Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells55
Missing cells (%)14.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory112.4 B

Variable types

Text3
Numeric3
Unsupported1
DateTime2
Categorical4

Dataset

Description지정약수터 정보
Author충청북도 청주시
URLhttps://www.data.go.kr/data/15061815/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
수질검사결과구분 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
관리기관명 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
부적합항목 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
관리기관전화번호 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 수질검사결과구분 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 부적합항목 and 2 other fieldsHigh correlation
일평균이용인구수 is highly overall correlated with 부적합항목High correlation
수질검사결과구분 is highly imbalanced (53.1%)Imbalance
부적합항목 is highly imbalanced (53.1%)Imbalance
소재지도로명주소 has 25 (83.3%) missing valuesMissing
지정일자 has 30 (100.0%) missing valuesMissing
약수터명 has unique valuesUnique
지정일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 19:37:24.498591
Analysis finished2023-12-12 19:37:26.032384
Duration1.53 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

약수터명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T04:37:26.199630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.7333333
Min length2

Characters and Unicode

Total characters172
Distinct characters72
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row옻샘
2nd row고씨우물
3rd row광덕사
4th row우암산등산로2
5th row어린이회관등산로
ValueCountFrequency (%)
옻샘 1
 
3.3%
고씨우물 1
 
3.3%
원산면옥(초정약수터 1
 
3.3%
초정약수원탕매점 1
 
3.3%
묘안옹달샘 1
 
3.3%
잠방골옹달샘 1
 
3.3%
양지등산로공동우물 1
 
3.3%
옥화유원지공동우물3 1
 
3.3%
옥화유원지공동우물1 1
 
3.3%
청석굴공동우물 1
 
3.3%
Other values (20) 20
66.7%
2023-12-13T04:37:26.634966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 17
 
9.9%
로 8
 
4.7%
등 8
 
4.7%
원 7
 
4.1%
우 7
 
4.1%
공 7
 
4.1%
정 5
 
2.9%
물 5
 
2.9%
사 4
 
2.3%
성 4
 
2.3%
Other values (62) 100
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 163
94.8%
Decimal Number 7
 
4.1%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 17
 
10.4%
로 8
 
4.9%
등 8
 
4.9%
원 7
 
4.3%
우 7
 
4.3%
공 7
 
4.3%
정 5
 
3.1%
물 5
 
3.1%
사 4
 
2.5%
성 4
 
2.5%
Other values (57) 91
55.8%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
3 2
28.6%
2 2
28.6%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 163
94.8%
Common 9
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 17
 
10.4%
로 8
 
4.9%
등 8
 
4.9%
원 7
 
4.3%
우 7
 
4.3%
공 7
 
4.3%
정 5
 
3.1%
물 5
 
3.1%
사 4
 
2.5%
성 4
 
2.5%
Other values (57) 91
55.8%
Common
ValueCountFrequency (%)
1 3
33.3%
3 2
22.2%
2 2
22.2%
( 1
 
11.1%
) 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 163
94.8%
ASCII 9
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 17
 
10.4%
로 8
 
4.9%
등 8
 
4.9%
원 7
 
4.3%
우 7
 
4.3%
공 7
 
4.3%
정 5
 
3.1%
물 5
 
3.1%
사 4
 
2.5%
성 4
 
2.5%
Other values (57) 91
55.8%
ASCII
ValueCountFrequency (%)
1 3
33.3%
3 2
22.2%
2 2
22.2%
( 1
 
11.1%
) 1
 
11.1%

소재지도로명주소
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing25
Missing (%)83.3%
Memory size372.0 B
2023-12-13T04:37:26.860387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length25.6
Min length23

Characters and Unicode

Total characters128
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row충청북도 청주시 상당구 당산로89번길 155(용담동)
2nd row충청북도 청주시 상당구 교동로71번길 91
3rd row충청북도 청주시 상당구 명암로 103(명암동)
4th row충청북도 청주시 흥덕구 복대로118번길 16
5th row충청북도 청주시 청원구 내수읍 미원초정로 1357
ValueCountFrequency (%)
충청북도 5
19.2%
청주시 5
19.2%
상당구 3
11.5%
당산로89번길 1
 
