Overview

Dataset statistics

Number of variables11
Number of observations2926
Missing cells63
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory274.4 KiB
Average record size in memory96.0 B

Variable types

Categorical1
Text2
Numeric8

Dataset

Description구명,동명,수질측정지점명,전기전도도,PH,잔류염소,탁도,수온,구코드,동코드,측정시간
Author상수도사업본부
URLhttps://data.seoul.go.kr/dataList/OA-13507/S/1/datasetView.do

Alerts

구코드 is highly overall correlated with 동코드 and 1 other fieldsHigh correlation
동코드 is highly overall correlated with 구코드 and 1 other fieldsHigh correlation
구명 is highly overall correlated with 구코드 and 1 other fieldsHigh correlation
동코드 has 63 (2.2%) missing valuesMissing

Reproduction

Analysis started2024-05-11 09:06:04.573199
Analysis finished2024-05-11 09:06:30.413378
Duration25.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
송파구
 
189
관악구
 
147
강남구
 
147
강서구
 
140
노원구
 
133
Other values (20)
2170 

Length

Max length4
Median length3
Mean length3.076555
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송파구 189
 
6.5%
관악구 147
 
5.0%
강남구 147
 
5.0%
강서구 140
 
4.8%
노원구 133
 
4.5%
성북구 133
 
4.5%
강동구 133
 
4.5%
영등포구 126
 
4.3%
서초구 119
 
4.1%
종로구 119
 
4.1%
Other values (15) 1540
52.6%

Length

2024-05-11T09:06:30.558076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 189
 
6.5%
강남구 147
 
5.0%
관악구 147
 
5.0%
강서구 140
 
4.8%
노원구 133
 
4.5%
성북구 133
 
4.5%
강동구 133
 
4.5%
영등포구 126
 
4.3%
서초구 119
 
4.1%
종로구 119
 
4.1%
Other values (15) 1540
52.6%

동명
Text

Distinct417
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
2024-05-11T09:06:31.138964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.7942584
Min length2

Characters and Unicode

Total characters11102
Distinct characters188
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개포1동
2nd row개포2동
3rd row개포4동
4th row논현1동
5th row논현2동
ValueCountFrequency (%)
신사동 14
 
0.5%
정릉4동 7
 
0.2%
방이1동 7
 
0.2%
문정2동 7
 
0.2%
문정1동 7
 
0.2%
마천2동 7
 
0.2%
마천1동 7
 
0.2%
거여2동 7
 
0.2%
거여1동 7
 
0.2%
가락본동 7
 
0.2%
Other values (407) 2849
97.4%
2024-05-11T09:06:32.065434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2940
26.5%
1 672
 
6.1%
2 665
 
6.0%
3 301
 
2.7%
266
 
2.4%
4 182
 
1.6%
161
 
1.5%
119
 
1.1%
119
 
1.1%
119
 
1.1%
Other values (178) 5558
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9016
81.2%
Decimal Number 2023
 
18.2%
Other Punctuation 63
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2940
32.6%
266
 
3.0%
161
 
1.8%
119
 
1.3%
119
 
1.3%
119
 
1.3%
112
 
1.2%
112
 
1.2%
112
 
1.2%
105
 
1.2%
Other values (167) 4851
53.8%
Decimal Number
ValueCountFrequency (%)
1 672
33.2%
2 665
32.9%
3 301
14.9%
4 182
 
9.0%
5 77
 
3.8%
6 49
 
2.4%
7 42
 
2.1%
8 21
 
1.0%
9 7
 
0.3%
0 7
 
0.3%
Other Punctuation
ValueCountFrequency (%)
? 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9016
81.2%
Common 2086
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2940
32.6%
266
 
3.0%
161
 
1.8%
119
 
1.3%
119
 
1.3%
119
 
1.3%
112
 
1.2%
112
 
1.2%
112
 
1.2%
105
 
1.2%
Other values (167) 4851
53.8%
Common
ValueCountFrequency (%)
1 672
32.2%
2 665
31.9%
3 301
14.4%
4 182
 
8.7%
5 77
 
3.7%
? 63
 
3.0%
6 49
 
2.3%
7 42
 
2.0%
8 21
 
1.0%
9 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9016
81.2%
ASCII 2086
 
