Overview

Dataset statistics

Number of variables3
Number of observations3674
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.8 KiB
Average record size in memory25.0 B

Variable types

Numeric1
Categorical1
Text1

Dataset

Description물공급 가뭄체감지수 분석정보
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2783

Alerts

물공급체감지수 is highly imbalanced (72.5%)Imbalance

Reproduction

Analysis started2024-01-09 20:37:37.265037
Analysis finished2024-01-09 20:37:37.663017
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분석일자
Real number (ℝ)

Distinct22
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220612
Minimum20220601
Maximum20220622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.4 KiB
2024-01-10T05:37:37.727031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220601
5-th percentile20220602
Q120220606
median20220612
Q320220617
95-th percentile20220621
Maximum20220622
Range21
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.3451523
Coefficient of variation (CV)3.1379626 × 10-7
Kurtosis-1.2049755
Mean20220612
Median Absolute Deviation (MAD)5.5
Skewness0
Sum7.4290527 × 1010
Variance40.260958
MonotonicityDecreasing
2024-01-10T05:37:37.829634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20220622 167
 
4.5%
20220610 167
 
4.5%
20220601 167
 
4.5%
20220602 167
 
4.5%
20220603 167
 
4.5%
20220604 167
 
4.5%
20220605 167
 
4.5%
20220606 167
 
4.5%
20220607 167
 
4.5%
20220608 167
 
4.5%
Other values (12) 2004
54.5%
ValueCountFrequency (%)
20220601 167
4.5%
20220602 167
4.5%
20220603 167
4.5%
20220604 167
4.5%
20220605 167
4.5%
20220606 167
4.5%
20220607 167
4.5%
20220608 167
4.5%
20220609 167
4.5%
20220610 167
4.5%
ValueCountFrequency (%)
20220622 167
4.5%
20220621 167
4.5%
20220620 167
4.5%
20220619 167
4.5%
20220618 167
4.5%
20220617 167
4.5%
20220616 167
4.5%
20220615 167
4.5%
20220614 167
4.5%
20220613 167
4.5%

물공급체감지수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
정상
3407 
관심
 
196
주의
 
71

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 3407
92.7%
관심 196
 
5.3%
주의 71
 
1.9%

Length

2024-01-10T05:37:37.927713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:37:38.015525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 3407
92.7%
관심 196
 
5.3%
주의 71
 
1.9%

시군명
Text

Distinct167
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2024-01-10T05:37:38.213155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length6.7305389
Min length5

Characters and Unicode

Total characters24728
Distinct characters125
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

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도강릉시
2nd row강원도고성군
3rd row강원도동해시
4th row강원도삼척시
5th row강원도속초시
ValueCountFrequency (%)
강원도강릉시 22
 
0.6%
전라북도군산시 22
 
0.6%
전라남도구례군 22
 
0.6%
전라남도나주시 22
 
0.6%
전라남도담양군 22
 
0.6%
전라남도목포시 22
 
0.6%
전라남도무안군 22
 
0.6%
전라남도보성군 22
 
0.6%
전라남도순천시 22
 
0.6%
전라남도신안군 22
 
0.6%
Other values (157) 3454
94.0%
2024-01-10T05:37:38.555986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 3454
 
14.0%
시 2024
 
8.2%
군 1870
 
7.6%
경 1650
 
6.7%
남 1342
 
5.4%
북 1056
 
4.3%
상 924
 
3.7%
전 836
 
3.4%
라 792
 
3.2%
기 704
 
2.8%
Other values (115) 10076
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24728
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 3454
 
14.0%
시 2024
 
8.2%
군 1870
 
7.6%
경 1650
 
6.7%
남 1342
 
5.4%
북 1056
 
4.3%
상 924
 
3.7%
전 836
 
3.4%
라 792
 
3.2%
기 704
 
2.8%
Other values (115) 10076
40.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24728
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 3454
 
14.0%
시 2024
 
8.2%
군 1870
 
7.6%
경 1650
 
6.7%
남 1342
 
5.4%
북 1056
 
4.3%
상 924
 
3.7%
전 836
 
3.4%
라 792
 
3.2%
기 704
 
2.8%
Other values (115) 10076
40.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24728
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 3454
 
14.0%
시 2024
 
8.2%
군 1870
 
7.6%
경 1650
 
6.7%
남 1342
 
5.4%
북 1056
 
4.3%
상 924
 
3.7%
전 836
 
3.4%
라 792
 
3.2%
기 704
 
2.8%
Other values (115) 10076
40.7%

Interactions

2024-01-10T05:37:37.413637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:37:38.648150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석일자물공급체감지수
분석일자1.0000.142
물공급체감지수0.1421.000
2024-01-10T05:37:38.724579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석일자물공급체감지수
분석일자1.0000.097
물공급체감지수0.0971.000

Missing values

2024-01-10T05:37:37.530743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:37:37.621095image/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

분석일자물공급체감지수시군명
020220622정상강원도강릉시
120220622정상강원도고성군
220220622정상강원도동해시
320220622정상강원도삼척시
420220622정상강원도속초시
520220622정상강원도양구군
620220622정상강원도양양군
720220622정상강원도영월군
820220622정상강원도원주시
920220622정상강원도인제군
분석일자물공급체감지수시군명
366420220601정상충청북도단양군
366520220601정상충청북도보은군
366620220601정상충청북도영동군
366720220601정상충청북도옥천군
366820220601정상충청북도음성군
366920220601정상충청북도제천시
367020220601정상충청북도증평군
367120220601정상충청북도진천군
367220220601정상충청북도청주시
367320220601정상충청북도충주시