Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells87
Missing cells (%)0.6%
Duplicate rows2018
Duplicate rows (%)14.5%
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
2024-05-12T15:32:59.570693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T15:33:00.000218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 2018 (14.5%) duplicate rowsDuplicates

Reproduction

Analysis started2024-05-12 19:32:56.825104
Analysis finished2024-05-12 19:32:59.465356
Duration2.64 seconds
MissingQ_Station_NA_21237010_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

Distinct7341
Distinct (%)53.2%
Missing87
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.019558472
Minimum-11532
Maximum10879
Zeros4
Zeros (%)< 0.1%
Memory size216.9 KiB
2024-05-12T15:33:00.742237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-11532
5-th percentile-1422.2
Q1-424.4
median-3
Q3396.3
95-th percentile1478.8
Maximum10879
Range22411
Interquartile range (IQR)820.7

Descriptive statistics

Standard deviation960.83516
Coefficient of variation (CV)49126.29
Kurtosis9.843226
Mean0.019558472
Median Absolute Deviation (MAD)411
Skewness0.33367753
Sum269.77
Variance923204.2
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2024-05-12T15:33:01.348920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T15:33:02.976017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps10
min6 days
max2 weeks and 4 days
mean1 week, 2 days and 7 hours
std4 days, 8 hours and 56 minutes
2024-05-12T15:33:03.260267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
-2 17
 
0.1%
208 16
 
0.1%
124 15
 
0.1%
-167 15
 
0.1%
22 14
 
0.1%
-182 14
 
0.1%
51 14
 
0.1%
165 13
 
0.1%
-24 13
 
0.1%
61 13
 
0.1%
Other values (7331) 13649
98.3%
(Missing) 87
 
0.6%
ValueCountFrequency (%)
-11532 1
< 0.1%
-8506 1
< 0.1%
-6810 1
< 0.1%
-6525 1
< 0.1%
-6134 1
< 0.1%
-5775 1
< 0.1%
-5770.3 1
< 0.1%
-5421 1
< 0.1%
-5263 1
< 0.1%
-5176 1
< 0.1%
ValueCountFrequency (%)
10879 1
< 0.1%
8597 1
< 0.1%
7883 1
< 0.1%
7303 1
< 0.1%
7248 1
< 0.1%
7201 1
< 0.1%
6924 1
< 0.1%
6842 1
< 0.1%
6706 1
< 0.1%
6621 1
< 0.1%
2024-05-12T15:33:02.281638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T15:32:58.914630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T15:32:59.227273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T15:32:59.390063image/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
1983-01-05-1196.0
1983-01-06635.0
1983-01-07313.0
1983-01-08308.0
1983-01-09-1117.0
1983-01-101120.0
Flow
Date
2020-12-22106.91
2020-12-23-559.80
2020-12-24507.20
2020-12-25102.10
2020-12-26-549.30
2020-12-27266.40
2020-12-28515.10
2020-12-29-747.90
2020-12-3013.70
2020-12-31422.70

Duplicate rows

Most frequently occurring

Flow# duplicates
2017NaN87
1025-2.017
1239208.016
855-167.015
1154124.015
840-182.014
105022.014
108051.014
678-347.013
886-136.013