Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells5436
Missing cells (%)39.2%
Duplicate rows1117
Duplicate rows (%)8.0%
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
2024-05-12T15:33:54.946601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T15:33:55.456493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 1117 (8.0%) duplicate rowsDuplicates
Flow has 5436 (39.2%) missing valuesMissing

Reproduction

Analysis started2024-05-12 19:33:53.492024
Analysis finished2024-05-12 19:33:54.842524
Duration1.35 second
MissingQ_Station_NA_23167010_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

MISSING 

Distinct2987
Distinct (%)35.4%
Missing5436
Missing (%)39.2%
Infinite0
Infinite (%)0.0%
Mean1.8675959
Minimum-2634
Maximum2652
Zeros100
Zeros (%)0.7%
Memory size216.9 KiB
2024-05-12T15:33:56.166584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2634
5-th percentile-465
Q1-172
median-13
Q3160.475
95-th percentile516
Maximum2652
Range5286
Interquartile range (IQR)332.475

Descriptive statistics

Standard deviation305.58573
Coefficient of variation (CV)163.62519
Kurtosis4.0397427
Mean1.8675959
Median Absolute Deviation (MAD)166
Skewness0.39097977
Sum15769.98
Variance93382.64
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2024-05-12T15:33:56.796501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T15:33:59.403090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps102
min4 days
max5 years, 9 weeks and 6 days
mean7 weeks, 3 days and 15 hours
std29 weeks, 3 days and 17 hours
2024-05-12T15:33:59.979511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 100
 
0.7%
-75 21
 
0.2%
-18 20
 
0.1%
-90 19
 
0.1%
115 19
 
0.1%
18 19
 
0.1%
-5 19
 
0.1%
-49 18
 
0.1%
-20 18
 
0.1%
-103 18
 
0.1%
Other values (2977) 8173
58.9%
(Missing) 5436
39.2%
ValueCountFrequency (%)
-2634 1
< 0.1%
-1674 1
< 0.1%
-1615 1
< 0.1%
-1584 1
< 0.1%
-1551 1
< 0.1%
-1524 1
< 0.1%
-1435 1
< 0.1%
-1358 1
< 0.1%
-1328 1
< 0.1%
-1293 1
< 0.1%
ValueCountFrequency (%)
2652 1
< 0.1%
2511 1
< 0.1%
1795 1
< 0.1%
1783 1
< 0.1%
1536 1
< 0.1%
1534 1
< 0.1%
1533 1
< 0.1%
1484 1
< 0.1%
1467 1
< 0.1%
1427 1
< 0.1%
2024-05-12T15:33:58.671643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T15:33:54.218386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T15:33:54.558857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T15:33:54.751437image/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
1983-01-05NaN
1983-01-06NaN
1983-01-07NaN
1983-01-08NaN
1983-01-09NaN
1983-01-10NaN
Flow
Date
2020-12-22201.0
2020-12-23-235.7
2020-12-24-355.7
2020-12-25149.6
2020-12-26289.8
2020-12-27-187.4
2020-12-28195.4
2020-12-29-97.9
2020-12-30-361.9
2020-12-31133.5

Duplicate rows

Most frequently occurring

Flow# duplicates
1116NaN5436
5540.0100
464-75.021
532-18.020
440-90.019
548-5.019
57618.019
684115.019
427-103.018
492-49.018