Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 KiB
Average record size in memory90.3 B

Variable types

Numeric3
Categorical6
DateTime1

Alerts

노드 아이디 has constant value ""Constant
co 측정값 has constant value ""Constant
온도 has constant value ""Constant
시퀀스 is highly overall correlated with 습도 and 3 other fieldsHigh correlation
co2 측정값 is highly overall correlated with PM1.0 측정값 and 2 other fieldsHigh correlation
습도 is highly overall correlated with 시퀀스 and 3 other fieldsHigh correlation
PM1.0 측정값 is highly overall correlated with 시퀀스 and 3 other fieldsHigh correlation
PM2.5 측정값 is highly overall correlated with 시퀀스 and 4 other fieldsHigh correlation
PM10 측정값 is highly overall correlated with 시퀀스 and 3 other fieldsHigh correlation
시퀀스 has unique valuesUnique
기록 시간 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:31:51.317127
Analysis finished2023-12-10 13:31:53.579380
Duration2.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시퀀스
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8381.39
Minimum175
Maximum16545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:31:53.676403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum175
5-th percentile1012.4
Q14279.5
median8384.5
Q312461
95-th percentile15726.3
Maximum16545
Range16370
Interquartile range (IQR)8181.5

Descriptive statistics

Standard deviation4784.4873
Coefficient of variation (CV)0.57084652
Kurtosis-1.1997921
Mean8381.39
Median Absolute Deviation (MAD)4131
Skewness-0.00055132535
Sum838139
Variance22891319
MonotonicityStrictly increasing
2023-12-10T22:31:53.898144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175 1
 
1.0%
10773 1
 
1.0%
12420 1
 
1.0%
12256 1
 
1.0%
12091 1
 
1.0%
11928 1
 
1.0%
11762 1
 
1.0%
11597 1
 
1.0%
11431 1
 
1.0%
11267 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
175 1
1.0%
392 1
1.0%
551 1
1.0%
717 1
1.0%
887 1
1.0%
1019 1
1.0%
1206 1
1.0%
1382 1
1.0%
1542 1
1.0%
1706 1
1.0%
ValueCountFrequency (%)
16545 1
1.0%
16377 1
1.0%
16215 1
1.0%
16047 1
1.0%
15884 1
1.0%
15718 1
1.0%
15555 1
1.0%
15388 1
1.0%
15225 1
1.0%
15062 1
1.0%

노드 아이디
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
201101
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201101
2nd row201101
3rd row201101
4th row201101
5th row201101

Common Values

ValueCountFrequency (%)
201101 100
100.0%

Length

2023-12-10T22:31:54.103685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:31:54.216591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201101 100
100.0%

co 측정값
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.3000000119
100 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.3000000119
2nd row0.3000000119
3rd row0.3000000119
4th row0.3000000119
5th row0.3000000119

Common Values

ValueCountFrequency (%)
0.3000000119 100
100.0%

Length

2023-12-10T22:31:54.319243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:31:54.419547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.3000000119 100
100.0%

co2 측정값
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean435.87
Minimum426
Maximum442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:31:54.533876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum426
5-th percentile431
Q1434
median436
Q3438
95-th percentile442
Maximum442
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0741982
Coefficient of variation (CV)0.0093472783
Kurtosis-0.64003351
Mean435.87
Median Absolute Deviation (MAD)2
Skewness-0.017662655
Sum43587
Variance16.599091
MonotonicityNot monotonic
2023-12-10T22:31:54.690394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
434 20
20.0%
442 20
20.0%
436 19
19.0%
431 19
19.0%
438 18
18.0%
426 2
 
2.0%
429 2
 
2.0%
ValueCountFrequency (%)
426 2
 
2.0%
429 2
 
2.0%
431 19
19.0%
434 20
20.0%
436 19
19.0%
438 18
18.0%
442 20
20.0%
ValueCountFrequency (%)
442 20
20.0%
438 18
18.0%
436 19
19.0%
434 20
20.0%
431 19
19.0%
429 2
 
2.0%
426 2
 
2.0%

PM1.0 측정값
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
80 
4
20 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 80
80.0%
4 20
 
20.0%

Length

2023-12-10T22:31:54.847361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:31:55.040148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 80
80.0%
4 20
 
20.0%

PM2.5 측정값
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
5
41 
4
39 
3
20 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
5 41
41.0%
4 39
39.0%
3 20
20.0%

Length

2023-12-10T22:31:55.199049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:31:55.358801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 41
41.0%
4 39
39.0%
3 20
20.0%

PM10 측정값
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
5
59 
4
39 
6
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 59
59.0%
4 39
39.0%
6 2
 
2.0%

Length

2023-12-10T22:31:55.518365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:31:55.678734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 59
59.0%
4 39
39.0%
6 2
 
2.0%

온도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
26.200000763
100 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26.200000763
2nd row26.200000763
3rd row26.200000763
4th row26.200000763
5th row26.200000763