18.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2940
32.6%
266
 
3.0%
161
 
1.8%
119
 
1.3%
119
 
1.3%
119
 
1.3%
112
 
1.2%
112
 
1.2%
112
 
1.2%
105
 
1.2%
Other values (167) 4851
53.8%
ASCII
ValueCountFrequency (%)
1 672
32.2%
2 665
31.9%
3 301
14.4%
4 182
 
8.7%
5 77
 
3.7%
? 63
 
3.0%
6 49
 
2.3%
7 42
 
2.0%
8 21
 
1.0%
9 7
 
0.3%
Distinct399
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
2024-05-11T09:06:32.686885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length9.7799043
Min length7

Characters and Unicode

Total characters28616
Distinct characters238
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row잠원 [기타]
2nd row방배3 [기타]
3rd row개포1 [기타]
4th row내곡 [배수지]
5th row방배1 [기타]
ValueCountFrequency (%)
기타 1421
24.3%
공원 567
 
9.7%
학교 182
 
3.1%
배수지 175
 
3.0%
지역배수지 140
 
2.4%
관공서 119
 
2.0%
아파트 98
 
1.7%
올림터 70
 
1.2%
소방서 49
 
0.8%
수도사업소 49
 
0.8%
Other values (399) 2982
51.0%
2024-05-11T09:06:33.745620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
] 2926
 
10.2%
2926
 
10.2%
[ 2926
 
10.2%
2366
 
8.3%
1442
 
5.0%
1421
 
5.0%
777
 
2.7%
700
 
2.4%
1 644
 
2.3%
2 609
 
2.1%
Other values (228) 11879
41.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17003
59.4%
Close Punctuation 3304
 
11.5%
Open Punctuation 3304
 
11.5%
Space Separator 2926
 
10.2%
Decimal Number 1939
 
6.8%
Uppercase Letter 70
 
0.2%
Other Punctuation 70
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2366
 
13.9%
1442
 
8.5%
1421
 
8.4%
777
 
4.6%
700
 
4.1%
518
 
3.0%
518
 
3.0%
343
 
2.0%
287
 
1.7%
273
 
1.6%
Other values (210) 8358
49.2%
Decimal Number
ValueCountFrequency (%)
1 644
33.2%
2 609
31.4%
3 280
14.4%
4 182
 
9.4%
5 77
 
4.0%
6 49
 
2.5%
9 42
 
2.2%
7 28
 
1.4%
8 21
 
1.1%
0 7
 
0.4%
Close Punctuation
ValueCountFrequency (%)
] 2926
88.6%
) 378
 
11.4%
Open Punctuation
ValueCountFrequency (%)
[ 2926
88.6%
( 378
 
11.4%
Other Punctuation
ValueCountFrequency (%)
, 49
70.0%
. 21
30.0%
Space Separator
ValueCountFrequency (%)
2926
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17003
59.4%
Common 11543
40.3%
Latin 70
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2366
 
13.9%
1442
 
8.5%
1421
 
8.4%
777
 
4.6%
700
 
4.1%
518
 
3.0%
518
 
3.0%
343
 
2.0%
287
 
1.7%
273
 
1.6%
Other values (210) 8358
49.2%
Common
ValueCountFrequency (%)
] 2926
25.3%
2926
25.3%
[ 2926
25.3%
1 644
 
5.6%
2 609
 
5.3%
) 378
 
3.3%
( 378
 
3.3%
3 280
 
2.4%
4 182
 
1.6%
5 77
 
0.7%
Other values (7) 217
 
1.9%
Latin
ValueCountFrequency (%)
A 70
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17003
59.4%
ASCII 11613
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
] 2926
25.2%
2926
25.2%
[ 2926
25.2%
1 644
 
5.5%
2 609
 
5.2%
) 378
 
3.3%
( 378
 
3.3%
3 280
 
2.4%
4 182
 
1.6%
5 77
 
0.7%
Other values (8) 287
 
2.5%
Hangul
ValueCountFrequency (%)
2366
 
13.9%
1442
 
8.5%
1421
 
8.4%
777
 
4.6%
700
 
4.1%
518
 
3.0%
518
 
3.0%
343
 
2.0%
287
 
1.7%
273
 
1.6%
Other values (210) 8358
49.2%

전기전도도
Real number (ℝ)

