Overview

Dataset statistics

Number of variables7
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory64.3 B

Variable types

Text2
Numeric4
Categorical1

Dataset

Description경상북도 구미시에서 설치된 디지털계량기 현황을 행정동별로 구분하여 유니온계량기 설치 대수 , 디지털 계량기 설치 대수, 디지털계량기 설치 비율, 수용가수의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15102958/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
총합계 is highly overall correlated with 유니온 and 2 other fieldsHigh correlation
유니온 is highly overall correlated with 총합계 and 1 other fieldsHigh correlation
디지털 is highly overall correlated with 총합계 and 1 other fieldsHigh correlation
수용가수 is highly overall correlated with 총합계 and 2 other fieldsHigh correlation
행정동구분 has unique valuesUnique
유니온 has unique valuesUnique
수용가수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:52:09.554520
Analysis finished2023-12-12 03:52:12.825780
Duration3.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동구분
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-12T12:52:13.026016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2903226
Min length3

Characters and Unicode

Total characters102
Distinct characters45
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row고아읍
2nd row공단1동
3rd row공단2동
4th row광평동
5th row구평동
ValueCountFrequency (%)
고아읍 1
 
3.2%
신평2동 1
 
3.2%
형곡1동 1
 
3.2%
해평면 1
 
3.2%
진미동 1
 
3.2%
지산동 1
 
3.2%
장천면 1
 
3.2%
임오동 1
 
3.2%
인동동 1
 
3.2%
원평3동 1
 
3.2%
Other values (21) 21
67.7%
2023-12-12T12:52:13.478391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 25
24.5%
평 8
 
7.8%
면 5
 
4.9%
원 4
 
3.9%
2 4
 
3.9%
산 4
 
3.9%
1 4
 
3.9%
곡 3
 
2.9%
읍 3
 
2.9%
단 2
 
2.0%
Other values (35) 40
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
91.2%
Decimal Number 9
 
8.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
26.9%
평 8
 
8.6%
면 5
 
5.4%
원 4
 
4.3%
산 4
 
4.3%
곡 3
 
3.2%
읍 3
 
3.2%
단 2
 
2.2%
신 2
 
2.2%
도 2
 
2.2%
Other values (32) 35
37.6%
Decimal Number
ValueCountFrequency (%)
2 4
44.4%
1 4
44.4%
3 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
91.2%
Common 9
 
8.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
26.9%
평 8
 
8.6%
면 5
 
5.4%
원 4
 
4.3%
산 4
 
4.3%
곡 3
 
3.2%
읍 3
 
3.2%
단 2
 
2.2%
신 2
 
2.2%
도 2
 
2.2%
Other values (32) 35
37.6%
Common
ValueCountFrequency (%)
2 4
44.4%
1 4
44.4%
3 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
91.2%
ASCII 9
 
8.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 25
26.9%
평 8
 
8.6%
면 5
 
5.4%
원 4
 
4.3%
산 4
 
4.3%
곡 3
 
3.2%
읍 3
 
3.2%
단 2
 
2.2%
신 2
 
2.2%
도 2
 
2.2%
Other values (32) 35
37.6%
ASCII
ValueCountFrequency (%)
2 4
44.4%
1 4
44.4%
3 1
 
11.1%

총합계
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1442.871
Minimum243
Maximum4249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T12:52:13.692123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum243
5-th percentile477.5
Q1651
median1218
Q31873
95-th percentile3434
Maximum4249
Range4006
Interquartile range (IQR)1222

Descriptive statistics

Standard deviation970.75231
Coefficient of variation (CV)0.67279219
Kurtosis1.8014819
Mean1442.871
Median Absolute Deviation (MAD)576
Skewness1.3424677
Sum44729
Variance942360.05
MonotonicityNot monotonic
2023-12-12T12:52:13.886173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1606 2
 
6.5%
3860 1
 
3.2%
477 1
 
3.2%
2213 1
 
3.2%
1174 1
 
3.2%
2087 1
 
3.2%
2306 1
 
3.2%
874 1
 
3.2%
1213 1
 
3.2%
3008 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
243 1
3.2%
477 1
3.2%
478 1
3.2%
492 1
3.2%
514 1
3.2%
547 1
3.2%
553 1
3.2%
642 1
3.2%
660 1
3.2%
874 1
3.2%
ValueCountFrequency (%)
4249 1
3.2%
3860 1
3.2%
3008 1
3.2%
2396 1
3.2%
2306 1
3.2%
2213 1
3.2%
2087 1
3.2%
2021 1
3.2%
1725 1
3.2%
1606 2
6.5%

