Overview

Dataset statistics

Number of variables5
Number of observations138
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory44.0 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description경상북도 구미시 유수율제고블록시스템DB의 급수전수변화테이블 데이터로 급수월,블록명,급수량 데이터를 제공하고 있습니다.
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15049704/fileData.do

Alerts

당월급수량 is highly overall correlated with 전월급수량High correlation
전월급수량 is highly overall correlated with 당월급수량High correlation
월 급수전 변화량 12개월 평균 has 56 (40.6%) zerosZeros

Reproduction

Analysis started2023-12-12 17:15:22.516384
Analysis finished2023-12-12 17:15:23.689844
Duration1.17 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

급수월
Categorical

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2020-08-01
73 
2021-06-28
65 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-08-01
2nd row2020-08-01
3rd row2020-08-01
4th row2020-08-01
5th row2020-08-01

Common Values

ValueCountFrequency (%)
2020-08-01 73
52.9%
2021-06-28 65
47.1%

Length

2023-12-13T02:15:23.760530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:15:23.877175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-08-01 73
52.9%
2021-06-28 65
47.1%

블록명
Text

Distinct73
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T02:15:24.082406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0434783
Min length3

Characters and Unicode

Total characters834
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)7.2%

Sample

1st row3P-7-1
2nd row432
3rd row4F-1-1
4th row4F-1-2
5th row4F-1-3
ValueCountFrequency (%)
ss-1-4 4
 
2.9%
wh-1-3 3
 
2.2%
4f-1-3 2
 
1.4%
sp-7-2 2
 
1.4%
sp-5-1 2
 
1.4%
sp-4-1 2
 
1.4%
sp-4-2 2
 
1.4%
wh-4-4 2
 
1.4%
sp-5-2 2
 
1.4%
sp-5-3 2
 
1.4%
Other values (62) 115
83.3%
2023-12-13T02:15:24.491116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 275
33.0%
1 113
13.5%
S 100
 
12.0%
P 66
 
7.9%
2 53
 
6.4%
4 48
 
5.8%
H 36
 
4.3%
W 31
 
3.7%
3 29
 
3.5%
5 20
 
2.4%
Other values (10) 63
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 292
35.0%
Dash Punctuation 275
33.0%
Uppercase Letter 266
31.9%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 113
38.7%
2 53
18.2%
4 48
16.4%
3 29
 
9.9%
5 20
 
6.8%
6 16
 
5.5%
7 7
 
2.4%
0 2
 
0.7%
8 2
 
0.7%
9 2
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
S 100
37.6%
P 66
24.8%
H 36
 
13.5%
W 31
 
11.7%
F 10
 
3.8%
I 10
 
3.8%
D 10
 
3.8%
A 3
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 275
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 568
68.1%
Latin 266
31.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 275
48.4%
1 113
19.9%
2 53
 
9.3%
4 48
 
8.5%
3 29
 
5.1%
5 20
 
3.5%
6 16
 
2.8%
7 7
 
1.2%
0 2
 
0.4%
8 2
 
0.4%
Other values (2) 3
 
0.5%
Latin
ValueCountFrequency (%)
S 100
37.6%
P 66
24.8%
H 36
 
13.5%
W 31
 
11.7%
F 10
 
3.8%
I 10
 
3.8%
D 10
 
3.8%
A 3
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 275
33.0%
1 113
13.5%
S 100
 
12.0%
P 66
 
7.9%
2 53
 
6.4%
4 48
 
5.8%
H 36
 
4.3%
W 31
 
3.7%
3 29
 
3.5%
5 20
 
2.4%
Other values (10) 63
 
7.6%

당월급수량
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.8913
Minimum1
Maximum2784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T02:15:24.637649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1149.75
median392
Q3745.5
95-th percentile1346.7
Maximum2784
Range2783
Interquartile range (IQR)595.75

Descriptive statistics

Standard deviation479.37728
Coefficient of variation (CV)0.95514164
Kurtosis3.401788
Mean501.8913
Median Absolute Deviation (MAD)279.5
Skewness1.5331044
Sum69261
Variance229802.58
MonotonicityNot monotonic
2023-12-13T02:15:24.788596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
5.8%
4 3
 
2.2%
198 3
 
2.2%
199 3
 
2.2%
371 2
 
1.4%
560 2
 
1.4%
14 2
 
1.4%
9 2
 
1.4%
477 2
 
1.4%
67 1
 
0.7%
Other values (110) 110
79.7%
ValueCountFrequency (%)
1 8
5.8%
3 1
 
0.7%
4 3
 
2.2%
6 1
 
0.7%
9 2
 
1.4%
11 1
 
0.7%
14 2
 
1.4%
28 1
 
0.7%
29 1
 
0.7%
59 1
 
0.7%
ValueCountFrequency (%)
2784 1
0.7%
1946 1
0.7%
1915 1
0.7%
1611 1
0.7%
1502 1
0.7%
1448 1
0.7%
1379 1
0.7%
1341 1
0.7%
1319 1
0.7%
1311 1
0.7%

