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Drop rows where timestamps are older than subsequent row


Drop rows where timestamps are older than subsequent row

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Drop rows where timestamps are older than subsequent row
Tag : python , By : Pitmairen
Date : November 28 2020, 12:01 PM

I wish did fix the issue. I'm trying to drop rows where timestamps are older or later than subsequent rows. I'm not sure if I'm overthinking this but my current attempt below. , This should do it:
def myfunc(t,l):
    try:
        return not (t > min(l))
    except:
        return True

df[df.apply(lambda x: myfunc(x.Time,df.Time.iloc[x.name+1:len(df)].to_list()),axis=1)]

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How do I get subsequent timestamps accurate upto 5 micro seconds in android


Tag : android , By : PepeM
Date : November 01 2020, 01:03 PM
I wish did fix the issue. Below is sample code I am using: , Based on the comments you might be asking for this:
public void onSensorChanged(SensorEvent event) {
  long systemTimeStamp= SystemClock.elapsedRealtimeNanos();
  double timeDifference = ((double) (systemTimeStamp - prevTime)) / 1000000d;    //msec        
  Log.e("No PROBLEM",timeDifference);
  prevTime=systemTimeStamp;
}

Keep the most recent values and drop older rows (pandas)


Tag : python , By : doctorbigtime
Date : March 29 2020, 07:55 AM
will help you I think you can use this solution with to_timedelta, if need filter by max value of column Time:
df.Time = pd.to_timedelta(df.Time)
df = df[df.Time == df.Time.max()]
print (df)
   ID Name     Time Comment
0   0  Foo 12:17:37    Rand
1   1  Foo 12:17:37   Rand1
df = df.groupby('Name', sort=False)
       .apply(lambda x: x[x.Time == x.Time.max()])
       .reset_index(drop=True)
print (df)
   ID Name     Time Comment
0   0  Foo 12:17:37    Rand
1   1  Foo 12:17:37   Rand1
2   4  Bar 09:01:00   Rand4
3   5  Bar 09:01:00   Rand5

Drop duplicate rows from a pandas DataFrame whose timestamps are within a specified range or duration


Tag : python , By : James B
Date : March 29 2020, 07:55 AM
it fixes the issue Use duplicated + diff in conjunction with groupby to figure out what rows you want to remove.
c = ['Subject', 'Verb', 'Object']

def f(x):
    return x[c].duplicated() & x.Date.diff().dt.days.lt(5)

df = df.sort_values(c)
df[~df.groupby(c).apply(f).values]

  Subject     Verb Object       Date
0    Bill      Ate   Food 2015-07-11
1   Steve  Painted  House 2011-08-12
3   Steve  Painted  House 2011-08-25

Influxdb - subtracting data from two subsequent timestamps


Tag : development , By : Brownell
Date : March 29 2020, 07:55 AM
wish helps you There are no joins in influxdb, but you can use difference(), derivative(), non_negative_derivative() aggregate functions in query.
> INSERT myMetric value1=1
> INSERT myMetric value1=10
> select value1 from myMetric
name: myMetric
time                value1
----                ------
1526032578114702408 1
1526034352621423990 10
> select difference(value1) from myMetric
name: myMetric
time                difference
----                ----------
1526034352621423990 9
> select derivative(value1,1s) from myMetric
name: myMetric
time                derivative
----                ----------
1526034352621423990 0.005071832014237941

Combine several rows with timestamps into one row with start and end timestamps


Tag : mysql , By : cjdavis
Date : March 29 2020, 07:55 AM
it fixes the issue I'm trying to pull some SQL reports from an inventory system backend. , MySql 8.0 using row_number() window function to match out/in
select usr,asset
  , max(case event when 'out' then ts end) out_time
  , max(case event when 'in' then ts end) in_time
from (
  select f.*,
      -- first 'out' matches first 'in' and so on
      row_number() over(partition by usr, asset, event order by ts) as grp
  from foo f
  ) t
group by usr, asset, grp
order by out_time 
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