Overview

Dataset statistics

Number of variables8
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory75.3 B

Variable types

Categorical1
Text1
Numeric6

Alerts

집계년도 has constant value ""Constant
연간부과량(천톤) is highly overall correlated with 부과액(백만원) and 3 other fieldsHigh correlation
부과액(백만원) is highly overall correlated with 연간부과량(천톤) and 3 other fieldsHigh correlation
평균단가(원/톤) is highly overall correlated with 처리원가(원/톤)High correlation
총괄원가(백만원) is highly overall correlated with 연간부과량(천톤) and 1 other fieldsHigh correlation
처리원가(원/톤) is highly overall correlated with 연간부과량(천톤) and 3 other fieldsHigh correlation
현실화율(%) is highly overall correlated with 연간부과량(천톤) and 2 other fieldsHigh correlation
시군명 has unique valuesUnique
연간부과량(천톤) has unique valuesUnique
부과액(백만원) has unique valuesUnique
총괄원가(백만원) has unique valuesUnique
처리원가(원/톤) has unique valuesUnique
현실화율(%) has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:14:45.375012
Analysis finished2023-12-10 21:14:48.741727
Duration3.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2021
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 31
100.0%

Length

2023-12-11T06:14:48.815232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:14:48.923934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 31
100.0%

시군명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T06:14:49.081465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters38
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

Unique31 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
가평군 1
 
3.2%
안양시 1
 
3.2%
하남시 1
 
3.2%
포천시 1
 
3.2%
평택시 1
 
3.2%
파주시 1
 
3.2%
이천시 1
 
3.2%
의정부시 1
 
3.2%
의왕시 1
 
3.2%
용인시 1
 
3.2%
Other values (21) 21
67.7%
2023-12-11T06:14:49.362747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

연간부과량(천톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47115.226
Minimum5574
Maximum139977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:14:49.476838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5574
5-th percentile6201
Q116081.5
median40752
Q365677
95-th percentile117505.5
Maximum139977
Range134403
Interquartile range (IQR)49595.5

Descriptive statistics

Standard deviation37736.743
Coefficient of variation (CV)0.80094581
Kurtosis0.19285786
Mean47115.226
Median Absolute Deviation (MAD)25278
Skewness0.99062733
Sum1460572
Variance1.4240618 × 109
MonotonicityNot monotonic
2023-12-11T06:14:49.588883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6109 1
 
3.2%
100154 1
 
3.2%
77956 1
 
3.2%
28349 1
 
3.2%
12919 1
 
3.2%
56155 1
 
3.2%
41769 1
 
3.2%
20426 1
 
3.2%
44513 1
 
3.2%
16689 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
5574 1
3.2%
6109 1
3.2%
6293 1
3.2%
7097 1
3.2%
8728 1
3.2%
12919 1
3.2%
15065 1
3.2%
15474 1
3.2%
16689 1
3.2%
20426 1
3.2%
ValueCountFrequency (%)
139977 1
3.2%
132331 1
3.2%
102680 1
3.2%
100730 1
3.2%
100154 1
3.2%
87298 1
3.2%
77956 1
3.2%
66639 1
3.2%
64715 1
3.2%
56949 1
3.2%

부과액(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28641.452
Minimum1707
Maximum74336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:14:49.694643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1707
5-th percentile4563
Q113111
median24116
Q341919
95-th percentile65716.5
Maximum74336
Range72629
Interquartile range (IQR)28808

Descriptive statistics

Standard deviation20757.502
Coefficient of variation (CV)0.72473638
Kurtosis-0.55898417
Mean28641.452
Median Absolute Deviation (MAD)14203
Skewness0.66874012
Sum887885
Variance4.3087388 × 108
MonotonicityNot monotonic
2023-12-11T06:14:50.052428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5416 1
 
3.2%
61269 1
 
3.2%
63272 1
 
3.2%
14594 1
 
3.2%
11628 1
 
3.2%
38319 1
 
3.2%
41986 1
 
3.2%
16143 1
 
3.2%
25320 1
 
3.2%
15561 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1707 1
3.2%
3962 1
3.2%
5164 1
3.2%
5416 1
3.2%
5681 1
3.2%
8193 1
3.2%
10085 1
3.2%
11628 1
3.2%
14594 1
3.2%
14665 1
3.2%
ValueCountFrequency (%)
74336 1
3.2%
68161 1
3.2%
63272 1
3.2%
61269 1
3.2%
57233 1
3.2%
48075 1
3.2%
42093 1
3.2%
41986 1
3.2%
41852 1
3.2%
38319 1
3.2%

