Overview

Dataset statistics

Number of variables4
Number of observations9698
Missing cells0
Missing cells (%)0.0%
Duplicate rows738
Duplicate rows (%)7.6%
Total size in memory312.7 KiB
Average record size in memory33.0 B

Variable types

Categorical1
DateTime2
Numeric1

Dataset

Description2020년 2월 27일 기준 괴산군 강우량정보 (지역명, 날짜, 가우량) 데이터파일입니다. 자세한 사항은 괴산군청으로 문의 주시기 바랍니다.
Author충청북도 괴산군
URLhttps://www.data.go.kr/data/15049989/fileData.do

Alerts

ͱ has constant value ""Constant
Dataset has 738 (7.6%) duplicate rowsDuplicates
췮 X 0.01 (mm) has 6135 (63.3%) zerosZeros

Reproduction

Analysis started2023-12-12 15:23:49.658990
Analysis finished2023-12-12 15:23:50.125645
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
<NA>
3528 
忬
882 
dz
882 
ûõ
882 
ûȸ
882 
Other values (3)
2642 

Length

Max length4
Median length2
Mean length2.2732522
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3528
36.4%
忬 882
 
9.1%
dz 882
 
9.1%
ûõ 882
 
9.1%
ûȸ 882
 
9.1%
縮 882
 
9.1%
Ҽ 882
 
9.1%
ĥ 878
 
9.1%

Length

2023-12-13T00:23:50.230190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:23:50.388631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3528
36.4%
忬 882
 
9.1%
dz 882
 
9.1%
ûõ 882
 
9.1%
ûȸ 882
 
9.1%
縮 882
 
9.1%
ҽ 882
 
9.1%
ĥ 878
 
9.1%

¥
Date

Distinct882
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
Minimum2009-06-01 00:00:00
Maximum2018-07-24 00:00:00
2023-12-13T00:23:50.526416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:50.685688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

췮 X 0.01 (mm)
Real number (ℝ)

ZEROS 

Distinct242
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean715.99814
Minimum-80100
Maximum80300
Zeros6135
Zeros (%)63.3%
Negative8
Negative (%)0.1%
Memory size85.4 KiB
2023-12-13T00:23:50.853124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-80100
5-th percentile0
Q10
median0
Q3400
95-th percentile4200
Maximum80300
Range160400
Interquartile range (IQR)400

Descriptive statistics

Standard deviation2256.5148
Coefficient of variation (CV)3.1515651
Kurtosis346.97218
Mean715.99814
Median Absolute Deviation (MAD)0
Skewness2.482312
Sum6943750
Variance5091858.9
MonotonicityNot monotonic
2023-12-13T00:23:51.018859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6135
63.3%
100 466
 
4.8%
200 243
 
2.5%
300 176
 
1.8%
400 135
 
1.4%
50 135
 
1.4%
500 114
 
1.2%
600 97
 
1.0%
700 89
 
0.9%
1000 88
 
0.9%
Other values (232) 2020
 
20.8%
ValueCountFrequency (%)
-80100 1
 
< 0.1%
-27700 1
 
< 0.1%
-11900 1
 
< 0.1%
-5500 1
 
< 0.1%
-1200 1
 
< 0.1%
-700 1
 
< 0.1%
-500 1
 
< 0.1%
-100 1
 
< 0.1%
0 6135
63.3%
50 135
 
1.4%
ValueCountFrequency (%)
80300 1
< 0.1%
27700 1
< 0.1%
23000 1
< 0.1%
20850 1
< 0.1%
19000 1
< 0.1%
18700 1
< 0.1%
18400 1
< 0.1%
18150 1
< 0.1%
18050 1
< 0.1%
17800 1
< 0.1%

ͱ
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
Minimum2020-02-27 00:00:00
Maximum2020-02-27 00:00:00
2023-12-13T00:23:51.166842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:51.282903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T00:23:49.841204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:23:51.356444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0췮 X 0.01 (mm)
Unnamed: 01.0000.000
췮 X 0.01 (mm)0.0001.000
2023-12-13T00:23:51.456624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
췮 X 0.01 (mm)Unnamed: 0
췮 X 0.01 (mm)1.0000.000
Unnamed: 00.0001.000

Missing values

2023-12-13T00:23:49.987926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:23:50.081641image/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

Unnamed: 0¥췮 X 0.01 (mm)ͱ
0<NA>2009-06-0102020-02-27
1<NA>2009-06-0202020-02-27
2<NA>2009-06-038002020-02-27
3<NA>2009-06-0402020-02-27
4<NA>2009-06-0502020-02-27
5<NA>2009-06-0602020-02-27
6<NA>2009-06-0702020-02-27
7<NA>2009-06-0802020-02-27
8<NA>2009-06-099002020-02-27
9<NA>2009-06-1011002020-02-27
Unnamed: 0¥췮 X 0.01 (mm)ͱ
9688<NA>2018-07-1502020-02-27
9689<NA>2018-07-1602020-02-27
9690<NA>2018-07-1702020-02-27
9691<NA>2018-07-1802020-02-27
9692<NA>2018-07-1902020-02-27
9693<NA>2018-07-2002020-02-27
9694<NA>2018-07-2102020-02-27
9695<NA>2018-07-2202020-02-27
9696<NA>2018-07-2302020-02-27
9697<NA>2018-07-2402020-02-27

Duplicate rows

Most frequently occurring

Unnamed: 0¥췮 X 0.01 (mm)ͱ# duplicates
0<NA>2009-06-0102020-02-274
3<NA>2009-06-0402020-02-274
6<NA>2009-06-0702020-02-274
7<NA>2009-06-0802020-02-274
10<NA>2009-06-1102020-02-274
11<NA>2009-06-1202020-02-274
12<NA>2009-06-1302020-02-274
15<NA>2009-06-1602020-02-274
16<NA>2009-06-1702020-02-274
17<NA>2009-06-1802020-02-274