Overview

Dataset statistics

Number of variables17
Number of observations21
Missing cells56
Missing cells (%)15.7%
Duplicate rows1
Duplicate rows (%)4.8%
Total size in memory3.2 KiB
Average record size in memory154.3 B

Variable types

Text2
Numeric12
Categorical3

Dataset

Description하천별 수질검사 결과에 대한 데이터로 "하천명, 수질검사일자, 측정지점(장소), 비고(수질오염지표, 측정단위)"
Author경기도 양주시
URLhttps://www.data.go.kr/data/3076878/fileData.do

Alerts

Dataset has 1 (4.8%) duplicate rowsDuplicates
관리부서 is highly overall correlated with 1월 13일 and 13 other fieldsHigh correlation
비고 is highly overall correlated with 1월 13일 and 13 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 1월 13일 and 13 other fieldsHigh correlation
1월 13일 is highly overall correlated with 2월 10일 and 6 other fieldsHigh correlation
2월 10일 is highly overall correlated with 1월 13일 and 9 other fieldsHigh correlation
3월 16일 is highly overall correlated with 2월 10일 and 4 other fieldsHigh correlation
4월 12일 is highly overall correlated with 비고 and 2 other fieldsHigh correlation
5월 18일 is highly overall correlated with 2월 10일 and 9 other fieldsHigh correlation
6월 23일 is highly overall correlated with 5월 18일 and 8 other fieldsHigh correlation
7월 18일 is highly overall correlated with 1월 13일 and 9 other fieldsHigh correlation
8월 8일 is highly overall correlated with 1월 13일 and 11 other fieldsHigh correlation
9월 22일 is highly overall correlated with 6월 23일 and 5 other fieldsHigh correlation
10월 13일 is highly overall correlated with 1월 13일 and 7 other fieldsHigh correlation
11월 15일 is highly overall correlated with 2월 10일 and 6 other fieldsHigh correlation
12월 19일 is highly overall correlated with 5월 18일 and 8 other fieldsHigh correlation
하천명 has 4 (19.0%) missing valuesMissing
1월 13일 has 4 (19.0%) missing valuesMissing
2월 10일 has 4 (19.0%) missing valuesMissing
3월 16일 has 4 (19.0%) missing valuesMissing
4월 12일 has 4 (19.0%) missing valuesMissing
5월 18일 has 4 (19.0%) missing valuesMissing
6월 23일 has 4 (19.0%) missing valuesMissing
7월 18일 has 4 (19.0%) missing valuesMissing
8월 8일 has 4 (19.0%) missing valuesMissing
9월 22일 has 4 (19.0%) missing valuesMissing
10월 13일 has 4 (19.0%) missing valuesMissing
11월 15일 has 4 (19.0%) missing valuesMissing
12월 19일 has 4 (19.0%) missing valuesMissing
측정지점 has 4 (19.0%) missing valuesMissing

Reproduction

Analysis started2024-04-06 08:56:02.829435
Analysis finished2024-04-06 08:56:32.616032
Duration29.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천명
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-06T17:56:32.826972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.9411765
Min length3

Characters and Unicode

Total characters67
Distinct characters32
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

Unique17 ?
Unique (%)100.0%

Sample

1st row신천1(상류)
2nd row신천5
3rd row신천9
4th row신천11(하류)
5th row신천중류
ValueCountFrequency (%)
신천5 1
 
5.9%
능안천 1
 
5.9%
청담천 1
 
5.9%
신천(용암교 1
 
5.9%
덕계천 1
 
5.9%
우고천 1
 
5.9%
연곡천 1
 
5.9%
홍죽천 1
 
5.9%
신천1(상류 1
 
5.9%
신천9 1
 
5.9%
Other values (7) 7
41.2%
2024-04-06T17:56:33.460992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
25.4%
6
 
9.0%
4
 
6.0%
1 3
 
4.5%
( 3
 
4.5%
3
 
4.5%
) 3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (22) 22
32.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55
82.1%
Decimal Number 5
 
7.5%
Open Punctuation 3
 
4.5%
Close Punctuation 3
 
4.5%
Dash Punctuation 1
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
17
30.9%
6
 
10.9%
4
 
7.3%
3
 
5.5%
2
 
3.6%
2
 
3.6%
2
 
3.6%
1
 
1.8%
1
 
1.8%
1
 
1.8%
Other values (16) 16
29.1%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
5 1
 
20.0%
9 1
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55
82.1%
Common 12
 
