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 시군구명 is highly overall correlated with 배출횟수 and 2 other fieldsHigh correlation
지자체코드 is highly overall correlated with 배출횟수 and 2 other fieldsHigh correlation
지자체 시도명 is highly overall correlated with 지자체코드 and 1 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 2 other fieldsHigh correlation
배출횟수 is highly overall correlated with 배출량(g) and 4 other fieldsHigh correlation
일평균배출횟수 is highly overall correlated with 배출량(g) and 2 other fieldsHigh correlation
요일갯수 is highly overall correlated with 배출요일High correlation
지자체 시도명 is highly imbalanced (91.9%)Imbalance
배출량(g) has unique valuesUnique
일평균배출량(g) has unique valuesUnique
배출횟수 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:12:04.920601
Analysis finished2023-12-10 13:12:10.086203
Duration5.17 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
2020
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 100
100.0%

Length

2023-12-10T22:12:10.268045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:10.434944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 100
100.0%

배출월
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 100
100.0%

Length

2023-12-10T22:12:10.598426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:10.746326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 100
100.0%

배출요일
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.99
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:10.861933image/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 deviation2.0225746
Coefficient of variation (CV)0.50691093
Kurtosis-1.2714599
Mean3.99
Median Absolute Deviation (MAD)2
Skewness-0.0085335222
Sum399
Variance4.0908081
MonotonicityNot monotonic
2023-12-10T22:12:11.051654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 15
15.0%
1 15
15.0%
2 14
14.0%
3 14
14.0%
4 14
14.0%
5 14
14.0%
7 14
14.0%
ValueCountFrequency (%)
1 15
15.0%
2 14
14.0%
3 14
14.0%
4 14
14.0%
5 14
14.0%
6 15
15.0%
7 14
14.0%
ValueCountFrequency (%)
7 14
14.0%
6 15
15.0%
5 14
14.0%
4 14
14.0%
3 14
14.0%
2 14
14.0%
1 15
15.0%

요일갯수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4
86 
5
14 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 86
86.0%
5 14
 
14.0%

Length

2023-12-10T22:12:11.241973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:11.377172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 86
86.0%
5 14
 
14.0%

지자체코드
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
W01
7 
W02
7 
W03
7 
W04
7 
W05
7 
Other values (11)
65 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)2.0%

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 (6) 30
30.0%

Length

2023-12-10T22:12:11.534516image/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 (6) 30
30.0%

지자체 시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
99 
경기도
 
1

Length

Max length5
Median length5
Mean length4.98
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 99
99.0%
경기도 1
 
1.0%

Length

2023-12-10T22:12:11.751323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:11.931420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 99
99.0%
경기도 1
 
1.0%

지자체 시군구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로구
7 
중구
7 
용산구
7 
성동구
7 
광진구
7 
Other values (11)
65 

Length

Max length4
Median length3
Mean length3.07
Min length2

Unique

Unique2 ?
Unique (%)2.0%

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 (6) 30
30.0%

Length

2023-12-10T22:12:12.101580image/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 (6) 30
30.0%

배출량(g)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66438896
Minimum1934650
Maximum2.7496775 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:12.412457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1934650
5-th percentile2618312.5
Q122733200
median47645120
Q376772088
95-th percentile2.0619172 × 108
Maximum2.7496775 × 108
Range2.730331 × 108
Interquartile range (IQR)54038888

Descriptive statistics

Standard deviation64979745
Coefficient of variation (CV)0.9780377
Kurtosis1.6712987
Mean66438896
Median Absolute Deviation (MAD)25280945
Skewness1.5422679
Sum6.6438896 × 109
Variance4.2223673 × 1015
MonotonicityNot monotonic
2023-12-10T22:12:12.694833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15363550 1
 
1.0%
274967750 1
 
1.0%
186591700 1
 
1.0%
183812499 1
 
1.0%
187488250 1
 
1.0%
204679349 1
 
1.0%
234926789 1
 
1.0%
253776150 1
 
1.0%
187071200 1
 
1.0%
172156090 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1934650 1
1.0%
1944650 1
1.0%
2080450 1
1.0%
2163349 1
1.0%
2164449 1
1.0%
2642200 1
1.0%
2651750 1
1.0%
8977900 1
1.0%
8991060 1
1.0%
9286700 1
1.0%
ValueCountFrequency (%)
274967750 1
1.0%
253776150 1
1.0%
252101599 1
1.0%
235174190 1
1.0%
234926789 1
1.0%
204679349 1
1.0%
187488250 1
1.0%
187071200 1
1.0%
186591700 1
1.0%
183812499 1
1.0%