3.8%
155(용담동 1
 
3.8%
교동로71번길 1
 
3.8%
91 1
 
3.8%
명암로 1
 
3.8%
103(명암동 1
 
3.8%
흥덕구 1
 
3.8%
Other values (6) 6
23.1%
2023-12-13T04:37:27.218012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
16.4%
청 11
 
8.6%
1 8
 
6.2%
시 5
 
3.9%
로 5
 
3.9%
구 5
 
3.9%
충 5
 
3.9%
주 5
 
3.9%
도 5
 
3.9%
북 5
 
3.9%
Other values (31) 53
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
64.1%
Space Separator 21
 
16.4%
Decimal Number 21
 
16.4%
Open Punctuation 2
 
1.6%
Close Punctuation 2
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 11
 
13.4%
시 5
 
6.1%
로 5
 
6.1%
구 5
 
6.1%
충 5
 
6.1%
주 5
 
6.1%
도 5
 
6.1%
북 5
 
6.1%
당 4
 
4.9%
상 3
 
3.7%
Other values (20) 29
35.4%
Decimal Number
ValueCountFrequency (%)
1 8
38.1%
5 3
 
14.3%
9 2
 
9.5%
8 2
 
9.5%
3 2
 
9.5%
7 2
 
9.5%
6 1
 
4.8%
0 1
 
4.8%
Space Separator
ValueCountFrequency (%)
21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82
64.1%
Common 46
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 11
 
13.4%
시 5
 
6.1%
로 5
 
6.1%
구 5
 
6.1%
충 5
 
6.1%
주 5
 
6.1%
도 5
 
6.1%
북 5
 
6.1%
당 4
 
4.9%
상 3
 
3.7%
Other values (20) 29
35.4%
Common
ValueCountFrequency (%)
21
45.7%
1 8
 
17.4%
5 3
 
6.5%
( 2
 
4.3%
9 2
 
4.3%
8 2
 
4.3%
) 2
 
4.3%
3 2
 
4.3%
7 2
 
4.3%
6 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82
64.1%
ASCII 46
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
45.7%
1 8
 
17.4%
5 3
 
6.5%
( 2
 
4.3%
9 2
 
4.3%
8 2
 
4.3%
) 2
 
4.3%
3 2
 
4.3%
7 2
 
4.3%
6 1
 
2.2%
Hangul
ValueCountFrequency (%)
청 11
 
13.4%
시 5
 
6.1%
로 5
 
6.1%
구 5
 
6.1%
충 5
 
6.1%
주 5
 
6.1%
도 5
 
6.1%
북 5
 
6.1%
당 4
 
4.9%
상 3
 
3.7%
Other values (20) 29
35.4%

소재지지번주소
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T04:37:27.448049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length22.4
Min length20

Characters and Unicode

Total characters672
Distinct characters61
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row충청북도 청주시 청원구 우암동 산 7-4
2nd row충청북도 청주시 상당구 용담동 산49
3rd row충청북도 청주시 상당구 용담동 120-1
4th row충청북도 청주시 상당구 수동 산2-1
5th row충청북도 청주시 상당구 명암동 산63
ValueCountFrequency (%)
충청북도 30
18.8%
청주시 30
18.8%
상당구 14
 
8.8%
청원구 6
 
3.8%
서원구 5
 
3.1%
흥덕구 5
 
3.1%
미원면 4
 
2.5%
내수읍 3
 
1.9%
명암동 3
 
1.9%
초정리 3
 
1.9%
Other values (50) 57
35.6%
2023-12-13T04:37:27.848938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
130
19.3%
청 66
 
