Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells426
Missing cells (%)3.1%
Duplicate rows927
Duplicate rows (%)6.7%
Total size in memory216.9 KiB
Average record size in memory16.0 B

Variable types

TimeSeries1

Timeseries statistics

Number of series1
Time series length13880
Starting point1983-01-01 00:00:00
Ending point2020-12-31 00:00:00
Period1 day
2024-05-12T15:34:29.852951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T15:34:30.392838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 927 (6.7%) duplicate rowsDuplicates
Flow has 426 (3.1%) missing valuesMissing

Reproduction

Analysis started2024-05-12 19:34:27.110346
Analysis finished2024-05-12 19:34:29.751566
Duration2.64 seconds
MissingQ_Station_NA_27037010_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

MISSING 

Distinct5603
Distinct (%)41.6%
Missing426
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean0.046881968
Minimum-2373
Maximum2485
Zeros70
Zeros (%)0.5%
Memory size216.9 KiB
2024-05-12T15:34:31.114715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2373
5-th percentile-324.105
Q1-89
median-0.6
Q389.225
95-th percentile323.48
Maximum2485
Range4858
Interquartile range (IQR)178.225

Descriptive statistics

Standard deviation224.18552
Coefficient of variation (CV)4781.9135
Kurtosis11.368365
Mean0.046881968
Median Absolute Deviation (MAD)89.4
Skewness0.13256486
Sum630.75
Variance50259.145
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2024-05-12T15:34:31.675723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T15:34:33.110564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps35
min6 days
max7 weeks and 3 days
mean1 week, 6 days and 1 hour
std1 week, 3 days and 17 hours
2024-05-12T15:34:33.430659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 70
 
0.5%
1 50
 
0.4%
-25 49
 
0.4%
-7 48
 
0.3%
-4 48
 
0.3%
-5 47
 
0.3%
-2 46
 
0.3%
5 45
 
0.3%
10 44
 
0.3%
-15 44
 
0.3%
Other values (5593) 12963
93.4%
(Missing) 426
 
3.1%
ValueCountFrequency (%)
-2373 1
< 0.1%
-1994 1
< 0.1%
-1709 1
< 0.1%
-1615 1
< 0.1%
-1580 1
< 0.1%
-1539 1
< 0.1%
-1514 1
< 0.1%
-1448.6 1
< 0.1%
-1443.9 1
< 0.1%
-1439 1
< 0.1%
ValueCountFrequency (%)
2485 1
< 0.1%
2138 1
< 0.1%
2069.1 1
< 0.1%
2006 1
< 0.1%
1912 1
< 0.1%
1670 1
< 0.1%
1668 1
< 0.1%
1566.9 1
< 0.1%
1482 1
< 0.1%
1453.9 1
< 0.1%
2024-05-12T15:34:32.540965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T15:34:29.054283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T15:34:29.391958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T15:34:29.659070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Flow
Date
1983-01-01NaN
1983-01-02NaN
1983-01-03NaN
1983-01-04NaN
1983-01-05111.0
1983-01-06-11.0
1983-01-074.0
1983-01-08-91.0
1983-01-09197.0
1983-01-10-372.0
Flow
Date
2020-12-22118.80
2020-12-23-132.92
2020-12-24136.34
2020-12-25-125.03
2020-12-262.41
2020-12-27-31.99
2020-12-2865.64
2020-12-29-33.31
2020-12-3010.23
2020-12-31-71.74

Duplicate rows

Most frequently occurring

Flow# duplicates
926NaN426
4500.070
4511.050
416-25.049
442-7.048
445-4.048
444-5.047
448-2.046
4565.045
430-15.044