17.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
17
30.9%
6
 
10.9%
4
 
7.3%
3
 
5.5%
2
 
3.6%
2
 
3.6%
2
 
3.6%
1
 
1.8%
1
 
1.8%
1
 
1.8%
Other values (16) 16
29.1%
Common
ValueCountFrequency (%)
1 3
25.0%
( 3
25.0%
) 3
25.0%
5 1
 
8.3%
9 1
 
8.3%
- 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55
82.1%
ASCII 12
 
17.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
17
30.9%
6
 
10.9%
4
 
7.3%
3
 
5.5%
2
 
3.6%
2
 
3.6%
2
 
3.6%
1
 
1.8%
1
 
1.8%
1
 
1.8%
Other values (16) 16
29.1%
ASCII
ValueCountFrequency (%)
1 3
25.0%
( 3
25.0%
) 3
25.0%
5 1
 
8.3%
9 1
 
8.3%
- 1
 
8.3%

1월 13일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)94.1%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean6.5529412
Minimum2.1
Maximum28.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:33.812180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile2.1
Q13
median4.3
Q37.6
95-th percentile15.14
Maximum28.9
Range26.8
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation6.4977032
Coefficient of variation (CV)0.99157051
Kurtosis9.2333599
Mean6.5529412
Median Absolute Deviation (MAD)2.1
Skewness2.8118944
Sum111.4
Variance42.220147
MonotonicityNot monotonic
2024-04-06T17:56:34.079062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.1 2
 
9.5%
3.2 1
 
4.8%
2.2 1
 
4.8%
5.1 1
 
4.8%
3.4 1
 
4.8%
2.6 1
 
4.8%
28.9 1
 
4.8%
4.3 1
 
4.8%
7.0 1
 
4.8%
3.3 1
 
4.8%
Other values (6) 6
28.6%
(Missing) 4
19.0%
ValueCountFrequency (%)
2.1 2
9.5%
2.2 1
4.8%
2.6 1
4.8%
3.0 1
4.8%
3.2 1
4.8%
3.3 1
4.8%
3.4 1
4.8%
4.3 1
4.8%
5.1 1
4.8%
5.5 1
4.8%
ValueCountFrequency (%)
28.9 1
4.8%
11.7 1
4.8%
10.6 1
4.8%
8.8 1
4.8%
7.6 1
4.8%
7.0 1
4.8%
5.5 1
4.8%
5.1 1
4.8%
4.3 1
4.8%
3.4 1
4.8%

2월 10일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)88.2%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean6.0470588
Minimum1.1
Maximum24.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:34.312304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.26
Q11.5
median2.7
Q36.3
95-th percentile20.26
Maximum24.9
Range23.8
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation6.9655507
Coefficient of variation (CV)1.1518907
Kurtosis2.5867752
Mean6.0470588
Median Absolute Deviation (MAD)1.4
Skewness1.7938224
Sum102.8
Variance48.518897
MonotonicityNot monotonic
2024-04-06T17:56:34.531062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1.4 2
 
9.5%
1.5 2
 
9.5%
4.6 1
 
4.8%
5.2 1
 
4.8%
11.2 1
 
4.8%
3.5 1
 
4.8%
6.3 1
 
4.8%
19.1 1
 
4.8%
24.9 1
 
4.8%
2.4 1
 
4.8%
Other values (5) 5
23.8%
(Missing) 4
19.0%
ValueCountFrequency (%)
1.1 1
4.8%
1.3 1
4.8%
1.4 2
9.5%
1.5 2
9.5%
2.1 1
4.8%
2.4 1
4.8%
2.7 1
4.8%
3.5 1
4.8%
4.6 1
4.8%
5.2 1
4.8%
ValueCountFrequency (%)
24.9 1
4.8%
19.1 1
4.8%
12.6 1
4.8%
11.2 1
4.8%
6.3 1
4.8%
5.2 1
4.8%
4.6 1
4.8%
3.5 1
4.8%
2.7 1
4.8%
2.4 1
4.8%

3월 16일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)94.1%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean5.6058824
Minimum1.3
Maximum28.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:34.785669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2.1
Q12.8
median3.9
Q34.7
95-th percentile17.36
Maximum28.4
Range27.1
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation6.5529069
Coefficient of variation (CV)1.1689341
Kurtosis10.041917
Mean5.6058824
Median Absolute Deviation (MAD)1.1
Skewness3.0982468
Sum95.3
Variance42.940588
MonotonicityNot monotonic
2024-04-06T17:56:35.159523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2.8 2
 