9.8%
충 32
 
4.8%
도 31
 
4.6%
북 30
 
4.5%
주 30
 
4.5%
시 30
 
4.5%
구 30
 
4.5%
1 27
 
4.0%
동 21
 
3.1%
Other values (51) 245
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 429
63.8%
Space Separator 130
 
19.3%
Decimal Number 94
 
14.0%
Dash Punctuation 19
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 66
15.4%
충 32
 
7.5%
도 31
 
7.2%
북 30
 
7.0%
주 30
 
7.0%
시 30
 
7.0%
구 30
 
7.0%
동 21
 
4.9%
산 18
 
4.2%
원 16
 
3.7%
Other values (39) 125
29.1%
Decimal Number
ValueCountFrequency (%)
1 27
28.7%
2 17
18.1%
4 12
12.8%
0 8
 
8.5%
5 7
 
7.4%
6 7
 
7.4%
3 6
 
6.4%
8 4
 
4.3%
7 3
 
3.2%
9 3
 
3.2%
Space Separator
ValueCountFrequency (%)
130
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 429
63.8%
Common 243
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 66
15.4%
충 32
 
7.5%
도 31
 
7.2%
북 30
 
7.0%
주 30
 
7.0%
시 30
 
7.0%
구 30
 
7.0%
동 21
 
4.9%
산 18
 
4.2%
원 16
 
3.7%
Other values (39) 125
29.1%
Common
ValueCountFrequency (%)
130
53.5%
1 27
 
11.1%
- 19
 
7.8%
2 17
 
7.0%
4 12
 
4.9%
0 8
 
3.3%
5 7
 
2.9%
6 7
 
2.9%
3 6
 
2.5%
8 4
 
1.6%
Other values (2) 6
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 429
63.8%
ASCII 243
36.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
53.5%
1 27
 
11.1%
- 19
 
7.8%
2 17
 
7.0%
4 12
 
4.9%
0 8
 
3.3%
5 7
 
2.9%
6 7
 
2.9%
3 6
 
2.5%
8 4
 
1.6%
Other values (2) 6
 
2.5%
Hangul
ValueCountFrequency (%)
청 66
15.4%
충 32
 
7.5%
도 31
 
7.2%
북 30
 
7.0%
주 30
 
7.0%
시 30
 
7.0%
구 30
 
7.0%
동 21
 
4.9%
산 18
 
4.2%
원 16
 
3.7%
Other values (39) 125
29.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.643186
Minimum36.479905
Maximum36.721797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T04:37:27.987332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.479905
5-th percentile36.615
Q136.624945
median36.642227
Q336.661546
95-th percentile36.720659
Maximum36.721797
Range0.241892
Interquartile range (IQR)0.0366015

Descriptive statistics

Standard deviation0.042394917
Coefficient of variation (CV)0.0011569659
Kurtosis7.4898718
Mean36.643186
Median Absolute Deviation (MAD)0.018186105
Skewness-1.4476901
Sum1099.2956
Variance0.001797329
MonotonicityNot monotonic
2023-12-13T04:37:28.135031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
36.64199721 2
 
6.7%
36.720659067 2
 
6.7%
36.647711 1
 
3.3%
36.624256 1
 
3.3%
36.721797 1
 
3.3%
36.661697 1
 
3.3%
36.639149 1
 
3.3%
36.479905 1
 
3.3%
36.617511 1
 
3.3%
36.612945 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
36.479905 1
3.3%
36.612945 1
3.3%
36.617511 1
3.3%
36.619772 1
3.3%
36.619942 1
3.3%
36.623861 1
3.3%
36.624221 1
3.3%
36.624256 1
3.3%
36.627011 1
3.3%
36.628471 1
3.3%
ValueCountFrequency (%)
36.721797 1
3.3%
36.720659067 2
6.7%
36.665455 1
3.3%
36.6651297 1
3.3%
36.6647184 1
3.3%
36.662179 1
3.3%
36.661697 1
3.3%
36.661094 1
3.3%
36.656978 1
3.3%
36.649431 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.522
Minimum127.35269
Maximum127.70387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T04:37:28.262031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.35269
5-th percentile127.43171
Q1127.46826
median127.50561
Q3127.54674
95-th percentile127.69251
Maximum127.70387
Range0.351184
Interquartile range (IQR)0.0784852