Distinct86
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.38448
Minimum0
Maximum269
Zeros7
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size25.8 KiB
2024-05-11T09:06:34.180573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile163
Q1171
median183
Q3191
95-th percentile201
Maximum269
Range269
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.74105
Coefficient of variation (CV)0.086782784
Kurtosis41.387642
Mean181.38448
Median Absolute Deviation (MAD)10
Skewness-3.5504862
Sum530731
Variance247.78067
MonotonicityNot monotonic
2024-05-11T09:06:34.644710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185 106
 
3.6%
188 104
 
3.6%
190 99
 
3.4%
168 97
 
3.3%
187 95
 
3.2%
186 94
 
3.2%
169 90
 
3.1%
191 90
 
3.1%
183 87
 
3.0%
189 86
 
2.9%
Other values (76) 1978
67.6%
ValueCountFrequency (%)
0 7
0.2%
120 1
 
< 0.1%
121 1
 
< 0.1%
122 1
 
< 0.1%
123 1
 
< 0.1%
124 2
 
0.1%
125 1
 
< 0.1%
147 1
 
< 0.1%
148 4
0.1%
149 5
0.2%
ValueCountFrequency (%)
269 1
 
< 0.1%
235 4
0.1%
232 2
0.1%
229 2
0.1%
226 2
0.1%
223 2
0.1%
220 2
0.1%
218 2
0.1%
217 1
 
< 0.1%
216 4
0.1%

PH
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0967532
Minimum0
Maximum7.5
Zeros7
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size25.8 KiB
2024-05-11T09:06:35.015446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.9
Q17
median7.1
Q37.2
95-th percentile7.3
Maximum7.5
Range7.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.36522287
Coefficient of variation (CV)0.051463375
Kurtosis338.90595
Mean7.0967532
Median Absolute Deviation (MAD)0.1
Skewness-17.56316
Sum20765.1
Variance0.13338775
MonotonicityNot monotonic
2024-05-11T09:06:35.420852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7.1 1106
37.8%
7.2 824
28.2%
7.0 519
17.7%
7.3 223
 
7.6%
6.9 162
 
5.5%
6.8 42
 
1.4%
7.4 31
 
1.1%
7.5 7
 
0.2%
0.0 7
 
0.2%
6.7 5
 
0.2%
ValueCountFrequency (%)
0.0 7
 
0.2%
6.7 5
 
0.2%
6.8 42
 
1.4%
6.9 162
 
5.5%
7.0 519
17.7%
7.1 1106
37.8%
7.2 824
28.2%
7.3 223
 
7.6%
7.4 31
 
1.1%
7.5 7
 
0.2%
ValueCountFrequency (%)
7.5 7
 
0.2%
7.4 31
 
1.1%
7.3 223
 
7.6%
7.2 824
28.2%
7.1 1106
37.8%
7.0 519
17.7%
6.9 162
 
5.5%
6.8 42
 
1.4%
6.7 5
 
0.2%
0.0 7
 
0.2%

잔류염소
Real number (ℝ)

Distinct38
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28941558
Minimum0
Maximum0.49
Zeros7
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size25.8 KiB
2024-05-11T09:06:35.890165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.18
Q10.24
median0.29
Q30.33
95-th percentile0.41
Maximum0.49
Range0.49
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.071316558
Coefficient of variation (CV)0.24641575
Kurtosis0.47888796
Mean0.28941558
Median Absolute Deviation (MAD)0.04
Skewness0.22609824
Sum846.83
Variance0.0050860515
MonotonicityNot monotonic
2024-05-11T09:06:36.712705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.3 210
 
7.2%
0.29 207
 
7.1%
0.27 191
 
6.5%
0.31 177
 
6.0%
0.32 166
 
5.7%
0.24 149
 
5.1%
0.28 148
 
5.1%
0.26 144
 
4.9%
0.25 138
 
4.7%
0.33 102
 
3.5%
Other values (28) 1294
44.2%
ValueCountFrequency (%)
0.0 7
 
0.2%
0.13 1
 
< 0.1%
0.14 11
 
0.4%
0.15 13
 
0.4%
0.16 25
 
0.9%
0.17 59
2.0%
0.18 86
2.9%
0.19 88
3.0%
0.2 85
2.9%
0.21 83
2.8%
ValueCountFrequency (%)
0.49 10
 
