Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows430
Duplicate rows (%)4.3%
Total size in memory498.0 KiB
Average record size in memory51.0 B

Variable types

Categorical2
Numeric3

Dataset

Description가축분뇨 전자인계관리시스템과 농림사업정보시스템(Agrix)간 연계하여 송/수신하고 있는 정보중 액비 인계정보(살포) 데이터입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15041935/fileData.do

Alerts

Dataset has 430 (4.3%) duplicate rowsDuplicates
배출량 is highly overall correlated with 운반자인수량 and 1 other fieldsHigh correlation
운반자인수량 is highly overall correlated with 배출량 and 1 other fieldsHigh correlation
운반자살포량 is highly overall correlated with 배출량 and 1 other fieldsHigh correlation
운반자살포량 has 110 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-12 09:20:34.716761
Analysis finished2023-12-12 09:20:36.600858
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

입력구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
5578 
U
4422 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowU
3rd rowU
4th rowU
5th rowI

Common Values

ValueCountFrequency (%)
I 5578
55.8%
U 4422
44.2%

Length

2023-12-12T18:20:36.677417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:20:36.813677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 5578
55.8%
u 4422
44.2%

배출량
Real number (ℝ)

HIGH CORRELATION 

Distinct685
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.281296
Minimum1
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:20:36.997382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.5
Q113.1775
median20
Q323
95-th percentile72
Maximum288
Range287
Interquartile range (IQR)9.8225

Descriptive statistics

Standard deviation31.265946
Coefficient of variation (CV)1.2876556
Kurtosis22.960243
Mean24.281296
Median Absolute Deviation (MAD)5
Skewness4.5571864
Sum242812.96
Variance977.55939
MonotonicityNot monotonic
2023-12-12T18:20:37.208729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 1287
 
12.9%
15.0 1255
 
12.6%
24.0 793
 
7.9%
8.0 502
 
5.0%
22.0 451
 
4.5%
21.0 429
 
4.3%
7.5 357
 
3.6%
20.0 345
 
3.5%
14.0 345
 
3.5%
7.0 322
 
3.2%
Other values (675) 3914
39.1%
ValueCountFrequency (%)
1.0 2
 
< 0.1%
2.0 12
 
0.1%
3.0 4
 
< 0.1%
3.5 1
 
< 0.1%
3.8 2
 
< 0.1%
4.0 18
 
0.2%
4.5 15
 
0.1%
5.0 125
1.2%
5.22 1
 
< 0.1%
5.25 1
 
< 0.1%
ValueCountFrequency (%)
288.0 1
 
< 0.1%
276.0 2
 
< 0.1%
275.0 6
 
0.1%
264.0 3
 
< 0.1%
253.0 1
 
< 0.1%
252.0 1
 
< 0.1%
250.0 2
 
< 0.1%
240.0 2
 
< 0.1%
230.0 4
 
< 0.1%
225.0 35
0.4%

처리방법
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
액비화
5547 
<NA>
4425 
퇴비화
 
28

Length

Max length4
Median length3
Mean length3.4425
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row액비화
2nd row<NA>
3rd row액비화
4th row<NA>
5th row액비화

Common Values

ValueCountFrequency (%)
액비화 5547
55.5%
<NA> 4425
44.2%
퇴비화 28
 
0.3%

Length

2023-12-12T18:20:37.391464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:20:37.524915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
액비화 5547
55.5%
na 4425
44.2%
퇴비화 28
 
0.3%

운반자인수량
Real number (ℝ)

HIGH CORRELATION 

Distinct685
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.281296
Minimum1
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:20:37.717783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.5
Q113.1775
median20
Q323
95-th percentile72
Maximum288
Range287
Interquartile range (IQR)9.8225

Descriptive statistics

Standard deviation31.265946
Coefficient of variation (CV)1.2876556
Kurtosis22.960243
Mean24.281296
Median Absolute Deviation (MAD)5
Skewness4.5571864
Sum242812.96
Variance977.55939
MonotonicityNot monotonic
2023-12-12T18:20:37.885810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 1287
 
