Overview

Dataset statistics

Number of variables1
Number of observations13880
Missing cells123
Missing cells (%)0.9%
Duplicate rows1165
Duplicate rows (%)8.4%
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
2024-05-12T15:34:39.729000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-12T15:34:40.140109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Alerts

Dataset has 1165 (8.4%) duplicate rowsDuplicates

Reproduction

Analysis started2024-05-12 19:34:36.609712
Analysis finished2024-05-12 19:34:39.626741
Duration3.02 seconds
MissingQ_Station_NA_26207080_ok_Missing.csv
Download configurationconfig.json

Variables

Flow
Numeric time series

Distinct5210
Distinct (%)37.9%
Missing123
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.065457585
Minimum-1701
Maximum1345
Zeros74
Zeros (%)0.5%
Memory size216.9 KiB
2024-05-12T15:34:40.903339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1701
5-th percentile-309.02
Q1-47
median8
Q367.65
95-th percentile258
Maximum1345
Range3046
Interquartile range (IQR)114.65

Descriptive statistics

Standard deviation184.06181
Coefficient of variation (CV)2811.9248
Kurtosis9.1999538
Mean0.065457585
Median Absolute Deviation (MAD)57
Skewness-0.88076609
Sum900.5
Variance33878.748
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2024-05-12T15:34:41.438574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-12T15:34:42.753703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Gap statistics

number of gaps21
min4 days
max4 weeks and 1 day
mean6 days, 17 hours and 8 minutes
std5 days, 20 hours and 10 minutes
2024-05-12T15:34:43.203180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 74
 
0.5%
8 63
 
0.5%
12 63
 
0.5%
-5 60
 
0.4%
3 60
 
0.4%
-4 59
 
0.4%
10 57
 
0.4%
9 57
 
0.4%
21 57
 
0.4%
-11 56
 
0.4%
Other values (5200) 13151
94.7%
(Missing) 123
 
0.9%
ValueCountFrequency (%)
-1701 1
< 0.1%
-1666 1
< 0.1%
-1531 1
< 0.1%
-1482.4 1
< 0.1%
-1451 1
< 0.1%
-1398 1
< 0.1%
-1372 1
< 0.1%
-1362 1
< 0.1%
-1336 1
< 0.1%
-1316 1
< 0.1%
ValueCountFrequency (%)
1345 1
< 0.1%
1272 1
< 0.1%
1266.6 1
< 0.1%
1194.8 1
< 0.1%
1120.5 1
< 0.1%
1118 1
< 0.1%
1107 1
< 0.1%
1093 1
< 0.1%
1059 2
< 0.1%
1030 1
< 0.1%
2024-05-12T15:34:41.889344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-12T15:34:39.013226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-12T15:34:39.335601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T15:34:39.528710image/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
1983-01-0355.0
1983-01-04-29.0
1983-01-05-15.0
1983-01-0622.0
1983-01-0763.0
1983-01-08-45.0
1983-01-09-10.0
1983-01-101.0
Flow
Date
2020-12-22-42.17
2020-12-234.09
2020-12-2456.27
2020-12-25127.31
2020-12-26121.58
2020-12-27-275.18
2020-12-28178.70
2020-12-29-143.50
2020-12-30-79.60
2020-12-31NaN

Duplicate rows

Most frequently occurring

Flow# duplicates
1164NaN123
5430.074
6128.063
63612.063
502-5.060
5693.060
506-4.059
6189.057
62310.057
69021.057