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 지자체코드High correlation
지자체코드 is highly overall correlated with 지자체 시군구명High 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 2 other fieldsHigh correlation
일평균배출횟수 is highly overall correlated with 배출량(g) and 2 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 13:11:53.636955
Analysis finished2023-12-10 13:11:58.443292
Duration4.81 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

2023-12-10T22:11:58.881969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:11:59.027705image/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
3
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 100
100.0%

Length

2023-12-10T22:11:59.202823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:11:59.382072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 100
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
2023-12-10T22:11:59.521938image/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
2023-12-10T22:11:59.712661image/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
58 
5
42 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 58
58.0%
5 42
42.0%

Length

2023-12-10T22:11:59.964972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:00.111557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 58
58.0%
5 42
42.0%

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

2023-12-10T22:12:00.284495image/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

2023-12-10T22:12:00.513533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:12:00.768468image/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

2023-12-10T22:12:00.966382image/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%
Mean71084332
Minimum1919150
Maximum3.383543 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:01.187881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1919150
5-th percentile2786927.5
Q123545114
median52579750
Q387883237
95-th percentile1.9571017 × 108
Maximum3.383543 × 108
Range3.3643515 × 108
Interquartile range (IQR)64338123

Descriptive statistics

Standard deviation69025611
Coefficient of variation (CV)0.97103833
Kurtosis2.8856855
Mean71084332
Median Absolute Deviation (MAD)29292525
Skewness1.7102457
Sum7.1084332 × 109
Variance4.764535 × 1015
MonotonicityNot monotonic
2023-12-10T22:12:01.431108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20434450 1
 
1.0%
270191850 1
 
1.0%
178626250 1
 
1.0%
175610050 1
 
1.0%
191057150 1
 
1.0%
233174700 1
 
1.0%
256880400 1
 
1.0%
291257700 1
 
1.0%
187458350 1
 
1.0%
182689600 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1919150 1
1.0%
1930650 1
1.0%
2121950 1
1.0%
2136700 1
1.0%
2675349 1
1.0%
2792800 1
1.0%
3028350 1
1.0%
8240350 1
1.0%
9447950 1
1.0%
9459010 1
1.0%
ValueCountFrequency (%)
338354299 1
1.0%
291257700 1
1.0%
270191850 1
1.0%
256880400 1
1.0%
233174700 1
1.0%
193738348 1
1.0%
191057150 1
1.0%
187458350 1
1.0%
182689600 1
1.0%
178626250 1
1.0%

일평균배출량(g)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16527956
Minimum383830
Maximum84588575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:01.673697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum383830
5-th percentile696731.85
Q14957442.2
median11557222
Q321052530
95-th percentile48927541
Maximum84588575
Range84204745
Interquartile range (IQR)16095088

Descriptive statistics

Standard deviation16723635
Coefficient of variation (CV)1.0118393
Kurtosis3.9475001
Mean16527956
Median Absolute Deviation (MAD)6955224.5
Skewness1.91173
Sum1.6527956 × 109
Variance2.7967998 × 1014
MonotonicityNot monotonic
2023-12-10T22:12:01.888276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5108613 1
 
1.0%
67547963 1
 
1.0%
35725250 1
 
1.0%
35122010 1
 
1.0%
38211430 1
 
1.0%
58293675 1
 
1.0%
64220100 1
 
1.0%
72814425 1
 
1.0%
46864588 1
 
1.0%
36537920 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
383830 1
1.0%
386130 1
1.0%
424390 1
1.0%
534175 1
1.0%
668837 1
1.0%
698200 1
1.0%
757088 1
1.0%
1648070 1
1.0%
1889590 1
1.0%
1891802 1
1.0%
ValueCountFrequency (%)
84588575 1
1.0%
72814425 1
1.0%
67547963 1
1.0%
64220100 1
1.0%
58293675 1
1.0%
48434587 1
1.0%
46864588 1
1.0%
44361248 1
1.0%
38211430 1
1.0%
36537920 1
1.0%

배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44906.1
Minimum1548
Maximum199823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:02.135517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1548
5-th percentile2104.55
Q117681.25
median34976
Q357862
95-th percentile137581.55
Maximum199823
Range198275
Interquartile range (IQR)40180.75

Descriptive statistics

Standard deviation40842.459
Coefficient of variation (CV)0.90950803
Kurtosis3.486144
Mean44906.1
Median Absolute Deviation (MAD)19402.5
Skewness1.7742946
Sum4490610
Variance1.6681064 × 109
MonotonicityNot monotonic
2023-12-10T22:12:02.399354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6674 2
 
2.0%
13578 1
 
1.0%
89594 1
 
1.0%
137439 1
 
1.0%
133833 1
 
1.0%
144215 1
 
1.0%
176609 1
 
1.0%
186973 1
 
1.0%
199823 1
 
1.0%
71651 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
1548 1
1.0%
1584 1
1.0%
1635 1
1.0%
1639 1
1.0%
2077 1
1.0%
2106 1
1.0%
2335 1
1.0%
5826 1
1.0%
6652 1
1.0%
6674 2
2.0%
ValueCountFrequency (%)
199823 1
1.0%
186973 1
1.0%
176609 1
1.0%
144215 1
1.0%
140290 1
1.0%
137439 1
1.0%
133833 1
1.0%
108026 1
1.0%
100314 1
1.0%
98199 1
1.0%