평균단가(원/톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean657.12903
Minimum196
Maximum1041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:14:50.177009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum196
5-th percentile425.5
Q1530.5
median647
Q3806.5
95-th percentile968.5
Maximum1041
Range845
Interquartile range (IQR)276

Descriptive statistics

Standard deviation189.793
Coefficient of variation (CV)0.28882151
Kurtosis0.00027230604
Mean657.12903
Median Absolute Deviation (MAD)132
Skewness0.071820759
Sum20371
Variance36021.383
MonotonicityNot monotonic
2023-12-11T06:14:50.290387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
812 2
 
6.5%
887 1
 
3.2%
562 1
 
3.2%
515 1
 
3.2%
900 1
 
3.2%
682 1
 
3.2%
1005 1
 
3.2%
790 1
 
3.2%
569 1
 
3.2%
932 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
196 1
3.2%
418 1
3.2%
433 1
3.2%
470 1
3.2%
479 1
3.2%
515 1
3.2%
519 1
3.2%
530 1
3.2%
531 1
3.2%
545 1
3.2%
ValueCountFrequency (%)
1041 1
3.2%
1005 1
3.2%
932 1
3.2%
900 1
3.2%
887 1
3.2%
821 1
3.2%
812 2
6.5%
801 1
3.2%
790 1
3.2%
711 1
3.2%

총괄원가(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60912.903
Minimum14882
Maximum163115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:14:50.393547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14882
5-th percentile18487
Q140982.5
median51627
Q380372
95-th percentile105191.5
Maximum163115
Range148233
Interquartile range (IQR)39389.5

Descriptive statistics

Standard deviation33861.58
Coefficient of variation (CV)0.5559016
Kurtosis1.1268881
Mean60912.903
Median Absolute Deviation (MAD)23999
Skewness0.90653612
Sum1888300
Variance1.1466066 × 109
MonotonicityNot monotonic
2023-12-11T06:14:50.503659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
43693 1
 
3.2%
102389 1
 
3.2%
107622 1
 
3.2%
42982 1
 
3.2%
52189 1
 
3.2%
101616 1
 
3.2%
90414 1
 
3.2%
41194 1
 
3.2%
51627 1
 
3.2%
16807 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
14882 1
3.2%
16807 1
3.2%
20167 1
3.2%
21266 1
3.2%
23227 1
3.2%
25025 1
3.2%
31798 1
3.2%
40771 1
3.2%
41194 1
3.2%
42436 1
3.2%
ValueCountFrequency (%)
163115 1
3.2%
107622 1
3.2%
102761 1
3.2%
102389 1
3.2%
101616 1
3.2%
94713 1
3.2%
90414 1
3.2%
82105 1
3.2%
78639 1
3.2%
75626 1
3.2%

처리원가(원/톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2034.3226
Minimum572
Maximum7152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:14:50.611605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum572
5-th percentile693
Q11004
median1381
Q32319.5
95-th percentile6257.5
Maximum7152
Range6580
Interquartile range (IQR)1315.5

Descriptive statistics

Standard deviation1707.0173
Coefficient of variation (CV)0.83910847
Kurtosis3.5354478
Mean2034.3226
Median Absolute Deviation (MAD)600
Skewness2.0062728
Sum63064
Variance2913908.1
MonotonicityNot monotonic
2023-12-11T06:14:50.721979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7152 1
 
3.2%
1022 1
 
3.2%
1381 1
 
3.2%
1516 1
 
3.2%
4040 1
 
3.2%
1810 1
 
3.2%
2165 1
 
3.2%
2017 1
 
3.2%
1160 1
 
3.2%
1007 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
572 1
3.2%
677 1
3.2%
709 1
3.2%
744 1
3.2%
781 1
3.2%
821 1
3.2%
991 1
3.2%
1001 1
3.2%
1007 1
3.2%
1022 1
3.2%
ValueCountFrequency (%)
7152 1
3.2%
6810 1
3.2%
5705 1
3.2%
4040 1
3.2%
2867 1
3.2%
2817 1
3.2%
2534 1
3.2%
2365 1
3.2%
2274 1
3.2%
2165 1
3.2%

