Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells3021
Missing cells (%)21.8%
Duplicate rows885
Duplicate rows (%)6.4%
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
2024-05-12T15:34:16.415516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T15:34:16.687027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 885 (6.4%) duplicate rowsDuplicates
Flow has 3021 (21.8%) missing valuesMissing

Reproduction

Analysis started2024-05-12 19:34:14.685849
Analysis finished2024-05-12 19:34:16.238484
Duration1.55 second
MissingQ_Station_NA_25027410_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

MISSING 

Distinct2478
Distinct (%)22.8%
Missing3021
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean0.29206188
Minimum-3231
Maximum2600
Zeros67
Zeros (%)0.5%
Memory size216.9 KiB
2024-05-12T15:34:17.271409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-3231
5-th percentile-322
Q1-92
median2
Q399
95-th percentile313
Maximum2600
Range5831
Interquartile range (IQR)191

Descriptive statistics

Standard deviation223.50477
Coefficient of variation (CV)765.26512
Kurtosis22.749703
Mean0.29206188
Median Absolute Deviation (MAD)96
Skewness-0.57326012
Sum3171.5
Variance49954.383
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2024-05-12T15:34:17.870074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T15:34:18.969069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps77
min5 days
max1 year, 5 weeks and 1 day
mean5 weeks, 4 days and 19 hours
std8 weeks, 2 days and 8 minutes
2024-05-12T15:34:19.632739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 67
 
0.5%
-6 42
 
0.3%
-1 40
 
0.3%
22 40
 
0.3%
7 38
 
0.3%
-8 36
 
0.3%
-33 35
 
0.3%
-36 35
 
0.3%
31 35
 
0.3%
-20 35
 
0.3%
Other values (2468) 10456
75.3%
(Missing) 3021
 
21.8%
ValueCountFrequency (%)
-3231 1
< 0.1%
-2806 1
< 0.1%
-2669 1
< 0.1%
-2468 1
< 0.1%
-2437 1
< 0.1%
-2311 1
< 0.1%
-2014 1
< 0.1%
-1796 1
< 0.1%
-1648 1
< 0.1%
-1583 1
< 0.1%
ValueCountFrequency (%)
2600 1
< 0.1%
2519 1
< 0.1%
2331 1
< 0.1%
2182 1
< 0.1%
1790 1
< 0.1%
1586 1
< 0.1%
1501 1
< 0.1%
1498 1
< 0.1%
1426 1
< 0.1%
1418 1
< 0.1%
2024-05-12T15:34:18.398890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T15:34:15.774365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T15:34:16.009084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T15:34:16.188373image/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
1983-01-0436.0
1983-01-05100.0
1983-01-06-456.0
1983-01-0736.0
1983-01-08601.0
1983-01-09-68.0
1983-01-10-686.0
Flow
Date
2020-12-22NaN
2020-12-23NaN
2020-12-24NaN
2020-12-25NaN
2020-12-26NaN
2020-12-27NaN
2020-12-28NaN
2020-12-29NaN
2020-12-30NaN
2020-12-31NaN

Duplicate rows

Most frequently occurring

Flow# duplicates
884NaN3021
4450.067
437-6.042
444-1.040
47122.040
4547.038
435-8.036
404-36.035
407-33.035
421-20.035