0.3%
0.48 33
1.1%
0.47 10
 
0.3%
0.46 23
0.8%
0.45 13
 
0.4%
0.44 16
 
0.5%
0.43 14
 
0.5%
0.42 21
0.7%
0.41 39
1.3%
0.4 41
1.4%

탁도
Real number (ℝ)

Distinct18
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.054336979
Minimum0
Maximum0.19
Zeros9
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size25.8 KiB
2024-05-11T09:06:37.169765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.05
median0.05
Q30.06
95-th percentile0.08
Maximum0.19
Range0.19
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.013820136
Coefficient of variation (CV)0.25434126
Kurtosis18.270835
Mean0.054336979
Median Absolute Deviation (MAD)0
Skewness2.631001
Sum158.99
Variance0.00019099615
MonotonicityNot monotonic
2024-05-11T09:06:37.640877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.05 1718
58.7%
0.06 398
 
13.6%
0.04 286
 
9.8%
0.07 261
 
8.9%
0.08 152
 
5.2%
0.03 35
 
1.2%
0.09 33
 
1.1%
0.0 9
 
0.3%
0.02 8
 
0.3%
0.17 5
 
0.2%
Other values (8) 21
 
0.7%
ValueCountFrequency (%)
0.0 9
 
0.3%
0.01 4
 
0.1%
0.02 8
 
0.3%
0.03 35
 
1.2%
0.04 286
 
9.8%
0.05 1718
58.7%
0.06 398
 
13.6%
0.07 261
 
8.9%
0.08 152
 
5.2%
0.09 33
 
1.1%
ValueCountFrequency (%)
0.19 1
 
< 0.1%
0.17 5
 
0.2%
0.16 2
 
0.1%
0.15 3
 
0.1%
0.14 2
 
0.1%
0.13 4
 
0.1%
0.11 1
 
< 0.1%
0.1 4
 
0.1%
0.09 33
 
1.1%
0.08 152
5.2%

수온
Real number (ℝ)

Distinct63
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.613397
Minimum0
Maximum20.3
Zeros7
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size25.8 KiB
2024-05-11T09:06:38.660483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.8
Q117.1
median17.8
Q318.3
95-th percentile19.2
Maximum20.3
Range20.3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.3500104
Coefficient of variation (CV)0.076646794
Kurtosis67.641222
Mean17.613397
Median Absolute Deviation (MAD)0.6
Skewness-5.4780227
Sum51536.8
Variance1.8225281
MonotonicityNot monotonic
2024-05-11T09:06:39.720976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 141
 
4.8%
18.3 135
 
4.6%
17.6 134
 
4.6%
17.9 133
 
4.5%
18.2 130
 
4.4%
17.8 128
 
4.4%
17.7 124
 
4.2%
17.5 116
 
4.0%
18.4 114
 
3.9%
18.5 105
 
3.6%
Other values (53) 1666
56.9%
ValueCountFrequency (%)
0.0 7
0.2%
14.1 1
 
< 0.1%
14.2 8
0.3%
14.4 1
 
< 0.1%
14.5 5
 
0.2%
14.6 8
0.3%
14.7 3
 
0.1%
14.8 4
 
0.1%
14.9 2
 
0.1%
15.0 17
0.6%
ValueCountFrequency (%)
20.3 5
 
0.2%
20.2 4
 
0.1%
20.1 6
 
0.2%
20.0 8
 
0.3%
19.9 10
 
0.3%
19.8 17
0.6%
19.7 9
 
0.3%
19.6 15
0.5%
19.5 9
 
0.3%
19.4 35
1.2%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11136.603
Minimum11010
Maximum11250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 KiB
2024-05-11T09:06:40.655460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11020
Q111070
median11140
Q311210
95-th percentile11240
Maximum11250
Range240
Interquartile range (IQR)140

Descriptive statistics

Standard deviation73.909236
Coefficient of variation (CV)0.0066366052
Kurtosis-1.2388412
Mean11136.603
Median Absolute Deviation (MAD)70
Skewness-0.091352778
Sum32585700
Variance5462.5752
MonotonicityNot monotonic
2024-05-11T09:06:41.493298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11240 189
 