유니온
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean885.22581
Minimum42
Maximum2772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T12:52:14.040338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile63
Q1348.5
median787
Q31267
95-th percentile2380
Maximum2772
Range2730
Interquartile range (IQR)918.5

Descriptive statistics

Standard deviation750.00412
Coefficient of variation (CV)0.84724611
Kurtosis0.52971882
Mean885.22581
Median Absolute Deviation (MAD)497
Skewness1.027259
Sum27442
Variance562506.18
MonotonicityNot monotonic
2023-12-12T12:52:14.187831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2772 1
 
3.2%
123 1
 
3.2%
1789 1
 
3.2%
939 1
 
3.2%
181 1
 
3.2%
1760 1
 
3.2%
787 1
 
3.2%
82 1
 
3.2%
979 1
 
3.2%
2696 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
42 1
3.2%
54 1
3.2%
72 1
3.2%
82 1
3.2%
123 1
3.2%
181 1
3.2%
220 1
3.2%
290 1
3.2%
407 1
3.2%
414 1
3.2%
ValueCountFrequency (%)
2772 1
3.2%
2696 1
3.2%
2064 1
3.2%
1789 1
3.2%
1760 1
3.2%
1499 1
3.2%
1479 1
3.2%
1381 1
3.2%
1153 1
3.2%
1091 1
3.2%

디지털
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean557.64516
Minimum49
Maximum4029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T12:52:14.374328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile60
Q1129.5
median246
Q3534
95-th percentile1715
Maximum4029
Range3980
Interquartile range (IQR)404.5

Descriptive statistics

Standard deviation796.8822
Coefficient of variation (CV)1.429013
Kurtosis11.855319
Mean557.64516
Median Absolute Deviation (MAD)177
Skewness3.1332493
Sum17287
Variance635021.24
MonotonicityNot monotonic
2023-12-12T12:52:14.594329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
69 2
 
6.5%
1088 1
 
3.2%
246 1
 
3.2%
424 1
 
3.2%
235 1
 
3.2%
1906 1
 
3.2%
546 1
 
3.2%
87 1
 
3.2%
1524 1
 
3.2%
234 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
49 1
3.2%
56 1
3.2%
64 1
3.2%
69 2
6.5%
87 1
3.2%
107 1
3.2%
120 1
3.2%
139 1
3.2%
161 1
3.2%
162 1
3.2%
ValueCountFrequency (%)
4029 1
3.2%
1906 1
3.2%
1524 1
3.2%
1164 1
3.2%
1148 1
3.2%
1088 1
3.2%
931 1
3.2%
546 1
3.2%
522 1
3.2%
428 1
3.2%

디지털비율
Text

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-12T12:52:14.868927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9677419
Min length2

Characters and Unicode

Total characters92
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)32.3%

Sample

1st row28%
2nd row49%
3rd row56%
4th row13%
5th row9%
ValueCountFrequency (%)
14 4
12.9%
13 3
 
9.7%
28 2
 
6.5%
19 2
 
6.5%
96 2
 
6.5%
15 2
 
6.5%
10 2
 
6.5%
95 2
 
6.5%
25 2
 
6.5%
12 1
 
3.2%
Other values (9) 9
29.0%
2023-12-12T12:52:15.313754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 31
33.7%
1 16
17.4%
9 10
 
10.9%
2 9
 
9.8%
4 7
 
7.6%
5 7
 
7.6%
6 4
 
4.3%
3 3
 
3.3%
0 3
 
3.3%
8 2
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61
66.3%
Other Punctuation 31
33.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
26.2%
9 10
16.4%
2 9
14.8%
4 7
11.5%
5 7
11.5%
6 4
 
6.6%
3 3
 
4.9%
0 3
 
4.9%
8 2
 
3.3%
Other Punctuation
ValueCountFrequency (%)
% 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 92
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 31
33.7%
1 16
17.4%
9 10
 
