Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells1923
Missing cells (%)13.9%
Duplicate rows2681
Duplicate rows (%)19.3%
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
2024-05-12T14:18:53.828885image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T14:18:54.261083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 2681 (19.3%) duplicate rowsDuplicates
Flow has 1923 (13.9%) missing valuesMissing
Flow is non stationaryNon stationary
Flow is seasonalSeasonal

Reproduction

Analysis started2024-05-12 18:18:51.895446
Analysis finished2024-05-12 18:18:53.722064
Duration1.83 second
MissingQ_Station_NA_25027930_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

MISSING  NON STATIONARY  SEASONAL 

Distinct4769
Distinct (%)39.9%
Missing1923
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean4971.9662
Minimum1293
Maximum8306
Zeros0
Zeros (%)0.0%
Memory size216.9 KiB
2024-05-12T14:18:55.109775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1293
5-th percentile2472
Q13985
median4998
Q36040.3
95-th percentile7245.4
Maximum8306
Range7013
Interquartile range (IQR)2055.3

Descriptive statistics

Standard deviation1432.151
Coefficient of variation (CV)0.28804519
Kurtosis-0.56514577
Mean4971.9662
Median Absolute Deviation (MAD)1027
Skewness-0.11557169
Sum59449800
Variance2051056.4
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.385401197 × 10-19
2024-05-12T14:18:55.770962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T14:18:58.256508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps27
min3 days
max2 years, 34 weeks and 4 days
mean10 weeks, 2 days and 1 hour
std26 weeks, 16 hours and 11 minutes
2024-05-12T14:18:58.547866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
5342 17
 
0.1%
4533 15
 
0.1%
6077 15
 
0.1%
4998 15
 
0.1%
6857 15
 
0.1%
5750 14
 
0.1%
7519 13
 
0.1%
5027 13
 
0.1%
4220 13
 
0.1%
4720 13
 
0.1%
Other values (4759) 11814
85.1%
(Missing) 1923
 
13.9%
ValueCountFrequency (%)
1293 1
< 0.1%
1306 1
< 0.1%
1331 1
< 0.1%
1340 1
< 0.1%
1359 1
< 0.1%
1362 2
< 0.1%
1371 1
< 0.1%
1373 1
< 0.1%
1375 1
< 0.1%
1379 1
< 0.1%
ValueCountFrequency (%)
8306 1
 
< 0.1%
8299 1
 
< 0.1%
8291 3
< 0.1%
8284 3
< 0.1%
8276 1
 
< 0.1%
8268 1
 
< 0.1%
8261 3
< 0.1%
8253 1
 
< 0.1%
8246 3
< 0.1%
8238 1
 
< 0.1%
2024-05-12T14:18:57.667995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T14:18:53.067892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T14:18:53.443428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T14:18:53.632171image/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
1983-01-014629.0
1983-01-024582.0
1983-01-034526.0
1983-01-044411.0
1983-01-054333.0
1983-01-064259.0
1983-01-074157.0
1983-01-084087.0
1983-01-094003.0
1983-01-103946.0
Flow
Date
2020-12-226654.9
2020-12-236602.7
2020-12-246545.6
2020-12-256399.2
2020-12-266331.8
2020-12-276263.8
2020-12-286225.9
2020-12-296180.6
2020-12-306161.3
2020-12-316127.6

Duplicate rows

Most frequently occurring

Flow# duplicates
2680NaN1923
15895342.017
10874533.015
13714998.015
20156077.015
24286857.015
18365750.014
8684220.013
11984720.013
13855027.013