Common Values

ValueCountFrequency (%)
26.200000763 100
100.0%

Length

2023-12-10T22:31:55.853385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:31:55.991553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26.200000763 100
100.0%

습도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.943
Minimum33.900002
Maximum37.700001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:31:56.122331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.900002
5-th percentile34.099998
Q134.400002
median35.200001
Q335.299999
95-th percentile35.5
Maximum37.700001
Range3.7999992
Interquartile range (IQR)0.89999771

Descriptive statistics

Standard deviation0.68479235
Coefficient of variation (CV)0.019597411
Kurtosis3.3243223
Mean34.943
Median Absolute Deviation (MAD)0.29999924
Skewness0.95938083
Sum3494.3
Variance0.46894056
MonotonicityNot monotonic
2023-12-10T22:31:56.311114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
35.5 20
20.0%
35.299999237 20
20.0%
34.400001526 19
19.0%
34.099998474 19
19.0%
35.200000763 18
18.0%
37.700000763 2
 
2.0%
33.900001526 2
 
2.0%
ValueCountFrequency (%)
33.900001526 2
 
2.0%
34.099998474 19
19.0%
34.400001526 19
19.0%
35.200000763 18
18.0%
35.299999237 20
20.0%
35.5 20
20.0%
37.700000763 2
 
2.0%
ValueCountFrequency (%)
37.700000763 2
 
2.0%
35.5 20
20.0%
35.299999237 20
20.0%
35.200000763 18
18.0%
34.400001526 19
19.0%
34.099998474 19
19.0%
33.900001526 2
 
2.0%

기록 시간
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2022-06-01 00:00:01
Maximum2022-06-01 00:03:21
2023-12-10T22:31:56.548078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:56.831695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-10T22:31:52.726776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:51.752162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:52.257254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:52.865419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:51.877202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:52.409038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:53.050795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:52.090268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:31:52.572100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:31:57.006588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시퀀스co2 측정값PM1.0 측정값PM2.5 측정값PM10 측정값습도기록 시간
시퀀스1.0000.9481.0000.9320.7620.9851.000
co2 측정값0.9481.0001.0001.0001.0001.0001.000
PM1.0 측정값1.0001.0001.0000.3710.2430.5101.000
PM2.5 측정값0.9321.0000.3711.0000.9420.8151.000
PM10 측정값0.7621.0000.2430.9421.0000.8331.000
습도0.9851.0000.5100.8150.8331.0001.000
기록 시간1.0001.0001.0001.0001.0001.0001.000
2023-12-10T22:31:57.176362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PM1.0 측정값PM10 측정값PM2.5 측정값
PM1.0 측정값1.0000.3940.586
PM10 측정값0.3941.0000.706
PM2.5 측정값0.5860.7061.000
2023-12-10T22:31:57.326286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시퀀스co2 측정값습도PM1.0 측정값PM2.5 측정값PM10 측정값
시퀀스1.000-0.242-0.8990.9580.8610.645
co2 측정값-0.2421.0000.3320.9740.9790.979
습도-0.8990.3321.0000.6090.8310.857
PM1.0 측정값0.9580.9740.6091.0000.5860.394
PM2.5 측정값0.8610.9790.8310.5861.0000.706
PM10 측정값0.6450.9790.8570.3940.7061.000

Missing values

2023-12-10T22:31:53.265278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:31:53.493854image/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

시퀀스노드 아이디co 측정값co2 측정값PM1.0 측정값PM2.5 측정값PM10 측정값온도습도기록 시간
01752011010.342635626.20000137.7000012022-06-01 00:00:01
13922011010.342635626.20000137.7000012022-06-01 00:00:03
25512011010.343433526.20000135.52022-06-01 00:00:05
37172011010.343433526.20000135.52022-06-01 00:00:07
48872011010.343433526.20000135.52022-06-01 00:00:09
510192011010.343433526.20000135.52022-06-01 00:00:11
612062011010.343433526.20000135.52022-06-01 00:00:13
713822011010.343433526.20000135.52022-06-01 00:00:15
815422011010.343433526.20000135.52022-06-01 00:00:17
917062011010.343433526.20000135.52022-06-01 00:00:19
시퀀스노드 아이디co 측정값co2 측정값PM1.0 측정값PM2.5 측정값PM10 측정값온도습도기록 시간
90150622011010.343135526.20000134.0999982022-06-01 00:03:03
91152252011010.343135526.20000134.0999982022-06-01 00:03:04
92153882011010.343135526.20000134.0999982022-06-01 00:03:07
93155552011010.343135526.20000134.0999982022-06-01 00:03:08
94157182011010.343135526.20000134.0999982022-06-01 00:03:11
95158842011010.343135526.20000134.0999982022-06-01 00:03:13
96160472011010.343135526.20000134.0999982022-06-01 00:03:15
97162152011010.343135526.20000134.0999982022-06-01 00:03:17
98163772011010.342934426.20000133.9000022022-06-01 00:03:19
99165452011010.342934426.20000133.9000022022-06-01 00:03:21