10.9%
2 9
 
9.8%
4 7
 
7.6%
5 7
 
7.6%
6 4
 
4.3%
3 3
 
3.3%
0 3
 
3.3%
8 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 31
33.7%
1 16
17.4%
9 10
 
10.9%
2 9
 
9.8%
4 7
 
7.6%
5 7
 
7.6%
6 4
 
4.3%
3 3
 
3.3%
0 3
 
3.3%
8 2
 
2.2%

수용가수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1451.6129
Minimum245
Maximum4252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T12:52:15.514328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum245
5-th percentile481.5
Q1649
median1218
Q31883.5
95-th percentile3437
Maximum4252
Range4007
Interquartile range (IQR)1234.5

Descriptive statistics

Standard deviation973.69953
Coefficient of variation (CV)0.67077079
Kurtosis1.7148422
Mean1451.6129
Median Absolute Deviation (MAD)572
Skewness1.3202514
Sum45000
Variance948090.78
MonotonicityNot monotonic
2023-12-12T12:52:15.715849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3864 1
 
3.2%
245 1
 
3.2%
2217 1
 
3.2%
1180 1
 
3.2%
2158 1
 
3.2%
2316 1
 
3.2%
884 1
 
3.2%
1620 1
 
3.2%
1215 1
 
3.2%
3010 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
245 1
3.2%
477 1
3.2%
486 1
3.2%
495 1
3.2%
516 1
3.2%
555 1
3.2%
563 1
3.2%
646 1
3.2%
652 1
3.2%
884 1
3.2%
ValueCountFrequency (%)
4252 1
3.2%
3864 1
3.2%
3010 1
3.2%
2427 1
3.2%
2316 1
3.2%
2217 1
3.2%
2158 1
3.2%
2036 1
3.2%
1731 1
3.2%
1620 1
3.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-08-16
31 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-16
2nd row2023-08-16
3rd row2023-08-16
4th row2023-08-16
5th row2023-08-16

Common Values

ValueCountFrequency (%)
2023-08-16 31
100.0%

Length

2023-12-12T12:52:15.938328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:52:16.094428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-16 31
100.0%

Interactions

2023-12-12T12:52:11.929868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:09.849018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:10.476371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:11.038136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:12.050483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:09.996833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:10.635722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:11.181435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:12.181170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:10.136803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:10.761441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:11.303066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:12.346309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:10.298922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:10.909525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:52:11.797897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:52:16.201532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동구분총합계유니온디지털디지털비율수용가수
행정동구분1.0001.0001.0001.0001.0001.000
총합계1.0001.0000.7990.5570.4251.000
유니온1.0000.7991.0000.0000.0000.766
디지털1.0000.5570.0001.0000.8880.546
디지털비율1.0000.4250.0000.8881.0000.419
수용가수1.0001.0000.7660.5460.4191.000
2023-12-12T12:52:16.367872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총합계유니온디지털수용가수
총합계1.0000.5360.7501.000
유니온0.5361.000-0.0510.532
디지털0.750-0.0511.0000.753
수용가수1.0000.5320.7531.000

Missing values

2023-12-12T12:52:12.557986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:52:12.751750image/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

행정동구분총합계유니온디지털디지털비율수용가수데이터기준일자
0고아읍38602772108828%38642023-08-16
1공단1동24312312049%2452023-08-16
2공단2동66029037056%6522023-08-16
3광평동5474786913%5552023-08-16
4구평동553504499%5632023-08-16
5도개면122072114894%12222023-08-16
6도량동1606138122514%16182023-08-16
7무을면121854116496%12182023-08-16
8비산동4924236914%4952023-08-16
9사곡동1352115319915%13742023-08-16
행정동구분총합계유니온디지털디지털비율수용가수데이터기준일자
21원평2동51440710721%5162023-08-16
22원평3동4784146413%4862023-08-16
23인동동3008269631210%30102023-08-16
24임오동121397923419%12152023-08-16
25장천면160682152495%16202023-08-16
26지산동8747878710%8842023-08-16
27진미동2306176054624%23162023-08-16
28해평면2087181190691%21582023-08-16
29형곡1동117493923520%11802023-08-16
30형곡2동2213178942419%22172023-08-16