일평균배출량(g)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16154814
Minimum432670
Maximum68741938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:13.176359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum432670
5-th percentile654578.1
Q15625947.2
median11076072
Q319193022
95-th percentile47241588
Maximum68741938
Range68309268
Interquartile range (IQR)13567075

Descriptive statistics

Standard deviation15846980
Coefficient of variation (CV)0.98094477
Kurtosis1.8468593
Mean16154814
Median Absolute Deviation (MAD)6534936
Skewness1.56519
Sum1.6154814 × 109
Variance2.5112678 × 1014
MonotonicityNot monotonic
2023-12-10T22:12:13.444965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3840888 1
 
1.0%
68741938 1
 
1.0%
46647925 1
 
1.0%
45953125 1
 
1.0%
37497650 1
 
1.0%
51169837 1
 
1.0%
58731697 1
 
1.0%
63444038 1
 
1.0%
46767800 1
 
1.0%
43039023 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
432670 1
1.0%
483663 1
1.0%
486163 1
1.0%
520113 1
1.0%
541112 1
1.0%
660550 1
1.0%
662938 1
1.0%
1798212 1
1.0%
2244475 1
1.0%
2321675 1
1.0%
ValueCountFrequency (%)
68741938 1
1.0%
63444038 1
1.0%
63025400 1
1.0%
58731697 1
1.0%
51169837 1
1.0%
47034838 1
1.0%
46767800 1
1.0%
46647925 1
1.0%
45953125 1
1.0%
43357375 1
1.0%

배출횟수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39706.77
Minimum1501
Maximum173362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:13.879713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1501
5-th percentile1857.9
Q116048.5
median29841
Q349535.5
95-th percentile133917.3
Maximum173362
Range171861
Interquartile range (IQR)33487

Descriptive statistics

Standard deviation36161.577
Coefficient of variation (CV)0.91071566
Kurtosis2.9274237
Mean39706.77
Median Absolute Deviation (MAD)15859
Skewness1.6932285
Sum3970677
Variance1.3076597 × 109
MonotonicityNot monotonic
2023-12-10T22:12:14.154650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9819 1
 
1.0%
74974 1
 
1.0%
136830 1
 
1.0%
133826 1
 
1.0%
135652 1
 
1.0%
140800 1
 
1.0%
152639 1
 
1.0%
89058 1
 
1.0%
66118 1
 
1.0%
70898 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1501 1
1.0%
1534 1
1.0%
1602 1
1.0%
1604 1
1.0%
1609 1
1.0%
1871 1
1.0%
1920 1
1.0%
6021 1
1.0%
6136 1
1.0%
6240 1
1.0%
ValueCountFrequency (%)
173362 1
1.0%
152639 1
1.0%
140800 1
1.0%
136830 1
1.0%
135652 1
1.0%
133826 1
1.0%
128253 1
1.0%
89058 1
1.0%
87731 1
1.0%
86882 1
1.0%

일평균배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9667.56
Minimum322
Maximum43341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:14.419762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile464.65
Q13623.25
median6908
Q312383.75
95-th percentile32132.7
Maximum43341
Range43019
Interquartile range (IQR)8760.5

Descriptive statistics

Standard deviation8879.3167
Coefficient of variation (CV)0.91846512
Kurtosis3.1612734
Mean9667.56
Median Absolute Deviation (MAD)4083
Skewness1.7306006
Sum966756
Variance78842265
MonotonicityNot monotonic
2023-12-10T22:12:14.658452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
401 2
 
2.0%
16893 2
 
2.0%
2455 1
 
1.0%
33457 1
 
1.0%
27130 1
 
1.0%
35200 1
 
1.0%
38160 1
 
1.0%
22265 1
 
1.0%
16530 1
 
1.0%
17725 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
322 1
1.0%
375 1
1.0%
384 1
1.0%
401 2
2.0%
468 1
1.0%
480 1
1.0%
1204 1
1.0%
1534 1
1.0%
1560 1
1.0%
1583 1
1.0%
ValueCountFrequency (%)
43341 1
1.0%
38160 1
1.0%
35200 1
1.0%
34208 1
1.0%
33457 1
1.0%
32063 1
1.0%
27130 1
1.0%
22265 1
1.0%
21933 1
1.0%
21721 1
1.0%