Descriptive statistics

Standard deviation0.084660684
Coefficient of variation (CV)0.00066389083
Kurtosis0.21216403
Mean127.522
Median Absolute Deviation (MAD)0.0411575
Skewness0.65764011
Sum3825.6599
Variance0.0071674314
MonotonicityNot monotonic
2023-12-13T04:37:28.391970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
127.6005325939 2
 
6.7%
127.497093 1
 
3.3%
127.507521 1
 
3.3%
127.601585 1
 
3.3%
127.352687 1
 
3.3%
127.650783 1
 
3.3%
127.430443 1
 
3.3%
127.703871 1
 
3.3%
127.701254 1
 
3.3%
127.681818 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
127.352687 1
3.3%
127.430443 1
3.3%
127.433265 1
3.3%
127.4336079 1
3.3%
127.4418369 1
3.3%
127.445436 1
3.3%
127.462441 1
3.3%
127.465816 1
3.3%
127.475591 1
3.3%
127.475612 1
3.3%
ValueCountFrequency (%)
127.703871 1
3.3%
127.701254 1
3.3%
127.681818 1
3.3%
127.650783 1
3.3%
127.601585 1
3.3%
127.6005325939 2
6.7%
127.548131 1
3.3%
127.5425868 1
3.3%
127.536475 1
3.3%
127.532071 1
3.3%

지정일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)100.0%
Memory size402.0 B

일평균이용인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.66667
Minimum20
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T04:37:28.529598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q152.5
median90
Q3100
95-th percentile177.5
Maximum500
Range480
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation86.02058
Coefficient of variation (CV)0.85450908
Kurtosis16.550549
Mean100.66667
Median Absolute Deviation (MAD)30
Skewness3.6654166
Sum3020
Variance7399.5402
MonotonicityNot monotonic
2023-12-13T04:37:28.661864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
100 9
30.0%
50 4
13.3%
150 4
13.3%
60 3
 
10.0%
40 3
 
10.0%
70 2
 
6.7%
80 2
 
6.7%
200 1
 
3.3%
500 1
 
3.3%
20 1
 
3.3%
ValueCountFrequency (%)
20 1
 
3.3%
40 3
 
10.0%
50 4
13.3%
60 3
 
10.0%
70 2
 
6.7%
80 2
 
6.7%
100 9
30.0%
150 4
13.3%
200 1
 
3.3%
500 1
 
3.3%
ValueCountFrequency (%)
500 1
 
3.3%
200 1
 
3.3%
150 4
13.3%
100 9
30.0%
80 2
 
6.7%
70 2
 
6.7%
60 3
 
10.0%
50 4
13.3%
40 3
 
10.0%
20 1
 
3.3%

수질검사일자
Date

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2018-05-09 00:00:00
Maximum2020-03-30 00:00:00
2023-12-13T04:37:28.770961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:28.870510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

수질검사결과구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
적합
27 
부적합
3 

Length

Max length3
Median length2
Mean length2.1
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
적합 27
90.0%
부적합 3
 
10.0%

Length

2023-12-13T04:37:28.989022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:37:29.088946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 27
90.0%
부적합 3
 
10.0%

부적합항목
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
27 
총대장균군
3 

Length

Max length5
Median length4
Mean length4.1
Min length4

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> 27
90.0%
총대장균군 3
 
10.0%

Length

2023-12-13T04:37:29.258342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:37:29.357875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
90.0%
총대장균군 3
 
10.0%

관리기관전화번호
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
043-201-5332
14 
043-201-8334
6 
043-201-7335
5 
043-201-6332
5 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row043-201-8334
2nd row043-201-5332
3rd row043-201-5332
4th row043-201-5332
5th row043-201-5332

