Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells1267
Missing cells (%)9.1%
Duplicate rows1196
Duplicate rows (%)8.6%
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
2024-05-12T15:34:05.559464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T15:34:05.983721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 1196 (8.6%) duplicate rowsDuplicates
Flow has 1267 (9.1%) missing valuesMissing

Reproduction

Analysis started2024-05-12 19:34:02.924919
Analysis finished2024-05-12 19:34:05.457855
Duration2.53 seconds
MissingQ_Station_NA_23187280_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

MISSING 

Distinct3156
Distinct (%)25.0%
Missing1267
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean-0.072504559
Minimum-2207
Maximum2712
Zeros65
Zeros (%)0.5%
Memory size216.9 KiB
2024-05-12T15:34:06.745901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2207
5-th percentile-428.4
Q1-130
median1
Q3140.7
95-th percentile414
Maximum2712
Range4919
Interquartile range (IQR)270.7

Descriptive statistics

Standard deviation261.2597
Coefficient of variation (CV)-3603.3555
Kurtosis4.0382475
Mean-0.072504559
Median Absolute Deviation (MAD)135
Skewness-0.16846052
Sum-914.5
Variance68256.632
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2024-05-12T15:34:07.367174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T15:34:08.761869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps41
min4 days
max22 weeks
mean4 weeks, 3 days and 20 hours
std5 weeks, 4 days and 2 hours
2024-05-12T15:34:09.232073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 65
 
0.5%
-8 36
 
0.3%
-36 36
 
0.3%
9 36
 
0.3%
-22 33
 
0.2%
-32 33
 
0.2%
20 33
 
0.2%
1 32
 
0.2%
-109 32
 
0.2%
-72 32
 
0.2%
Other values (3146) 12245
88.2%
(Missing) 1267
 
9.1%
ValueCountFrequency (%)
-2207 1
< 0.1%
-2068 1
< 0.1%
-1747 1
< 0.1%
-1603 1
< 0.1%
-1578 1
< 0.1%
-1541 1
< 0.1%
-1414 1
< 0.1%
-1335 1
< 0.1%
-1302 1
< 0.1%
-1291 1
< 0.1%
ValueCountFrequency (%)
2712 1
< 0.1%
1574 1
< 0.1%
1516 1
< 0.1%
1354 1
< 0.1%
1261 1
< 0.1%
1235 1
< 0.1%
1234 1
< 0.1%
1204 1
< 0.1%
1144 1
< 0.1%
1138 1
< 0.1%
2024-05-12T15:34:07.909902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T15:34:04.849454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T15:34:05.172188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T15:34:05.352348image/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
1983-01-0345.0
1983-01-04184.0
1983-01-05-154.0
1983-01-06-347.0
1983-01-07450.0
1983-01-08455.0
1983-01-09-617.0
1983-01-10-237.0
Flow
Date
2020-12-22-6.0
2020-12-23144.8
2020-12-24-272.1
2020-12-2570.0
2020-12-26191.3
2020-12-2726.5
2020-12-28-233.1
2020-12-29339.5
2020-12-30-294.7
2020-12-31-108.4

Duplicate rows

Most frequently occurring

Flow# duplicates
1195NaN1267
6030.065
562-36.036
593-8.036
6149.036
566-32.033
576-22.033
62620.033
474-109.032
514-72.032