Interactions

2023-12-10T22:12:08.986441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:05.618176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:06.338809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:07.053324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:07.941177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:09.136406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:05.758903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:06.479073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:07.207726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:08.082280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:09.269822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:05.883198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:06.634374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:07.368911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:08.210477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:09.413237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:06.037158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:06.790269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:07.565817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:08.697094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:09.548261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:06.206794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:06.933959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:07.772278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:12:08.848629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:12:14.793286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출요일요일갯수지자체코드지자체 시도명지자체 시군구명배출량(g)일평균배출량(g)배출횟수일평균배출횟수
배출요일1.0001.0000.0000.0000.0000.0000.0000.0000.000
요일갯수1.0001.0000.0000.0000.0000.0000.0000.0000.287
지자체코드0.0000.0001.0001.0001.0000.7970.7620.8600.820
지자체 시도명0.0000.0001.0001.0001.0000.0000.0000.0000.000
지자체 시군구명0.0000.0001.0001.0001.0000.7970.7620.8600.820
배출량(g)0.0000.0000.7970.0000.7971.0000.9960.9090.959
일평균배출량(g)0.0000.0000.7620.0000.7620.9961.0000.9000.970
배출횟수0.0000.0000.8600.0000.8600.9090.9001.0000.992
일평균배출횟수0.0000.2870.8200.0000.8200.9590.9700.9921.000
2023-12-10T22:12:14.980694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체 시군구명지자체코드지자체 시도명요일갯수
지자체 시군구명1.0001.0000.9260.000
지자체코드1.0001.0000.9260.000
지자체 시도명0.9260.9261.0000.000
요일갯수0.0000.0000.0001.000
2023-12-10T22:12:15.143449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출요일배출량(g)일평균배출량(g)배출횟수일평균배출횟수요일갯수지자체코드지자체 시도명지자체 시군구명
배출요일1.000-0.030-0.010-0.0160.0040.9740.0000.0000.000
배출량(g)-0.0301.0000.9960.9930.9890.0000.4540.0000.454
일평균배출량(g)-0.0100.9961.0000.9890.9930.0000.4140.0000.414
배출횟수-0.0160.9930.9891.0000.9960.0000.5640.0000.564
일평균배출횟수0.0040.9890.9930.9961.0000.2020.4850.0000.485
요일갯수0.9740.0000.0000.0000.2021.0000.0000.0000.000
지자체코드0.0000.4540.4140.5640.4850.0001.0000.9261.000
지자체 시도명0.0000.0000.0000.0000.0000.0000.9261.0000.926
지자체 시군구명0.0000.4540.4140.5640.4850.0001.0000.9261.000

Missing values

2023-12-10T22:12:09.717100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:12:09.953294image/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
02020224W01서울특별시종로구15363550384088898192455
12020235W01서울특별시종로구13798200275964092731855
22020244W01서울특별시종로구13110600327765089442236
32020254W01서울특별시종로구13433000335825091042276
42020264W01서울특별시종로구13081450327036389862247
52020274W01서울특별시종로구180834504520863118442961
62020214W02서울특별시중구339369008484225232815820
72020224W02서울특별시중구256110506402763183964599
82020235W02서울특별시중구228070504561410170153403
92020244W02서울특별시중구232819005820475173004325
배출년도배출월배출요일요일갯수지자체코드지자체 시도명지자체 시군구명배출량(g)일평균배출량(g)배출횟수일평균배출횟수
902020274W0D서울특별시서대문구78485350196213385307213268
912020214W0E서울특별시마포구155539099388847758688221721
922020224W0E서울특별시마포구101703700254259256757216893
932020235W0E서울특별시마포구124983590249967186675713351
942020244W0E서울특별시마포구108801429272003576621016553
952020254W0E서울특별시마포구112270008280675026757316893
962020264W0E서울특별시마포구107460510268651286413116033
972020274W0E서울특별시마포구155384340388460858773121933
982020214W0F서울특별시양천구4431770011079425245796145
992020214W01서울특별시종로구163366504084163102402560