일평균배출횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10406.37
Minimum310
Maximum49956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:12:02.666642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum310
5-th percentile526.6
Q13623
median7870.5
Q313894
95-th percentile27555.75
Maximum49956
Range49646
Interquartile range (IQR)10271

Descriptive statistics

Standard deviation9812.5139
Coefficient of variation (CV)0.9429334
Kurtosis4.4735546
Mean10406.37
Median Absolute Deviation (MAD)4750
Skewness1.9383294
Sum1040637
Variance96285428
MonotonicityNot monotonic
2023-12-10T22:12:02.955352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1335 2
 
2.0%
3395 1
 
1.0%
22399 1
 
1.0%
27488 1
 
1.0%
26767 1
 
1.0%
28843 1
 
1.0%
44152 1
 
1.0%
46743 1
 
1.0%
49956 1
 
1.0%
17913 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
310 1
1.0%
317 1
1.0%
328 1
1.0%
409 1
1.0%
519 1
1.0%
527 1
1.0%
584 1
1.0%
1165 1
1.0%
1335 2
2.0%
1663 1
1.0%
ValueCountFrequency (%)
49956 1
1.0%
46743 1
1.0%
44152 1
1.0%
35073 1
1.0%
28843 1
1.0%
27488 1
1.0%
27007 1
1.0%
26767 1
1.0%
25079 1
1.0%
24550 1
1.0%

Interactions

2023-12-10T22:11:57.210832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:54.187588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:54.913853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:55.676784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:56.375686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:57.481365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:54.316626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:55.072208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:55.817299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:56.559690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:57.635009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:54.456487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:55.231148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:55.954393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:56.721065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:57.754459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:54.619198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:55.377504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:56.084976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:56.855879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:57.901741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:54.784327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:55.546082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:56.233335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:11:57.018040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:12:03.139379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출요일요일갯수지자체코드지자체 시군구명배출량(g)일평균배출량(g)배출횟수일평균배출횟수
배출요일1.0001.0000.0000.0000.0000.0000.0000.000
요일갯수1.0001.0000.0000.0000.2320.1880.1430.271
지자체코드0.0000.0001.0001.0000.7950.6700.8420.735
지자체 시군구명0.0000.0001.0001.0000.7950.6700.8420.735
배출량(g)0.0000.2320.7950.7951.0000.9950.9620.904
일평균배출량(g)0.0000.1880.6700.6700.9951.0000.9580.908
배출횟수0.0000.1430.8420.8420.9620.9581.0000.956
일평균배출횟수0.0000.2710.7350.7350.9040.9080.9561.000
2023-12-10T22:12:03.370321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체 시군구명지자체코드요일갯수
지자체 시군구명1.0001.0000.000
지자체코드1.0001.0000.000
요일갯수0.0000.0001.000
2023-12-10T22:12:03.581731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출요일배출량(g)일평균배출량(g)배출횟수일평균배출횟수요일갯수지자체코드지자체 시군구명
배출요일1.000-0.160-0.211-0.158-0.1980.9740.0000.000
배출량(g)-0.1601.0000.9910.9900.9850.1740.4310.431
일평균배출량(g)-0.2110.9911.0000.9790.9890.1480.3130.313
배출횟수-0.1580.9900.9791.0000.9920.1140.4940.494
일평균배출횟수-0.1980.9850.9890.9921.0000.2670.3960.396
요일갯수0.9740.1740.1480.1140.2671.0000.0000.000
지자체코드0.0000.4310.3130.4940.3960.0001.0001.000
지자체 시군구명0.0000.4310.3130.4940.3960.0001.0001.000

Missing values

2023-12-10T22:11:58.081121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:11:58.338032image/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
02020324W01서울특별시종로구204344505108613135783395
12020334W01서울특별시종로구175153504378838121773044
22020345W01서울특별시종로구13054800261096095461909
32020355W01서울특별시종로구13047500260950093151863
42020365W01서울특별시종로구13483750269675095131903
52020374W01서울특별시종로구13554650338866394022351
62020314W02서울특별시중구4079855010199638290627266
72020324W02서울특별시중구332370508309263253436336
82020334W02서울특별시중구282710997067775218445461
92020345W02서울특별시중구231597504631950183223664
배출년도배출월배출요일요일갯수지자체코드지자체 시도명지자체 시군구명배출량(g)일평균배출량(g)배출횟수일평균배출횟수
902020314W0E서울특별시마포구1774449904436124810802627007
912020324W0E서울특별시마포구134430547336076379429923575
922020334W0E서울특별시마포구145254450363136138602921507
932020345W0E서울특별시마포구108679190217358387112814226
942020355W0E서울특별시마포구104768750209537506748113496
952020365W0E서울특별시마포구106744349213488706881513763
962020374W0E서울특별시마포구116289380290723457078117695
972020314W0F서울특별시양천구5218725013046813309287732
982020324W0F서울특별시양천구7621215019053038291427286
992020314W01서울특별시종로구188844004721100127563189