전월급수량
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.22464
Minimum1
Maximum2782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T02:15:24.977567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1148.75
median390.5
Q3745
95-th percentile1342.45
Maximum2782
Range2781
Interquartile range (IQR)596.25

Descriptive statistics

Standard deviation478.67372
Coefficient of variation (CV)0.95500835
Kurtosis3.4083739
Mean501.22464
Median Absolute Deviation (MAD)279.5
Skewness1.5333112
Sum69169
Variance229128.53
MonotonicityNot monotonic
2023-12-13T02:15:25.122978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
5.8%
4 3
 
2.2%
199 3
 
2.2%
14 2
 
1.4%
325 2
 
1.4%
9 2
 
1.4%
198 2
 
1.4%
560 2
 
1.4%
476 2
 
1.4%
197 1
 
0.7%
Other values (111) 111
80.4%
ValueCountFrequency (%)
1 8
5.8%
3 1
 
0.7%
4 3
 
2.2%
6 1
 
0.7%
9 2
 
1.4%
11 1
 
0.7%
14 2
 
1.4%
28 1
 
0.7%
29 1
 
0.7%
59 1
 
0.7%
ValueCountFrequency (%)
2782 1
0.7%
1937 1
0.7%
1914 1
0.7%
1601 1
0.7%
1512 1
0.7%
1447 1
0.7%
1379 1
0.7%
1336 1
0.7%
1317 1
0.7%
1311 1
0.7%

월 급수전 변화량 12개월 평균
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36043478
Minimum-65.2
Maximum65.5
Zeros56
Zeros (%)40.6%
Negative20
Negative (%)14.5%
Memory size1.3 KiB
2023-12-13T02:15:25.266816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-65.2
5-th percentile-0.315
Q10
median0
Q30.4
95-th percentile2.53
Maximum65.5
Range130.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation7.9953963
Coefficient of variation (CV)22.182644
Kurtosis65.018453
Mean0.36043478
Median Absolute Deviation (MAD)0.1
Skewness-0.070076232
Sum49.74
Variance63.926362
MonotonicityNot monotonic
2023-12-13T02:15:25.433197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 56
40.6%
0.2 11
 
8.0%
0.1 9
 
6.5%
-0.1 9
 
6.5%
0.4 5
 
3.6%
0.3 4
 
2.9%
0.5 4
 
2.9%
0.6 3
 
2.2%
0.7 3
 
2.2%
-0.2 3
 
2.2%
Other values (27) 31
22.5%
ValueCountFrequency (%)
-65.2 1
 
0.7%
-5.5 1
 
0.7%
-2.7 1
 
0.7%
-1.5 1
 
0.7%
-0.7 1
 
0.7%
-0.6 1
 
0.7%
-0.4 1
 
0.7%
-0.3 1
 
0.7%
-0.2 3
 
2.2%
-0.1 9
6.5%
ValueCountFrequency (%)
65.5 1
0.7%
7.6 1
0.7%
6.7 1
0.7%
4.1 1
0.7%
4.0 1
0.7%
2.8 1
0.7%
2.7 1
0.7%
2.5 1
0.7%
2.4 1
0.7%
2.3 1
0.7%

Interactions

2023-12-13T02:15:23.281473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:15:22.704813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:15:23.031781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:15:23.368390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:15:22.795740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:15:23.116176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:15:23.450212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:15:22.905428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:15:23.192909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:15:25.539823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수월블록명당월급수량전월급수량월 급수전 변화량 12개월 평균
급수월1.0000.0000.0670.0670.256
블록명0.0001.0000.5590.5590.430
당월급수량0.0670.5591.0001.0000.420
전월급수량0.0670.5591.0001.0000.420
월 급수전 변화량 12개월 평균0.2560.4300.4200.4201.000
2023-12-13T02:15:25.640203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당월급수량전월급수량월 급수전 변화량 12개월 평균급수월
당월급수량1.0001.0000.3120.045
전월급수량1.0001.0000.3120.045
월 급수전 변화량 12개월 평균0.3120.3121.0000.169
급수월0.0450.0450.1691.000

Missing values

2023-12-13T02:15:23.551353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:15:23.651277image/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

급수월블록명당월급수량전월급수량월 급수전 변화량 12개월 평균
02020-08-013P-7-1110.2
12020-08-01432110.14
22020-08-014F-1-14554540.0
32020-08-014F-1-2116411630.4
42020-08-014F-1-3670669-0.1
52020-08-014F-1-4103010261.2
62020-08-014F-1-5534533-65.2
72020-08-01HP-1-113411336-0.1
82020-08-01HP-1-2394394-0.7
92020-08-01HP-1-314140.4
급수월블록명당월급수량전월급수량월 급수전 변화량 12개월 평균
1282021-06-28SP-3-159590.0
1292021-06-28SP-4-11981970.5
1302021-06-28SP-4-214140.0
1312021-06-28SP-5-15475470.0
1322021-06-28SP-5-2121012090.1
1332021-06-28SP-5-35605600.0
1342021-06-28SP-6-13713622.5
1352021-06-28SP-6-21931930.0
1362021-06-28SP-6-38078040.4
1372021-06-28SP-6-4144814470.1