12.9%
15.0 1255
 
12.6%
24.0 793
 
7.9%
8.0 502
 
5.0%
22.0 451
 
4.5%
21.0 429
 
4.3%
7.5 357
 
3.6%
20.0 345
 
3.5%
14.0 345
 
3.5%
7.0 322
 
3.2%
Other values (675) 3914
39.1%
ValueCountFrequency (%)
1.0 2
 
< 0.1%
2.0 12
 
0.1%
3.0 4
 
< 0.1%
3.5 1
 
< 0.1%
3.8 2
 
< 0.1%
4.0 18
 
0.2%
4.5 15
 
0.1%
5.0 125
1.2%
5.22 1
 
< 0.1%
5.25 1
 
< 0.1%
ValueCountFrequency (%)
288.0 1
 
< 0.1%
276.0 2
 
< 0.1%
275.0 6
 
0.1%
264.0 3
 
< 0.1%
253.0 1
 
< 0.1%
252.0 1
 
< 0.1%
250.0 2
 
< 0.1%
240.0 2
 
< 0.1%
230.0 4
 
< 0.1%
225.0 35
0.4%

운반자살포량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct685
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.077996
Minimum0
Maximum288
Zeros110
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:20:38.033781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.15
Q113
median20
Q323
95-th percentile72
Maximum288
Range288
Interquartile range (IQR)10

Descriptive statistics

Standard deviation31.345401
Coefficient of variation (CV)1.3018276
Kurtosis22.815799
Mean24.077996
Median Absolute Deviation (MAD)5
Skewness4.5360057
Sum240779.96
Variance982.53413
MonotonicityNot monotonic
2023-12-12T18:20:38.176333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 1279
 
12.8%
15.0 1230
 
12.3%
24.0 774
 
7.7%
8.0 501
 
5.0%
22.0 434
 
4.3%
21.0 425
 
4.2%
7.5 357
 
3.6%
14.0 345
 
3.5%
20.0 335
 
3.4%
7.0 322
 
3.2%
Other values (675) 3998
40.0%
ValueCountFrequency (%)
0.0 110
1.1%
1.0 2
 
< 0.1%
2.0 11
 
0.1%
3.0 4
 
< 0.1%
3.5 1
 
< 0.1%
3.8 2
 
< 0.1%
4.0 18
 
0.2%
4.5 15
 
0.1%
5.0 120
1.2%
5.22 1
 
< 0.1%
ValueCountFrequency (%)
288.0 1
 
< 0.1%
276.0 2
 
< 0.1%
275.0 6
 
0.1%
264.0 3
 
< 0.1%
253.0 1
 
< 0.1%
252.0 1
 
< 0.1%
250.0 2
 
< 0.1%
240.0 2
 
< 0.1%
230.0 4
 
< 0.1%
225.0 35
0.4%

Interactions

2023-12-12T18:20:35.943410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:35.225688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:35.569687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:36.069353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:35.355962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:35.696108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:36.200210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:35.464853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:35.819198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:20:38.590334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입력구분배출량처리방법운반자인수량운반자살포량
입력구분1.0000.3180.0480.3180.317
배출량0.3181.0000.0001.0001.000
처리방법0.0480.0001.0000.0000.000
운반자인수량0.3181.0000.0001.0001.000
운반자살포량0.3171.0000.0001.0001.000
2023-12-12T18:20:38.696177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입력구분처리방법
입력구분1.0000.030
처리방법0.0301.000
2023-12-12T18:20:38.806076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출량운반자인수량운반자살포량입력구분처리방법
배출량1.0001.0000.9800.2440.000
운반자인수량1.0001.0000.9800.2440.000
운반자살포량0.9800.9801.0000.2430.000
입력구분0.2440.2440.2431.0000.030
처리방법0.0000.0000.0000.0301.000

Missing values

2023-12-12T18:20:36.357759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:20:36.533306image/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

입력구분배출량처리방법운반자인수량운반자살포량
71778U9.0액비화9.09.0
47300U100.0<NA>100.0100.0
36990U23.0액비화23.023.0
28302U24.0<NA>24.024.0
40246I16.28액비화16.2816.28
32676I23.0액비화23.023.0
26136U19.73액비화19.7319.73
3531I7.0액비화7.07.0
48352I7.5<NA>7.57.5
47620U15.0액비화15.015.0
입력구분배출량처리방법운반자인수량운반자살포량
3782I60.0<NA>60.060.0
35488I6.0<NA>6.00.0
55777U23.0액비화23.023.0
95933U22.0<NA>22.022.0
70246I23.0액비화23.023.0
81488I12.0액비화12.012.0
41822U7.0<NA>7.07.0
40606I15.37<NA>15.3715.37
93081U23.0액비화23.023.0
83710U15.0<NA>15.015.0

Duplicate rows

Most frequently occurring

입력구분배출량처리방법운반자인수량운반자살포량# duplicates
177I23.0액비화23.023.0458
96I15.0액비화15.015.0389
201I24.0액비화24.024.0349
333U23.0액비화23.023.0331
334U23.0<NA>23.023.0325
299U15.0<NA>15.015.0310
298U15.0액비화15.015.0268
98I15.0<NA>15.015.0263
281U7.5액비화7.57.5224
146I22.0액비화22.022.0206