9.5%
4.4 1
 
4.8%
3.9 1
 
4.8%
3.0 1
 
4.8%
2.7 1
 
4.8%
3.3 1
 
4.8%
1.3 1
 
4.8%
2.4 1
 
4.8%
2.3 1
 
4.8%
4.0 1
 
4.8%
Other values (6) 6
28.6%
(Missing) 4
19.0%
ValueCountFrequency (%)
1.3 1
4.8%
2.3 1
4.8%
2.4 1
4.8%
2.7 1
4.8%
2.8 2
9.5%
3.0 1
4.8%
3.3 1
4.8%
3.9 1
4.8%
4.0 1
4.8%
4.1 1
4.8%
ValueCountFrequency (%)
28.4 1
4.8%
14.6 1
4.8%
5.7 1
4.8%
4.9 1
4.8%
4.7 1
4.8%
4.4 1
4.8%
4.1 1
4.8%
4.0 1
4.8%
3.9 1
4.8%
3.3 1
4.8%

4월 12일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)76.5%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean3.0647059
Minimum1.6
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:35.560657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile1.68
Q12.1
median3.5
Q33.8
95-th percentile4.5
Maximum4.9
Range3.3
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.0653293
Coefficient of variation (CV)0.34761224
Kurtosis-1.4184224
Mean3.0647059
Median Absolute Deviation (MAD)0.9
Skewness0.043086091
Sum52.1
Variance1.1349265
MonotonicityNot monotonic
2024-04-06T17:56:35.940239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2.1 3
14.3%
3.5 2
9.5%
3.6 2
9.5%
1.7 1
 
4.8%
4.9 1
 
4.8%
3.8 1
 
4.8%
3.1 1
 
4.8%
3.9 1
 
4.8%
2.0 1
 
4.8%
4.3 1
 
4.8%
Other values (3) 3
14.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
1.6 1
 
4.8%
1.7 1
 
4.8%
1.9 1
 
4.8%
2.0 1
 
4.8%
2.1 3
14.3%
3.1 1
 
4.8%
3.5 2
9.5%
3.6 2
9.5%
3.8 1
 
4.8%
3.9 1
 
4.8%
ValueCountFrequency (%)
4.9 1
 
4.8%
4.4 1
 
4.8%
4.3 1
 
4.8%
3.9 1
 
4.8%
3.8 1
 
4.8%
3.6 2
9.5%
3.5 2
9.5%
3.1 1
 
4.8%
2.1 3
14.3%
2.0 1
 
4.8%

5월 18일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)76.5%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean1.8470588
Minimum0.8
Maximum3.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:36.165013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile0.8
Q11.3
median1.7
Q32
95-th percentile3.58
Maximum3.9
Range3.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.89030728
Coefficient of variation (CV)0.4820135
Kurtosis0.71468181
Mean1.8470588
Median Absolute Deviation (MAD)0.4
Skewness1.0952505
Sum31.4
Variance0.79264706
MonotonicityNot monotonic
2024-04-06T17:56:36.418376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1.6 2
9.5%
1.8 2
9.5%
1.7 2
9.5%
0.8 2
9.5%
1.3 1
 
4.8%
3.5 1
 
4.8%
2.5 1
 
4.8%
3.9 1
 
4.8%
2.0 1
 
4.8%
1.2 1
 
4.8%
Other values (3) 3
14.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
0.8 2
9.5%
0.9 1
4.8%
1.2 1
4.8%
1.3 1
4.8%
1.4 1
4.8%
1.6 2
9.5%
1.7 2
9.5%
1.8 2
9.5%
2.0 1
4.8%
2.5 1
4.8%
ValueCountFrequency (%)
3.9 1
4.8%
3.5 1
4.8%
2.9 1
4.8%
2.5 1
4.8%
2.0 1
4.8%
1.8 2
9.5%
1.7 2
9.5%
1.6 2
9.5%
1.4 1
4.8%
1.3 1
4.8%

6월 23일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)82.4%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean2.6764706
Minimum1.5
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:36.736311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.58
Q11.9
median2.5
Q33.1
95-th percentile4.34
Maximum4.5
Range3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.92771858
Coefficient of variation (CV)0.34662013
Kurtosis-0.38487765
Mean2.6764706
Median Absolute Deviation (MAD)0.6
Skewness0.6783255
Sum45.5
Variance0.86066176
MonotonicityNot monotonic
2024-04-06T17:56:36.936926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1.6 2
9.5%
2.3 2
9.5%
1.9 2
9.5%
4.3 1
 
