자체 시도명지자체 시군구명배출량(g)일평균배출량(g)배출횟수일평균배출횟수
020191225W01서울특별시종로구189059003781180122612452
120191235W01서울특별시종로구166838503336770111652233
220191244W01서울특별시종로구12715200317880087502188
320191254W01서울특별시종로구12538250313456386342159
420191264W01서울특별시종로구11806350295158882012050
520191274W01서울특별시종로구12216650305416381032026
620191215W02서울특별시중구413318508266370280945619
720191225W02서울특별시중구332815506656310241844837
820191235W02서울특별시중구281452005629040207824156
920191244W02서울특별시중구238640505966013177534438
배출년도배출월배출요일요일갯수지자체코드지자체 시도명지자체 시군구명배출량(g)일평균배출량(g)배출횟수일평균배출횟수
9020191215W0E서울특별시마포구1969424893938849810373220746
9120191225W0E서울특별시마포구134747688269495388866617733
9220191235W0E서울특별시마포구166062210332124428080116160
9320191244W0E서울특별시마포구116476900291192256706316766
9420191254W0E서울특별시마포구111091910277729786315915790
9520191264W0E서울특별시마포구109926648274816626223515559
9620191274W0E서울특별시마포구121772850304432136376115940
9720191215W0F서울특별시양천구5336385010672770281725634
9820191225W0F서울특별시양천구8620400017240800278315566
9920191215W01서울특별시종로구192641503852830117792356