Common Values

ValueCountFrequency (%)
043-201-5332 14
46.7%
043-201-8334 6
20.0%
043-201-7335 5
 
16.7%
043-201-6332 5
 
16.7%

Length

2023-12-13T04:37:29.472280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:37:29.610550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
043-201-5332 14
46.7%
043-201-8334 6
20.0%
043-201-7335 5
 
16.7%
043-201-6332 5
 
16.7%

관리기관명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
청주시 상당구청
14 
청주시 청원구청
6 
청주시 흥덕구청
5 
청주시 서원구청
5 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청주시 청원구청
2nd row청주시 상당구청
3rd row청주시 상당구청
4th row청주시 상당구청
5th row청주시 상당구청

Common Values

ValueCountFrequency (%)
청주시 상당구청 14
46.7%
청주시 청원구청 6
20.0%
청주시 흥덕구청 5
 
16.7%
청주시 서원구청 5
 
16.7%

Length

2023-12-13T04:37:29.752651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:37:29.886570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청주시 30
50.0%
상당구청 14
23.3%
청원구청 6
 
10.0%
흥덕구청 5
 
8.3%
서원구청 5
 
8.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2020-06-30 00:00:00
Maximum2020-06-30 00:00:00
2023-12-13T04:37:30.014458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:30.115899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T04:37:25.454898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:24.938522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:25.171431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:25.527166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:25.003101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:25.282472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:25.614277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:25.079788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:37:25.367406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:37:30.220511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
약수터명소재지도로명주소소재지지번주소위도경도일평균이용인구수수질검사일자수질검사결과구분관리기관전화번호관리기관명
약수터명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.000NaN1.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0000.8060.2490.7930.4940.5810.581
경도1.0001.0001.0000.8061.0000.1970.8410.6280.9130.913
일평균이용인구수1.0001.0001.0000.2490.1971.0000.0000.0000.1820.182
수질검사일자1.0001.0001.0000.7930.8410.0001.0000.2460.9560.956
수질검사결과구분1.000NaN1.0000.4940.6280.0000.2461.0000.6080.608
관리기관전화번호1.0001.0001.0000.5810.9130.1820.9560.6081.0001.000
관리기관명1.0001.0001.0000.5810.9130.1820.9560.6081.0001.000
2023-12-13T04:37:30.383648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수질검사결과구분관리기관명부적합항목관리기관전화번호
수질검사결과구분1.0000.4021.0000.402
관리기관명0.4021.0001.0001.000
부적합항목1.0001.0001.0001.000
관리기관전화번호0.4021.0001.0001.000
2023-12-13T04:37:30.869655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도일평균이용인구수수질검사결과구분부적합항목관리기관전화번호관리기관명
위도1.0000.2090.0080.5631.0000.4950.495
경도0.2091.000-0.2260.4151.0000.5680.568
일평균이용인구수0.008-0.2261.0000.0001.0000.1280.128
수질검사결과구분0.5630.4150.0001.0001.0000.4020.402
부적합항목1.0001.0001.0001.0001.0001.0001.000
관리기관전화번호0.4950.5680.1280.4021.0001.0001.000
관리기관명0.4950.5680.1280.4021.0001.0001.000

Missing values

2023-12-13T04:37:25.751417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:37:25.959864image/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