6.5%
11230 147
 
5.0%
11210 147
 
5.0%
11160 140
 
4.8%
11080 133
 
4.5%
11250 133
 
4.5%
11110 133
 
4.5%
11190 126
 
4.3%
11220 119
 
4.1%
11010 119
 
4.1%
Other values (15) 1540
52.6%
ValueCountFrequency (%)
11010 119
4.1%
11020 98
3.3%
11030 112
3.8%
11040 105
3.6%
11050 105
3.6%
11060 98
3.3%
11070 112
3.8%
11080 133
4.5%
11090 91
3.1%
11100 98
3.3%
ValueCountFrequency (%)
11250 133
4.5%
11240 189
6.5%
11230 147
5.0%
11220 119
4.1%
11210 147
5.0%
11200 98
3.3%
11190 126
4.3%
11180 70
 
2.4%
11170 112
3.8%
11160 140
4.8%

동코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct409
Distinct (%)14.3%
Missing63
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1113791
Minimum1101053
Maximum1125074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 KiB
2024-05-11T09:06:42.154199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1102060
Q11107073
median1114072
Q31121052
95-th percentile1124079
Maximum1125074
Range24021
Interquartile range (IQR)13979

Descriptive statistics

Standard deviation7349.6264
Coefficient of variation (CV)0.0065987483
Kurtosis-1.2357124
Mean1113791
Median Absolute Deviation (MAD)6982
Skewness-0.1071405
Sum3.1887836 × 109
Variance54017009
MonotonicityNot monotonic
2024-05-11T09:06:42.973393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1108065 7
 
0.2%
1124069 7
 
0.2%
1124068 7
 
0.2%
1124056 7
 
0.2%
1124055 7
 
0.2%
1124054 7
 
0.2%
1124053 7
 
0.2%
1124065 7
 
0.2%
1124067 7
 
0.2%
1124066 7
 
0.2%
Other values (399) 2793
95.5%
(Missing) 63
 
2.2%
ValueCountFrequency (%)
1101053 7
0.2%
1101054 7
0.2%
1101055 7
0.2%
1101056 7
0.2%
1101057 7
0.2%
1101058 7
0.2%
1101060 7
0.2%
1101064 7
0.2%
1101067 7
0.2%
1101068 7
0.2%
ValueCountFrequency (%)
1125074 7
0.2%
1125073 7
0.2%
1125072 7
0.2%
1125071 7
0.2%
1125070 7
0.2%
1125067 7
0.2%
1125066 7
0.2%
1125065 7
0.2%
1125063 7
0.2%
1125061 7
0.2%

측정시간
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0240511 × 109
Minimum2.0240511 × 109
Maximum2.0240511 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.8 KiB
2024-05-11T09:06:43.489821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0240511 × 109
5-th percentile2.0240511 × 109
Q12.0240511 × 109
median2.0240511 × 109
Q32.0240511 × 109
95-th percentile2.0240511 × 109
Maximum2.0240511 × 109
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0003419
Coefficient of variation (CV)9.8828623 × 10-10
Kurtosis-1.2500853
Mean2.0240511 × 109
Median Absolute Deviation (MAD)2
Skewness0
Sum5.9223736 × 1012
Variance4.0013675
MonotonicityDecreasing
2024-05-11T09:06:44.180267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2024051117 418
14.3%
2024051116 418
14.3%
2024051115 418
14.3%
2024051114 418
14.3%
2024051113 418
14.3%
2024051112 418
14.3%
2024051111 418
14.3%
ValueCountFrequency (%)
2024051111 418
14.3%
2024051112 418
14.3%
2024051113 418
14.3%
2024051114 418
14.3%
2024051115 418
14.3%
2024051116 418
14.3%
2024051117 418
14.3%
ValueCountFrequency (%)
2024051117 418
14.3%
2024051116 418
14.3%
2024051115 418
14.3%
2024051114 418
14.3%
2024051113 418
14.3%
2024051112 418
14.3%
2024051111 418
14.3%

