Overview

Dataset statistics

Number of variables5
Number of observations3647
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory156.8 KiB
Average record size in memory44.0 B

Variable types

Numeric4
Text1

Dataset

Description가뭄 분석 정보 제공을 위한 가뭄 분석 행정구역에 대한 급수율, 급수인구 등 행정구역 제원정보 데이터 항목을 제공합니다.
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049842/fileData.do

Alerts

행정동코드 is highly overall correlated with 급수율 and 1 other fieldsHigh correlation
인구 is highly overall correlated with 급수율 and 1 other fieldsHigh correlation
급수율 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
급수인구 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
행정동코드 has unique valuesUnique
급수율 has 102 (2.8%) zerosZeros
급수인구 has 102 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-12 08:14:38.775575
Analysis finished2023-12-12 08:14:41.588882
Duration2.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3647
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7267903 × 109
Minimum1.1110515 × 109
Maximum5.013062 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2023-12-12T17:14:41.695525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110515 × 109
5-th percentile1.1380513 × 109
Q12.8237666 × 109
median4.180036 × 109
Q34.6170565 × 109
95-th percentile4.8310597 × 109
Maximum5.013062 × 109
Range3.9020105 × 109
Interquartile range (IQR)1.79329 × 109

Descriptive statistics

Standard deviation1.1846916 × 109
Coefficient of variation (CV)0.31788524
Kurtosis-0.03428111
Mean3.7267903 × 109
Median Absolute Deviation (MAD)5.39016 × 108
Skewness-1.0954115
Sum1.3591604 × 1013
Variance1.4034943 × 1018
MonotonicityStrictly increasing
2023-12-12T17:14:41.897740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111051500 1
 
< 0.1%
4511358000 1
 
< 0.1%
4511360000 1
 
< 0.1%
4511361100 1
 
< 0.1%
4511361200 1
 
< 0.1%
4511362000 1
 
< 0.1%
4511364100 1
 
< 0.1%
4511364200 1
 
< 0.1%
4511365000 1
 
< 0.1%
4511366000 1
 
< 0.1%
Other values (3637) 3637
99.7%
ValueCountFrequency (%)
1111051500 1
< 0.1%
1111053000 1
< 0.1%
1111054000 1
< 0.1%
1111055000 1
< 0.1%
1111056000 1
< 0.1%
1111057000 1
< 0.1%
1111058000 1
< 0.1%
1111060000 1
< 0.1%
1111061500 1
< 0.1%
1111063000 1
< 0.1%
ValueCountFrequency (%)
5013062000 1
< 0.1%
5013061000 1
< 0.1%
5013060000 1
< 0.1%
5013059000 1
< 0.1%
5013058000 1
< 0.1%
5013057000 1
< 0.1%
5013056000 1
< 0.1%
5013055000 1
< 0.1%
5013054000 1
< 0.1%
5013053000 1
< 0.1%

행정동명
Text

Distinct3641
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size28.6 KiB
2023-12-12T17:14:42.211116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length10.854127
Min length8

Characters and Unicode

Total characters39585
Distinct characters347
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3635 ?
Unique (%)99.7%

Sample

1st row서울특별시종로구청운효자동
2nd row서울특별시종로구사직동
3rd row서울특별시종로구삼청동
4th row서울특별시종로구부암동
5th row서울특별시종로구평창동
ValueCountFrequency (%)
서울특별시강북구수유3동 2
 
0.1%
서울특별시강북구번3동 2
 
0.1%
서울특별시강북구번2동 2
 
0.1%
서울특별시강북구번1동 2
 
0.1%
서울특별시강북구수유2동 2
 
0.1%
서울특별시강북구수유1동 2
 
0.1%
전라북도전주시덕진구송천1동 1
 
< 0.1%
전라북도전주시덕진구혁신동 1
 
< 0.1%
전라북도전주시덕진구여의동 1
 
< 0.1%
전라북도전주시덕진구동산동 1
 
< 0.1%
Other values (3631) 3631
99.6%
2023-12-12T17:14:42.692819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 2906
 
7.3%
동 2652
 
6.7%
도 2567
 
6.5%
구 1905
 
4.8%
경 1345
 
3.4%
남 1252
 
3.2%
면 1211
 
3.1%
광 1014
 
2.6%
북 964
 
2.4%
산 923
 
2.3%
Other values (337) 22846
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38385
97.0%
Decimal Number 1162
 