4.8%
3.1 1
 
4.8%
2.2 1
 
4.8%
3.2 1
 
4.8%
4.0 1
 
4.8%
2.5 1
 
4.8%
2.9 1
 
4.8%
Other values (4) 4
19.0%
(Missing) 4
19.0%
ValueCountFrequency (%)
1.5 1
4.8%
1.6 2
9.5%
1.9 2
9.5%
2.2 1
4.8%
2.3 2
9.5%
2.5 1
4.8%
2.7 1
4.8%
2.9 1
4.8%
3.0 1
4.8%
3.1 1
4.8%
ValueCountFrequency (%)
4.5 1
4.8%
4.3 1
4.8%
4.0 1
4.8%
3.2 1
4.8%
3.1 1
4.8%
3.0 1
4.8%
2.9 1
4.8%
2.7 1
4.8%
2.5 1
4.8%
2.3 2
9.5%

7월 18일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)64.7%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean0.96470588
Minimum0.3
Maximum1.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:37.181681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.46
Q10.7
median1
Q31.2
95-th percentile1.58
Maximum1.9
Range1.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.39834215
Coefficient of variation (CV)0.41291565
Kurtosis0.52775033
Mean0.96470588
Median Absolute Deviation (MAD)0.3
Skewness0.57776533
Sum16.4
Variance0.15867647
MonotonicityNot monotonic
2024-04-06T17:56:37.407522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.2 3
14.3%
1.0 3
14.3%
0.7 2
9.5%
0.6 2
9.5%
0.8 1
 
4.8%
1.3 1
 
4.8%
1.5 1
 
4.8%
1.9 1
 
4.8%
0.3 1
 
4.8%
0.5 1
 
4.8%
(Missing) 4
19.0%
ValueCountFrequency (%)
0.3 1
 
4.8%
0.5 1
 
4.8%
0.6 2
9.5%
0.7 2
9.5%
0.8 1
 
4.8%
0.9 1
 
4.8%
1.0 3
14.3%
1.2 3
14.3%
1.3 1
 
4.8%
1.5 1
 
4.8%
ValueCountFrequency (%)
1.9 1
 
4.8%
1.5 1
 
4.8%
1.3 1
 
4.8%
1.2 3
14.3%
1.0 3
14.3%
0.9 1
 
4.8%
0.8 1
 
4.8%
0.7 2
9.5%
0.6 2
9.5%
0.5 1
 
4.8%

8월 8일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)70.6%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean1.3705882
Minimum0.5
Maximum3.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:37.643913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.58
Q10.9
median1.1
Q31.6
95-th percentile2.6
Maximum3.4
Range2.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.75230039
Coefficient of variation (CV)0.5488887
Kurtosis2.005311
Mean1.3705882
Median Absolute Deviation (MAD)0.5
Skewness1.3010512
Sum23.3
Variance0.56595588
MonotonicityNot monotonic
2024-04-06T17:56:37.926210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1.6 3
14.3%
0.6 2
9.5%
1.0 2
9.5%
0.9 2
9.5%
0.8 1
 
4.8%
1.5 1
 
4.8%
1.7 1
 
4.8%
3.4 1
 
4.8%
2.4 1
 
4.8%
0.5 1
 
4.8%
Other values (2) 2
9.5%
(Missing) 4
19.0%
ValueCountFrequency (%)
0.5 1
 
4.8%
0.6 2
9.5%
0.8 1
 
4.8%
0.9 2
9.5%
1.0 2
9.5%
1.1 1
 
4.8%
1.5 1
 
4.8%
1.6 3
14.3%
1.7 1
 
4.8%
2.1 1
 
4.8%
ValueCountFrequency (%)
3.4 1
 
4.8%
2.4 1
 
4.8%
2.1 1
 
4.8%
1.7 1
 
4.8%
1.6 3
14.3%
1.5 1
 
4.8%
1.1 1
 
4.8%
1.0 2
9.5%
0.9 2
9.5%
0.8 1
 
4.8%

9월 22일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)76.5%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean1.6764706
Minimum0.9
Maximum3.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:38.160362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile0.98
Q11.2
median1.5
Q32
95-th percentile3.12
Maximum3.2
Range2.3
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.70136422
Coefficient of variation (CV)0.4183576
Kurtosis0.48900193
Mean1.6764706
Median Absolute Deviation (MAD)0.4
Skewness1.134492
Sum28.5
Variance0.49191176
MonotonicityNot monotonic
2024-04-06T17:56:38.383441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1.5 3
14.3%
1.0 2
9.5%
1.2 2
9.5%
0.9 1
 
4.8%
1.3 1
 
4.8%
2.1 1
 
4.8%
3.2 1
 
4.8%
3.1 1
 
4.8%
2.5 1
 
4.8%
1.1 1
 
4.8%
Other values (3) 3
14.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
0.9 1
 
