Overview

Dataset statistics

Number of variables3
Number of observations94
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory28.4 B

Variable types

Numeric2
Categorical1

Dataset

Description충청북도 농업기술원 농가경영기록장(농가의 소득을 증진시킬 수 있는 회원전용 농가경영 관리 프로그램)의 수입지출관련 이용자 접속기록, 거래, 거래처 등의 관리시스템으로 메세지일련번호, Count등을 제공합니다.
Author충청북도
URLhttps://www.data.go.kr/data/15050318/fileData.do

Alerts

비고1 is highly imbalanced (91.5%)Imbalance
메세지일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:38:43.324464
Analysis finished2023-12-12 14:38:43.916777
Duration0.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

메세지일련번호
Real number (ℝ)

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.07447
Minimum130
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T23:38:43.993783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile144.65
Q1164.25
median222
Q3292.75
95-th percentile312.35
Maximum317
Range187
Interquartile range (IQR)128.5

Descriptive statistics

Standard deviation63.377558
Coefficient of variation (CV)0.27910473
Kurtosis-1.6072154
Mean227.07447
Median Absolute Deviation (MAD)64.5
Skewness0.042671561
Sum21345
Variance4016.7148
MonotonicityStrictly increasing
2023-12-12T23:38:44.134645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130 1
 
1.1%
277 1
 
1.1%
292 1
 
1.1%
291 1
 
1.1%
290 1
 
1.1%
289 1
 
1.1%
288 1
 
1.1%
287 1
 
1.1%
286 1
 
1.1%
285 1
 
1.1%
Other values (84) 84
89.4%
ValueCountFrequency (%)
130 1
1.1%
132 1
1.1%
133 1
1.1%
135 1
1.1%
144 1
1.1%
145 1
1.1%
146 1
1.1%
148 1
1.1%
149 1
1.1%
150 1
1.1%
ValueCountFrequency (%)
317 1
1.1%
316 1
1.1%
315 1
1.1%
314 1
1.1%
313 1
1.1%
312 1
1.1%
311 1
1.1%
310 1
1.1%
309 1
1.1%
308 1
1.1%

Count
Real number (ℝ)

Distinct35
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.57447
Minimum1
Maximum3143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T23:38:44.264868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q122
median56
Q373
95-th percentile2848.2
Maximum3143
Range3142
Interquartile range (IQR)51

Descriptive statistics

Standard deviation769.02118
Coefficient of variation (CV)2.9287737
Kurtosis9.1371727
Mean262.57447
Median Absolute Deviation (MAD)20.5
Skewness3.2997864
Sum24682
Variance591393.58
MonotonicityNot monotonic
2023-12-12T23:38:44.373149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 14
14.9%
62 10
 
10.6%
23 7
 
7.4%
22 7
 
7.4%
74 5
 
5.3%
52 5
 
5.3%
73 5
 
5.3%
56 5
 
5.3%
51 4
 
4.3%
55 3
 
3.2%
Other values (25) 29
30.9%
ValueCountFrequency (%)
1 14
14.9%
2 1
 
1.1%
3 2
 
2.1%
21 1
 
1.1%
22 7
7.4%
23 7
7.4%
51 4
 
4.3%
52 5
 
5.3%
55 3
 
3.2%
56 5
 
5.3%
ValueCountFrequency (%)
3143 1
1.1%
3138 1
1.1%
3012 1
1.1%
2884 1
1.1%
2856 1
1.1%
2844 1
1.1%
2804 1
1.1%
101 2
2.1%
98 1
1.1%
93 1
1.1%

비고1
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
<NA>
93 
1
 
1

Length

Max length4
Median length4
Mean length3.9680851
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 93
98.9%
1 1
 
1.1%

Length

2023-12-12T23:38:44.492571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:38:44.573073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 93
98.9%
1 1
 
1.1%

Interactions

2023-12-12T23:38:43.618394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:43.403879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:43.708350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:43.515362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:38:44.618192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
메세지일련번호Count
메세지일련번호1.0000.047
Count0.0471.000
2023-12-12T23:38:44.689456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
메세지일련번호Count비고1
메세지일련번호1.000-0.371NaN
Count-0.3711.000NaN
비고1NaNNaN1.000

Missing values

2023-12-12T23:38:43.811998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:38:43.886468image/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

메세지일련번호Count비고1
013011
11321<NA>
21331<NA>
31351<NA>
4144101<NA>
5145101<NA>
614698<NA>
714893<NA>
814985<NA>
91501<NA>
메세지일련번호Count비고1
8430822<NA>
8530922<NA>
8631022<NA>
873111<NA>
883121<NA>
893131<NA>
9031422<NA>
913153138<NA>
9231622<NA>
933173143<NA>