Interactions

2024-05-11T09:06:27.363602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:08.413448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:11.708315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:14.416047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:17.228597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:19.782154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:22.688872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:25.005379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:27.635736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:08.690388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:12.020889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:14.741277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:17.518059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:20.110519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:22.961258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:25.279855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:27.918282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:09.118869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:12.366084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:15.078121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:17.836422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:20.607837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:23.248987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:25.664658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:28.214061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:09.585612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:12.733916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:15.477402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:18.179464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:20.993484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:23.589414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:25.954670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:28.513160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:09.991956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:13.120765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:15.793326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:18.509118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:21.516752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:23.875367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:26.243268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:28.820451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:10.340208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:13.510019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:16.112187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:18.830449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:21.816386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:24.175882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:26.533565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:29.096062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:10.907694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:13.807464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:16.484643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:19.180624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:22.112282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:24.450183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:26.805544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:29.379624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:11.340461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:14.086864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:16.878860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:19.470582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:22.400196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:24.720972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:06:27.083408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:06:44.498629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명전기전도도PH잔류염소탁도수온구코드동코드측정시간
구명1.0000.5210.3390.7250.5630.6041.0001.0000.000
전기전도도0.5211.0000.7680.6520.1120.6660.3290.3400.000
PH0.3390.7681.0000.9380.0000.7180.2010.2030.000
잔류염소0.7250.6520.9381.0000.2840.7070.5170.5150.000
탁도0.5630.1120.0000.2841.0000.2550.4610.4430.000
수온0.6040.6660.7180.7070.2551.0000.5050.4870.131
구코드1.0000.3290.2010.5170.4610.5051.0001.0000.000
동코드1.0000.3400.2030.5150.4430.4871.0001.0000.000
측정시간0.0000.0000.0000.0000.0000.1310.0000.0001.000
2024-05-11T09:06:44.968784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전기전도도PH잔류염소탁도수온구코드동코드측정시간구명
전기전도도1.000-0.028-0.0880.0730.020-0.033-0.029-0.0810.251
PH-0.0281.0000.145-0.1170.0900.0810.077-0.0430.184
잔류염소-0.0880.1451.000-0.0610.1400.4180.4080.0090.378
탁도0.073-0.117-0.0611.000-0.148-0.108-0.1020.0020.234
수온0.0200.0900.140-0.1481.0000.1520.142-0.1100.306
구코드-0.0330.0810.418-0.1080.1521.0000.9990.0000.997
동코드-0.0290.0770.408-0.1020.1420.9991.0000.0000.992
측정시간-0.081-0.0430.0090.002-0.1100.0000.0001.0000.000
구명0.2510.1840.3780.2340.3060.9970.9920.0001.000

Missing values

2024-05-11T09:06:29.781710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:06:30.298800image/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

구명동명수질측정지점명전기전도도PH잔류염소탁도수온구코드동코드측정시간
0강남구개포1동잠원 [기타]1827.00.370.0617.21123011230682024051117
1강남구개포2동방배3 [기타]1507.20.480.0517.81123011230802024051117
2강남구개포4동개포1 [기타]1716.90.340.0517.61123011230712024051117
3강남구논현1동내곡 [배수지]1707.30.340.0518.31123011230522024051117
4강남구논현2동방배1 [기타]1737.00.370.0515.91123011230532024051117
5강남구대치1동압구정 [기타]1677.20.320.0917.31123011230602024051117
6강남구대치2동개포2 [기타]1917.10.330.0716.91123011230792024051117
7강남구대치4동청담 [기타]1687.20.350.0717.91123011230632024051117
8강남구도곡1동개포4 [기타]1937.10.360.0517.01123011230662024051117
9강남구도곡2동논현동(논현초교) [학교]1857.20.290.0518.61123011230672024051117
구명동명수질측정지점명전기전도도PH잔류염소탁도수온구코드동코드측정시간
2916중랑구면목7동면목4동 [학교]1907.00.20.0617.41107011070572024051111
2917중랑구면목본동면목5동 [기타]1827.10.20.0616.31107011070712024051111
2918중랑구묵1동중화1동 [기타]1786.90.180.0616.01107011070642024051111
2919중랑구묵2동중화2동 [기타]1927.00.160.0416.31107011070652024051111
2920중랑구상봉1동면목동(사가정공원) [공원]1937.10.180.0516.01107011070592024051111
2921중랑구상봉2동면목본동 [기타]1797.00.210.0916.11107011070602024051111
2922중랑구신내1동망우동(중랑캠핑숲) [공원]1887.20.180.0516.41107011070692024051111
2923중랑구신내2동망우3동 [기타]1886.90.170.0716.91107011070702024051111
2924중랑구중화1동상봉동(상봉초교) [학교]1567.10.20.0417.51107011070612024051111
2925중랑구중화2동상봉2동 [기타]1897.10.20.0416.81107011070622024051111