4.8%
1.0 2
9.5%
1.1 1
 
4.8%
1.2 2
9.5%
1.3 1
 
4.8%
1.5 3
14.3%
1.6 1
 
4.8%
1.8 1
 
4.8%
2.0 1
 
4.8%
2.1 1
 
4.8%
ValueCountFrequency (%)
3.2 1
 
4.8%
3.1 1
 
4.8%
2.5 1
 
4.8%
2.1 1
 
4.8%
2.0 1
 
4.8%
1.8 1
 
4.8%
1.6 1
 
4.8%
1.5 3
14.3%
1.3 1
 
4.8%
1.2 2
9.5%

10월 13일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)82.4%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean2.7529412
Minimum1.3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:38.614732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.54
Q11.9
median2.2
Q33.4
95-th percentile4.84
Maximum5
Range3.7
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.1731995
Coefficient of variation (CV)0.42616221
Kurtosis-0.69685409
Mean2.7529412
Median Absolute Deviation (MAD)0.6
Skewness0.74460679
Sum46.8
Variance1.3763971
MonotonicityNot monotonic
2024-04-06T17:56:38.888093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1.7 2
9.5%
2.0 2
9.5%
2.2 2
9.5%
3.2 1
 
4.8%
3.4 1
 
4.8%
3.7 1
 
4.8%
3.3 1
 
4.8%
2.4 1
 
4.8%
5.0 1
 
4.8%
1.6 1
 
4.8%
Other values (4) 4
19.0%
(Missing) 4
19.0%
ValueCountFrequency (%)
1.3 1
4.8%
1.6 1
4.8%
1.7 2
9.5%
1.9 1
4.8%
2.0 2
9.5%
2.2 2
9.5%
2.4 1
4.8%
3.2 1
4.8%
3.3 1
4.8%
3.4 1
4.8%
ValueCountFrequency (%)
5.0 1
4.8%
4.8 1
4.8%
4.4 1
4.8%
3.7 1
4.8%
3.4 1
4.8%
3.3 1
4.8%
3.2 1
4.8%
2.4 1
4.8%
2.2 2
9.5%
2.0 2
9.5%

11월 15일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)70.6%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean2.6470588
Minimum0.6
Maximum14.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:39.128323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.68
Q10.8
median1.2
Q31.8
95-th percentile9.7
Maximum14.1
Range13.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.5635512
Coefficient of variation (CV)1.3462304
Kurtosis6.8283866
Mean2.6470588
Median Absolute Deviation (MAD)0.4
Skewness2.6025015
Sum45
Variance12.698897
MonotonicityNot monotonic
2024-04-06T17:56:39.382561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1.5 3
14.3%
0.8 3
14.3%
1.2 2
9.5%
3.8 1
 
4.8%
0.9 1
 
4.8%
1.8 1
 
4.8%
4.2 1
 
4.8%
8.6 1
 
4.8%
14.1 1
 
4.8%
0.6 1
 
4.8%
Other values (2) 2
9.5%
(Missing) 4
19.0%
ValueCountFrequency (%)
0.6 1
 
4.8%
0.7 1
 
4.8%
0.8 3
14.3%
0.9 1
 
4.8%
1.0 1
 
4.8%
1.2 2
9.5%
1.5 3
14.3%
1.8 1
 
4.8%
3.8 1
 
4.8%
4.2 1
 
4.8%
ValueCountFrequency (%)
14.1 1
 
4.8%
8.6 1
 
4.8%
4.2 1
 
4.8%
3.8 1
 
4.8%
1.8 1
 
4.8%
1.5 3
14.3%
1.2 2
9.5%
1.0 1
 
4.8%
0.9 1
 
4.8%
0.8 3
14.3%

12월 19일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)64.7%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean1.8882353
Minimum0.3
Maximum12.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-04-06T17:56:39.597782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.46
Q10.8
median1
Q31.2
95-th percentile5.62
Maximum12.1
Range11.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation2.7708037
Coefficient of variation (CV)1.4674038
Kurtosis13.204683
Mean1.8882353
Median Absolute Deviation (MAD)0.2
Skewness3.5263415
Sum32.1
Variance7.6773529
MonotonicityNot monotonic
2024-04-06T17:56:39.845082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.8 3
14.3%
1.2 2
9.5%
1.0 2
9.5%
1.1 2
9.5%
0.9 2
9.5%
0.3 1
 