약수터명소재지도로명주소소재지지번주소위도경도지정일자일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호관리기관명데이터기준일자
0옻샘<NA>충청북도 청주시 청원구 우암동 산 7-436.647711127.497093<NA>702019-12-19적합<NA>043-201-8334청주시 청원구청2020-06-30
1고씨우물<NA>충청북도 청주시 상당구 용담동 산4936.646411127.507521<NA>2002020-03-30적합<NA>043-201-5332청주시 상당구청2020-06-30
2광덕사충청북도 청주시 상당구 당산로89번길 155(용담동)충청북도 청주시 상당구 용담동 120-136.642457127.503709<NA>1002020-03-30적합<NA>043-201-5332청주시 상당구청2020-06-30
3우암산등산로2충청북도 청주시 상당구 교동로71번길 91충청북도 청주시 상당구 수동 산2-136.641997127.499363<NA>602020-03-30적합<NA>043-201-5332청주시 상당구청2020-06-30
4어린이회관등산로<NA>충청북도 청주시 상당구 명암동 산6336.649431127.516482<NA>1002020-03-30적합<NA>043-201-5332청주시 상당구청2020-06-30
5산성등산로1<NA>충청북도 청주시 청원구 율량동 산11136.665455127.532071<NA>502020-03-12적합<NA>043-201-8334청주시 청원구청2020-06-30
6보성아파트<NA>충청북도 청주시 청원구 사천동 231-436.66513127.4777<NA>1002020-03-12적합<NA>043-201-8334청주시 청원구청2020-06-30
7용호사충청북도 청주시 상당구 명암로 103(명암동)충청북도 청주시 상당구 명암동 114-436.645874127.510808<NA>402020-03-30적합<NA>043-201-5332청주시 상당구청2020-06-30
8보살사<NA>충청북도 청주시 상당구 용암동 산48-1236.628471127.536475<NA>802020-03-30적합<NA>043-201-5332청주시 상당구청2020-06-30
9산성등산로2<NA>충청북도 청주시 상당구 명암동 산4-136.661094127.531491<NA>802020-03-30적합<NA>043-201-5332청주시 상당구청2020-06-30
약수터명소재지도로명주소소재지지번주소위도경도지정일자일평균이용인구수수질검사일자수질검사결과구분부적합항목관리기관전화번호관리기관명데이터기준일자
20명심근린공원<NA>충청북도 청주시 흥덕구 봉명동 231636.656978127.462441<NA>1002020-03-26부적합총대장균군043-201-7335청주시 흥덕구청2020-06-30
21청석굴공동우물<NA>충청북도 청주시 상당구 미원면 운암리 산2236.619942127.681818<NA>502019-07-31적합<NA>043-201-5332청주시 상당구청2020-06-30
22옥화유원지공동우물1<NA>충청북도 청주시 상당구 미원면 옥화리 2136.612945127.701254<NA>602019-07-31적합<NA>043-201-5332청주시 상당구청2020-06-30
23옥화유원지공동우물3<NA>충청북도 청주시 상당구 미원면 옥화리 153-236.617511127.703871<NA>502019-07-31적합<NA>043-201-5332청주시 상당구청2020-06-30
24양지등산로공동우물<NA>충청북도 청주시 서원구 현도면 양지리 15-136.479905127.430443<NA>502020-03-26부적합총대장균군043-201-6332청주시 서원구청2020-06-30
25잠방골옹달샘<NA>충청북도 청주시 상당구 미원면 미원리 산45-136.639149127.650783<NA>702020-03-30적합<NA>043-201-5332청주시 상당구청2020-06-30
26묘안옹달샘<NA>충청북도 청주시 흥덕구 옥산면 신촌리 산9-136.661697127.352687<NA>202020-03-16부적합총대장균군043-201-7335청주시 흥덕구청2020-06-30
27초정약수원탕매점<NA>충청북도 청주시 청원구 내수읍 초정리 111-436.720659127.600533<NA>1502019-11-14적합<NA>043-201-8334청주시 청원구청2020-06-30
28원산면옥(초정약수터)충청북도 청주시 청원구 내수읍 미원초정로 1357충청북도 청주시 청원구 내수읍 초정리 111-536.720659127.600533<NA>1502019-11-14적합<NA>043-201-8334청주시 청원구청2020-06-30
29초정공원<NA>충청북도 청주시 청원구 내수읍 초정리 64-236.721797127.601585<NA>1002018-05-09적합<NA>043-201-8334청주시 청원구청2020-06-30