Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells123
Missing cells (%)0.9%
Duplicate rows1269
Duplicate rows (%)9.1%
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
2024-05-12T15:32:46.055239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T15:32:46.344314image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 1269 (9.1%) duplicate rowsDuplicates

Reproduction

Analysis started2024-05-12 19:32:43.640407
Analysis finished2024-05-12 19:32:45.955916
Duration2.32 seconds
MissingQ_Station_NA_21137010_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

Distinct6086
Distinct (%)44.2%
Missing123
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean-0.1293138
Minimum-2626
Maximum2389
Zeros33
Zeros (%)0.2%
Memory size216.9 KiB
2024-05-12T15:32:46.933778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2626
5-th percentile-577
Q1-141
median12
Q3153.04
95-th percentile535.016
Maximum2389
Range5015
Interquartile range (IQR)294.04

Descriptive statistics

Standard deviation343.08638
Coefficient of variation (CV)-2653.1304
Kurtosis4.0015368
Mean-0.1293138
Median Absolute Deviation (MAD)147
Skewness-0.37496303
Sum-1778.97
Variance117708.27
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2024-05-12T15:32:47.524347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T15:32:48.911147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps9
min4 days
max9 weeks and 1 day
mean2 weeks, 10 hours and 40 minutes
std2 weeks, 5 days and 4 hours
2024-05-12T15:32:49.343863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
9 39
 
0.3%
-16 36
 
0.3%
22 36
 
0.3%
47 35
 
0.3%
7 34
 
0.2%
1 33
 
0.2%
0 33
 
0.2%
8 33
 
0.2%
10 32
 
0.2%
27 31
 
0.2%
Other values (6076) 13415
96.6%
(Missing) 123
 
0.9%
ValueCountFrequency (%)
-2626 1
< 0.1%
-2255 1
< 0.1%
-2219 1
< 0.1%
-2176 1
< 0.1%
-2090 1
< 0.1%
-1937 1
< 0.1%
-1930 1
< 0.1%
-1818 1
< 0.1%
-1817 1
< 0.1%
-1774.3 1
< 0.1%
ValueCountFrequency (%)
2389 1
< 0.1%
2200 1
< 0.1%
1838 1
< 0.1%
1805 1
< 0.1%
1764.6 1
< 0.1%
1732 1
< 0.1%
1727 1
< 0.1%
1678 1
< 0.1%
1636.6 1
< 0.1%
1591 2
< 0.1%
2024-05-12T15:32:48.064114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T15:32:45.362245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T15:32:45.686399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T15:32:45.872262image/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
1983-01-03158.0
1983-01-04205.0
1983-01-0513.0
1983-01-06-459.0
1983-01-07164.0
1983-01-0865.0
1983-01-09-318.0
1983-01-10175.0
Flow
Date
2020-12-22464.86
2020-12-23-118.72
2020-12-24-774.94
2020-12-25642.83
2020-12-26427.17
2020-12-27-841.66
2020-12-28435.87
2020-12-29140.27
2020-12-30-407.06
2020-12-31-280.31

Duplicate rows

Most frequently occurring

Flow# duplicates
1268NaN123
6469.039
605-16.036
67122.036
70647.035
6437.034
6300.033
6321.033
6448.033
64710.032