4.8%
4.0 1
 
4.8%
12.1 1
 
4.8%
2.5 1
 
4.8%
0.5 1
 
4.8%
(Missing) 4
19.0%
ValueCountFrequency (%)
0.3 1
 
4.8%
0.5 1
 
4.8%
0.8 3
14.3%
0.9 2
9.5%
1.0 2
9.5%
1.1 2
9.5%
1.2 2
9.5%
1.9 1
 
4.8%
2.5 1
 
4.8%
4.0 1
 
4.8%
ValueCountFrequency (%)
12.1 1
 
4.8%
4.0 1
 
4.8%
2.5 1
 
4.8%
1.9 1
 
4.8%
1.2 2
9.5%
1.1 2
9.5%
1.0 2
9.5%
0.9 2
9.5%
0.8 3
14.3%
0.5 1
 
4.8%

측정지점
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Memory size300.0 B
2024-04-06T17:56:40.157129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length13.235294
Min length3

Characters and Unicode

Total characters225
Distinct characters68
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)100.0%

Sample

1st row백석읍 오산3리(방성천 합류전)
2nd row우고천 합류부
3rd row은현교
4th row위생환경사업소 앞
5th row남면 상수2리(입암천 합류전)
ValueCountFrequency (%)
합류전 11
25.6%
남면 3
 
7.0%
광적면 3
 
7.0%
백석읍 3
 
7.0%
2
 
4.7%
우고천 1
 
2.3%
오산4리(신천 1
 
2.3%
용암2리(신천 1
 
2.3%
은현면 1
 
2.3%
세월교 1
 
2.3%
Other values (16) 16
37.2%
2024-04-06T17:56:40.720860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
11.6%
( 13
 
5.8%
13
 
5.8%
) 13
 
5.8%
12
 
5.3%
12
 
5.3%
11
 
4.9%
9
 
4.0%
8
 
3.6%
7
 
3.1%
Other values (58) 101
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 160
71.1%
Space Separator 26
 
11.6%
Open Punctuation 13
 
5.8%
Close Punctuation 13
 
5.8%
Decimal Number 11
 
4.9%
Dash Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13
 
8.1%
12
 
7.5%
12
 
7.5%
11
 
6.9%
9
 
5.6%
8
 
5.0%
7
 
4.4%
6
 
3.8%
4
 
2.5%
4
 
2.5%
Other values (49) 74
46.2%
Decimal Number
ValueCountFrequency (%)
1 4
36.4%
2 3
27.3%
4 2
18.2%
9 1
 
9.1%
3 1
 
9.1%
Space Separator
ValueCountFrequency (%)
26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 160
71.1%
Common 65
28.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13
 
8.1%
12
 
7.5%
12
 
7.5%
11
 
6.9%
9
 
5.6%
8
 
5.0%
7
 
4.4%
6
 
3.8%
4
 
2.5%
4
 
2.5%
Other values (49) 74
46.2%
Common
ValueCountFrequency (%)
26
40.0%
( 13
20.0%
) 13
20.0%
1 4
 
6.2%
2 3
 
4.6%
4 2
 
3.1%
- 2
 
3.1%
9 1
 
1.5%
3 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 160
71.1%
ASCII 65
28.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26
40.0%
( 13
20.0%
) 13
20.0%
1 4
 
6.2%
2 3
 
4.6%
4 2
 
3.1%
- 2
 
3.1%
9 1
 
1.5%
3 1
 
1.5%
Hangul
ValueCountFrequency (%)
13
 
8.1%
12
 
7.5%
12
 
7.5%
11
 
6.9%
9
 
5.6%
8
 
5.0%
7
 
4.4%
6
 
3.8%
4
 
2.5%
4
 
2.5%
Other values (49) 74
46.2%

비고
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
BOD, 단위:mg/L
17 
<NA>

Length

Max length12
Median length12
Mean length10.47619
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOD, 단위:mg/L
2nd rowBOD, 단위:mg/L
3rd rowBOD, 단위:mg/L
4th rowBOD, 단위:mg/L
5th rowBOD, 단위:mg/L

Common Values

ValueCountFrequency (%)
BOD, 단위:mg/L 17
81.0%
<NA> 4
 
19.0%

Length

2024-04-06T17:56:40.989405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:56:41.195013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bod 17
44.7%
단위:mg/l 17
44.7%
na 4
 
10.5%

관리부서
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
양주시 환경정책과
17 
<NA>

Length

Max length9
Median length9
Mean length8.047619
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양주시 환경정책과
2nd row양주시 환경정책과
3rd row양주시 환경정책과
4th row양주시 환경정책과
5th row양주시 환경정책과