2.9%
Other Punctuation 38
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2906
 
7.6%
동 2652
 
6.9%
도 2567
 
6.7%
구 1905
 
5.0%
경 1345
 
3.5%
남 1252
 
3.3%
면 1211
 
3.2%
광 1014
 
2.6%
북 964
 
2.5%
산 923
 
2.4%
Other values (324) 21646
56.4%
Decimal Number
ValueCountFrequency (%)
1 409
35.2%
2 399
34.3%
3 182
15.7%
4 87
 
7.5%
5 37
 
3.2%
6 22
 
1.9%
7 12
 
1.0%
8 8
 
0.7%
9 4
 
0.3%
0 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
· 33
86.8%
. 4
 
10.5%
, 1
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38385
97.0%
Common 1200
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2906
 
7.6%
동 2652
 
6.9%
도 2567
 
6.7%
구 1905
 
5.0%
경 1345
 
3.5%
남 1252
 
3.3%
면 1211
 
3.2%
광 1014
 
2.6%
북 964
 
2.5%
산 923
 
2.4%
Other values (324) 21646
56.4%
Common
ValueCountFrequency (%)
1 409
34.1%
2 399
33.2%
3 182
15.2%
4 87
 
7.2%
5 37
 
3.1%
· 33
 
2.8%
6 22
 
1.8%
7 12
 
1.0%
8 8
 
0.7%
9 4
 
0.3%
Other values (3) 7
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38385
97.0%
ASCII 1167
 
2.9%
None 33
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 2906
 
7.6%
동 2652
 
6.9%
도 2567
 
6.7%
구 1905
 
5.0%
경 1345
 
3.5%
남 1252
 
3.3%
면 1211
 
3.2%
광 1014
 
2.6%
북 964
 
2.5%
산 923
 
2.4%
Other values (324) 21646
56.4%
ASCII
ValueCountFrequency (%)
1 409
35.0%
2 399
34.2%
3 182
15.6%
4 87
 
7.5%
5 37
 
3.2%
6 22
 
1.9%
7 12
 
1.0%
8 8
 
0.7%
9 4
 
0.3%
. 4
 
0.3%
Other values (2) 3
 
0.3%
None
ValueCountFrequency (%)
· 33
100.0%

인구
Real number (ℝ)

HIGH CORRELATION 

Distinct3416
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15575.052
Minimum0
Maximum315631
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2023-12-12T17:14:42.902107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1629.3
Q13803.5
median11459
Q322607.5
95-th percentile41582.2
Maximum315631
Range315631
Interquartile range (IQR)18804

Descriptive statistics

Standard deviation15650.217
Coefficient of variation (CV)1.004826
Kurtosis43.587618
Mean15575.052
Median Absolute Deviation (MAD)8457
Skewness3.7457554
Sum56802215
Variance2.4492928 × 108
MonotonicityNot monotonic
2023-12-12T17:14:43.076986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2356 3
 
0.1%
2510 3
 
0.1%
4156 3
 
0.1%
4641 3
 
0.1%
3592 3
 
0.1%
1493 3
 
0.1%
1771 3
 
0.1%
7935 3
 
0.1%
1427 3
 
0.1%
2329 3
 
0.1%
Other values (3406) 3617
99.2%
ValueCountFrequency (%)
0 2
0.1%
109 1
< 0.1%
160 1
< 0.1%
213 1
< 0.1%
262 1
< 0.1%
611 1
< 0.1%
648 1
< 0.1%
668 1
< 0.1%
707 1
< 0.1%
708 1
< 0.1%
ValueCountFrequency (%)
315631 1
< 0.1%
161721 1
< 0.1%
130604 1
< 0.1%
120968 1
< 0.1%
117773 1
< 0.1%
114166 1
< 0.1%
112758 1
< 0.1%
96562 1
< 0.1%
96381 1
< 0.1%
95610 1
< 0.1%

급수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.687414
Minimum0
Maximum100
Zeros102
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2023-12-12T17:14:43.287089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q197
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation22.251897
Coefficient of variation (CV)0.24536918
Kurtosis7.7974716
Mean90.687414
Median Absolute Deviation (MAD)0
Skewness-2.8947106
Sum330737
Variance495.14692
MonotonicityNot monotonic
2023-12-12T17:14:43.463414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2249
61.7%
99 267
 