현실화율(%)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.483871
Minimum6.8
Maximum92.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T06:14:50.830289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile12.1
Q134.35
median45.8
Q357.65
95-th percentile77.7
Maximum92.6
Range85.8
Interquartile range (IQR)23.3

Descriptive statistics

Standard deviation20.930649
Coefficient of variation (CV)0.46017739
Kurtosis-0.17842621
Mean45.483871
Median Absolute Deviation (MAD)11.8
Skewness0.099803976
Sum1410
Variance438.09206
MonotonicityNot monotonic
2023-12-11T06:14:50.935230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
12.4 1
 
3.2%
59.8 1
 
3.2%
58.8 1
 
3.2%
34.0 1
 
3.2%
22.3 1
 
3.2%
37.7 1
 
3.2%
46.4 1
 
3.2%
39.2 1
 
3.2%
49.0 1
 
3.2%
92.6 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
6.8 1
3.2%
11.8 1
3.2%
12.4 1
3.2%
12.5 1
3.2%
22.3 1
3.2%
23.8 1
3.2%
32.0 1
3.2%
34.0 1
3.2%
34.7 1
3.2%
37.7 1
3.2%
ValueCountFrequency (%)
92.6 1
3.2%
78.5 1
3.2%
76.9 1
3.2%
75.7 1
3.2%
67.1 1
3.2%
63.1 1
3.2%
59.8 1
3.2%
58.8 1
3.2%
56.5 1
3.2%
54.4 1
3.2%

Interactions

2023-12-11T06:14:48.060802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:45.581374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.012664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.544869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.027510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.554703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:48.156867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:45.660921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.102708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.630843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.107667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.644359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:48.269720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:45.736765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.212134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.713263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.205763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.741922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:48.347219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:45.802113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.281144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.800275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.276437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.820746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:48.427305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:45.877663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.360996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.888607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.365332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.906079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:48.493863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:45.950577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.443254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:46.962064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.463579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:14:47.989536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:14:51.012266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명연간부과량(천톤)부과액(백만원)평균단가(원/톤)총괄원가(백만원)처리원가(원/톤)현실화율(%)
시군명1.0001.0001.0001.0001.0001.0001.000
연간부과량(천톤)1.0001.0000.8250.3150.6440.0000.736
부과액(백만원)1.0000.8251.0000.0000.7190.0000.889
평균단가(원/톤)1.0000.3150.0001.0000.3750.7400.000
총괄원가(백만원)1.0000.6440.7190.3751.0000.0000.000
처리원가(원/톤)1.0000.0000.0000.7400.0001.0000.640
현실화율(%)1.0000.7360.8890.0000.0000.6401.000
2023-12-11T06:14:51.120817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연간부과량(천톤)부과액(백만원)평균단가(원/톤)총괄원가(백만원)처리원가(원/톤)현실화율(%)
연간부과량(천톤)1.0000.953-0.3760.758-0.6910.671
부과액(백만원)0.9531.000-0.1020.850-0.5430.634
평균단가(원/톤)-0.376-0.1021.0000.0970.620-0.264
총괄원가(백만원)0.7580.8500.0971.000-0.0910.182
처리원가(원/톤)-0.691-0.5430.620-0.0911.000-0.902
현실화율(%)0.6710.634-0.2640.182-0.9021.000

Missing values

2023-12-11T06:14:48.582439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:14:48.695333image/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

집계년도시군명연간부과량(천톤)부과액(백만원)평균단가(원/톤)총괄원가(백만원)처리원가(원/톤)현실화율(%)
02021가평군6109541688743693715212.4
12021고양시10015461269612102389102259.8
22021과천시6293516482114882236534.7
32021광명시28463155065452016770976.9
42021광주시3611137580104182105227445.8
52021구리시407521951347940771100147.9
62021군포시31227146654702322774463.1
72021김포시413632847268850430121956.5
82021남양주시6471541852647102761158840.7
92021동두천시15474819353021266137438.5
집계년도시군명연간부과량(천톤)부과액(백만원)평균단가(원/톤)총괄원가(백만원)처리원가(원/톤)현실화율(%)
212021오산시416902411657944323106354.4
222021용인시10268068161664163115158941.8
232021의왕시166891556193216807100792.6
242021의정부시445132532056951627116049.0
252021이천시204261614379041194201739.2
262021파주시4176941986100590414216546.4
272021평택시5615538319682101616181037.7
282021포천시129191162890052189404022.3
292021하남시283491459451542982151634.0
302021화성시7795663272812107622138158.8