Common Values

ValueCountFrequency (%)
양주시 환경정책과 17
81.0%
<NA> 4
 
19.0%

Length

2024-04-06T17:56:41.417989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:56:41.609322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양주시 17
44.7%
환경정책과 17
44.7%
na 4
 
10.5%

데이터기준일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-29
17 
<NA>

Length

Max length10
Median length10
Mean length8.8571429
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-29
2nd row2024-03-29
3rd row2024-03-29
4th row2024-03-29
5th row2024-03-29

Common Values

ValueCountFrequency (%)
2024-03-29 17
81.0%
<NA> 4
 
19.0%

Length

2024-04-06T17:56:41.806746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:56:42.004799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-29 17
81.0%
na 4
 
19.0%

Interactions

2024-04-06T17:56:28.894970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:03.870637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:05.934632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:08.077901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:10.137464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:12.761657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:15.045332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:17.611260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:20.042226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:22.341453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:24.499926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:26.697457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:29.054383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:04.027942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:06.109306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:08.297357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:10.308118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:13.067276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:15.219173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:17.854941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:20.200977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:22.565492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:24.668638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:26.852293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:29.221931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:04.176665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:06.310657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:08.459687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:10.467391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:13.252826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:15.383918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:18.040467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:20.337993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:22.727592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:24.831023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:27.034466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:29.384925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:04.320157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:06.530781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:08.603610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:11.056589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:13.455935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:15.659220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:18.200776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:20.494872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:22.868706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:24.998171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:27.193467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:29.556321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:04.484977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:06.751761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:08.779980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:11.224930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:13.635680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:15.878001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:18.380266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:20.673902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:23.023906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:25.190389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:27.413522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:29.734566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:04.671443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:06.905400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:08.941271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:11.407621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:13.830462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:16.095166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:18.578264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:20.830771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:23.186669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:25.368437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:27.624390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:29.914024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:04.891351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:07.086627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:09.113604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:11.568045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:14.011804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:16.290737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:18.771060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:20.989122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:23.351046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:25.564734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:27.818235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:30.080948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:05.087163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:07.253585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:09.315330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:11.771685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:14.194078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:16.505708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:18.964355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:21.150234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:23.515599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:25.760401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:27.990356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:30.257520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:05.290994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:07.404574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:09.473578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:11.949670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:14.359531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:16.725196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:19.162064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:21.311938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:23.661481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:25.949282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:28.171846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:30.394130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:05.451633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:07.576866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:09.623005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:12.093676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:14.503463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:16.940823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:19.378766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:21.457397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:23.857991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:26.146869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:28.337228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:30.574073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:05.612168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:07.751667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:09.795020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:12.263843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:14.691366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:17.146277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:19.565293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:21.630924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:24.092049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:26.342256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:28.525645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:30.759444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:05.778434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:07.923244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:09.970236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:12.463430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:14.872858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:17.342216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:19.838546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:21.785930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:24.257337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:26.523655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:56:28.685356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:56:42.190477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천명1월 13일2월 10일3월 16일4월 12일5월 18일6월 23일7월 18일8월 8일9월 22일10월 13일11월 15일12월 19일측정지점
하천명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1월 13일1.0001.0000.7560.5960.5300.6550.0000.4780.5770.6100.7270.0960.4411.000
2월 10일1.0000.7561.0000.9010.5370.9030.3570.8550.8060.2150.5100.7650.9041.000
3월 16일1.0000.5960.9011.0000.7570.9720.0000.7010.8790.6660.2980.8210.8651.000
4월 12일1.0000.5300.5370.7571.0000.5400.2830.7550.7180.3310.8290.5670.7031.000
5월 18일1.0000.6550.9030.9720.5401.0000.7060.7520.8590.7170.0000.9070.9931.000
6월 23일1.0000.0000.3570.0000.2830.7061.0000.5090.0000.4370.4180.7260.3271.000
7월 18일1.0000.4780.8550.7010.7550.7520.5091.0000.7900.1680.3140.8430.8631.000
8월 8일1.0000.5770.8060.8790.7180.8590.0000.7901.0000.5680.8110.8540.9911.000
9월 22일1.0000.6100.2150.6660.3310.7170.4370.1680.5681.0000.3800.7670.9221.000
10월 13일1.0000.7270.5100.2980.8290.0000.4180.3140.8110.3801.0000.1400.5921.000
11월 15일1.0000.0960.7650.8210.5670.9070.7260.8430.8540.7670.1401.0000.9661.000
12월 19일1.0000.4410.9040.8650.7030.9930.3270.8630.9910.9220.5920.9661.0001.000
측정지점1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-04-06T17:56:42.453998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리부서비고데이터기준일자
관리부서1.0001.0001.000
비고1.0001.0001.000
데이터기준일자1.0001.0001.000
2024-04-06T17:56:42.644056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월 13일2월 10일3월 16일4월 12일5월 18일6월 23일7월 18일8월 8일9월 22일10월 13일11월 15일12월 19일비고관리부서데이터기준일자
1월 13일1.0000.6810.3910.0330.3820.3080.5340.6650.2670.6970.2920.3351.0001.0001.000
2월 10일0.6811.0000.667-0.0060.6810.3850.5110.6560.0850.5580.6550.3591.0001.0001.000
3월 16일0.3910.6671.0000.1400.6130.4240.4240.4850.1590.4310.4540.4251.0001.0001.000
4월 12일0.033-0.0060.1401.0000.1210.0460.0830.2590.2540.2470.1550.2131.0001.0001.000
5월 18일0.3820.6810.6130.1211.0000.5350.6020.5890.2580.3250.5470.5031.0001.0001.000
6월 23일0.3080.3850.4240.0460.5351.0000.6310.5770.6160.5280.3900.6781.0001.0001.000
7월 18일0.5340.5110.4240.0830.6020.6311.0000.7870.4940.6630.3410.5291.0001.0001.000
8월 8일0.6650.6560.4850.2590.5890.5770.7871.0000.5400.5620.5600.6251.0001.0001.000
9월 22일0.2670.0850.1590.2540.2580.6160.4940.5401.0000.4670.3890.8751.0001.0001.000
10월 13일0.6970.5580.4310.2470.3250.5280.6630.5620.4671.0000.2010.4861.0001.0001.000
11월 15일0.2920.6550.4540.1550.5470.3900.3410.5600.3890.2011.0000.5741.0001.0001.000
12월 19일0.3350.3590.4250.2130.5030.6780.5290.6250.8750.4860.5741.0001.0001.0001.000
비고1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관리부서1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-04-06T17:56:31.039800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:56:31.435791image/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.
2024-04-06T17:56:31.824631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