7.3%
97 114
 
3.1%
98 112
 
3.1%
0 102
 
2.8%
96 41
 
1.1%
94 39
 
1.1%
95 37
 
1.0%
93 36
 
1.0%
90 34
 
0.9%
Other values (87) 616
 
16.9%
ValueCountFrequency (%)
0 102
2.8%
1 2
 
0.1%
2 2
 
0.1%
3 2
 
0.1%
4 2
 
0.1%
6 4
 
0.1%
7 1
 
< 0.1%
8 2
 
0.1%
9 1
 
< 0.1%
11 3
 
0.1%
ValueCountFrequency (%)
100 2249
61.7%
99 267
 
7.3%
98 112
 
3.1%
97 114
 
3.1%
96 41
 
1.1%
95 37
 
1.0%
94 39
 
1.1%
93 36
 
1.0%
92 33
 
0.9%
91 27
 
0.7%

급수인구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3343
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15263.045
Minimum0
Maximum315631
Zeros102
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2023-12-12T17:14:43.628470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile725.7
Q13307
median11403
Q322540.5
95-th percentile41460.4
Maximum315631
Range315631
Interquartile range (IQR)19233.5

Descriptive statistics

Standard deviation15747.875
Coefficient of variation (CV)1.031765
Kurtosis42.367331
Mean15263.045
Median Absolute Deviation (MAD)8850
Skewness3.6451376
Sum55664324
Variance2.4799556 × 108
MonotonicityNot monotonic
2023-12-12T17:14:43.813635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102
 
2.8%
1279 4
 
0.1%
1948 3
 
0.1%
2473 3
 
0.1%
1493 3
 
0.1%
1019 3
 
0.1%
5184 3
 
0.1%
2286 3
 
0.1%
2383 3
 
0.1%
2032 3
 
0.1%
Other values (3333) 3517
96.4%
ValueCountFrequency (%)
0 102
2.8%
22 1
 
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
26 1
 
< 0.1%
65 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
108 1
 
< 0.1%
ValueCountFrequency (%)
315631 1
< 0.1%
159458 1
< 0.1%
130604 1
< 0.1%
120968 1
< 0.1%
114898 1
< 0.1%
111067 1
< 0.1%
110665 1
< 0.1%
95610 1
< 0.1%
95114 1
< 0.1%
94619 1
< 0.1%

Interactions

2023-12-12T17:14:40.727101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:39.279344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:39.798316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:40.264104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:40.877765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:39.430052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:39.918411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:40.392049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:41.054594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:39.548777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:40.061382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:40.495396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:41.203632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:39.677139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:40.161680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:14:40.603029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:14:43.938134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드인구급수율급수인구
행정동코드1.0000.1830.3080.182
인구0.1831.0000.1031.000
급수율0.3080.1031.0000.102
급수인구0.1821.0000.1021.000
2023-12-12T17:14:44.036598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드인구급수율급수인구
행정동코드1.000-0.496-0.519-0.501
인구-0.4961.0000.5440.993
급수율-0.5190.5441.0000.592
급수인구-0.5010.9930.5921.000

Missing values

2023-12-12T17:14:41.395963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:14:41.535696image/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

행정동코드행정동명인구급수율급수인구
01111051500서울특별시종로구청운효자동1263310012633
11111053000서울특별시종로구사직동98061009806
21111054000서울특별시종로구삼청동29091002909
31111055000서울특별시종로구부암동1033310010333
41111056000서울특별시종로구평창동1858210018582
51111057000서울특별시종로구무악동85921008592
61111058000서울특별시종로구교남동1063410010634
71111060000서울특별시종로구가회동44181004418
81111061500서울특별시종로구종로1·2·3·4가동84481008448
91111063000서울특별시종로구종로5·6가동56561005656
행정동코드행정동명인구급수율급수인구
36375013053000제주특별자치도서귀포시중앙동35921003592
36385013054000제주특별자치도서귀포시천지동36361003636
36395013055000제주특별자치도서귀포시효돈동53241005324
36405013056000제주특별자치도서귀포시영천동53711005371
36415013057000제주특별자치도서귀포시동홍동2422210024222
36425013058000제주특별자치도서귀포시서홍동1074610010746
36435013059000제주특별자치도서귀포시대륜동1496310014963
36445013060000제주특별자치도서귀포시대천동1411010014110
36455013061000제주특별자치도서귀포시중문동1187710011877
36465013062000제주특별자치도서귀포시예래동42001004200