하천명1월 13일2월 10일3월 16일4월 12일5월 18일6월 23일7월 18일8월 8일9월 22일10월 13일11월 15일12월 19일측정지점비고관리부서데이터기준일자
0신천1(상류)4.34.62.31.71.31.60.70.80.91.73.80.3백석읍 오산3리(방성천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
1신천57.05.22.82.11.62.30.81.51.33.20.90.8우고천 합류부BOD, 단위:mg/L양주시 환경정책과2024-03-29
2신천97.611.24.92.11.84.31.21.61.53.41.81.2은현교BOD, 단위:mg/L양주시 환경정책과2024-03-29
3신천11(하류)5.53.55.74.91.73.11.31.71.53.71.51.0위생환경사업소 앞BOD, 단위:mg/L양주시 환경정책과2024-03-29
4신천중류8.86.34.13.83.52.31.53.42.13.34.24.0남면 상수2리(입암천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
5석우천10.619.128.43.52.52.21.01.61.02.41.20.8광적면 석우리(신천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
6효촌천11.724.914.63.13.93.21.92.43.25.08.612.1남면 상수1리(신천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
7입암천3.32.44.03.92.04.00.71.63.11.614.12.5남면 상수2리(신천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
8회암천3.02.14.73.61.21.60.30.51.02.00.80.8봉양동 941-1(신천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
9회암천-중3.22.74.42.01.72.50.50.61.52.21.51.2회암천-중(율정초교앞)BOD, 단위:mg/L양주시 환경정책과2024-03-29
하천명1월 13일2월 10일3월 16일4월 12일5월 18일6월 23일7월 18일8월 8일9월 22일10월 13일11월 15일12월 19일측정지점비고관리부서데이터기준일자
11홍죽천2.11.11.34.31.61.90.60.61.11.90.80.5백석읍 백석매립장(생활체육공원)BOD, 단위:mg/L양주시 환경정책과2024-03-29
12연곡천2.61.53.31.62.93.01.20.91.21.70.70.9광적면 광석리(신천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
13우고천3.41.42.72.10.81.50.61.11.21.30.80.9광적면 가납1리(신천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
14덕계천2.11.33.01.90.82.71.01.02.02.01.01.0고암동(청담천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
15신천(용암교)5.112.63.93.61.84.51.22.11.64.41.51.9구 세월교 앞BOD, 단위:mg/L양주시 환경정책과2024-03-29
16청담천2.21.52.84.41.41.90.90.91.82.21.21.1은현면 용암2리(신천 합류전)BOD, 단위:mg/L양주시 환경정책과2024-03-29
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