Filling the missing data in a timeseries by making an average time series












1














I have an hourly dataseries in pandas for about 10 years. Sometimes the data is missing for 2-3 months. I want to fill the missing periods. The process I thought of it is as follows.




  1. Create one year hourly timeseries from the available data that is
    calculated by average values for each day and hour.

  2. Fill the missing values from this average time series.

  3. For example if 2009/01/28 1:00pm is missing, it will locate 01/28 1:00pm from the timeseries calculated in the first step and fill it.


I tried searching a lot, but I could not accomplish this task.



Any help will be appreciated.



Edit:
This is my attempt so far. I am still testing it. It takes a long time though.



for count in dfaverage.index:
currentday = count.day
currentmonth = count.month
currenthour = count.hour
match_timestamp=('{:02}').format(currentmonth) + '-' + ('{:02}').format(currentday) + ' ' + ('{:02}').format(currenthour)
#print(match_timestamp)
value = df.loc[df.index.strftime('%m-%d %H') == match_timestamp].mean()
dfaverage.loc[count]['value'] = value

for count in df.index:
if math.isnan(df.loc[count]):
currentday = count.day
currentmonth = count.month
currenthour = count.hour
match_timestamp=('{:02}').format(currentmonth) + '-' + ('{:02}').format(currentday) + ' ' + ('{:02}').format(currenthour)
value = dfaverage.loc[dfaverage.index.strftime('%m-%d %H') == match_timestamp].mean()
df.at[count, 'AtmPressurekPa'] = value


Now, I want to iterate through each element of this empty dataseries, find corresponding day, month, and hour values from the main dataframe (df), average it, and assign to this time series.



Later on, I will use the dfaverage timeseries to fill the missing values in the df timeseries.










share|improve this question




















  • 2




    Where's your attempt? Please put the code which you tried.
    – Mayank Porwal
    Nov 12 at 6:50






  • 1




    Hi. Please take the time to read this post on how to provide a great pandas example as well as how to provide a minimal, complete, and verifiable example and revise your question accordingly. These tips on how to ask a good question may also be useful.
    – jezrael
    Nov 12 at 7:39
















1














I have an hourly dataseries in pandas for about 10 years. Sometimes the data is missing for 2-3 months. I want to fill the missing periods. The process I thought of it is as follows.




  1. Create one year hourly timeseries from the available data that is
    calculated by average values for each day and hour.

  2. Fill the missing values from this average time series.

  3. For example if 2009/01/28 1:00pm is missing, it will locate 01/28 1:00pm from the timeseries calculated in the first step and fill it.


I tried searching a lot, but I could not accomplish this task.



Any help will be appreciated.



Edit:
This is my attempt so far. I am still testing it. It takes a long time though.



for count in dfaverage.index:
currentday = count.day
currentmonth = count.month
currenthour = count.hour
match_timestamp=('{:02}').format(currentmonth) + '-' + ('{:02}').format(currentday) + ' ' + ('{:02}').format(currenthour)
#print(match_timestamp)
value = df.loc[df.index.strftime('%m-%d %H') == match_timestamp].mean()
dfaverage.loc[count]['value'] = value

for count in df.index:
if math.isnan(df.loc[count]):
currentday = count.day
currentmonth = count.month
currenthour = count.hour
match_timestamp=('{:02}').format(currentmonth) + '-' + ('{:02}').format(currentday) + ' ' + ('{:02}').format(currenthour)
value = dfaverage.loc[dfaverage.index.strftime('%m-%d %H') == match_timestamp].mean()
df.at[count, 'AtmPressurekPa'] = value


Now, I want to iterate through each element of this empty dataseries, find corresponding day, month, and hour values from the main dataframe (df), average it, and assign to this time series.



Later on, I will use the dfaverage timeseries to fill the missing values in the df timeseries.










share|improve this question




















  • 2




    Where's your attempt? Please put the code which you tried.
    – Mayank Porwal
    Nov 12 at 6:50






  • 1




    Hi. Please take the time to read this post on how to provide a great pandas example as well as how to provide a minimal, complete, and verifiable example and revise your question accordingly. These tips on how to ask a good question may also be useful.
    – jezrael
    Nov 12 at 7:39














1












1








1







I have an hourly dataseries in pandas for about 10 years. Sometimes the data is missing for 2-3 months. I want to fill the missing periods. The process I thought of it is as follows.




  1. Create one year hourly timeseries from the available data that is
    calculated by average values for each day and hour.

  2. Fill the missing values from this average time series.

  3. For example if 2009/01/28 1:00pm is missing, it will locate 01/28 1:00pm from the timeseries calculated in the first step and fill it.


I tried searching a lot, but I could not accomplish this task.



Any help will be appreciated.



Edit:
This is my attempt so far. I am still testing it. It takes a long time though.



for count in dfaverage.index:
currentday = count.day
currentmonth = count.month
currenthour = count.hour
match_timestamp=('{:02}').format(currentmonth) + '-' + ('{:02}').format(currentday) + ' ' + ('{:02}').format(currenthour)
#print(match_timestamp)
value = df.loc[df.index.strftime('%m-%d %H') == match_timestamp].mean()
dfaverage.loc[count]['value'] = value

for count in df.index:
if math.isnan(df.loc[count]):
currentday = count.day
currentmonth = count.month
currenthour = count.hour
match_timestamp=('{:02}').format(currentmonth) + '-' + ('{:02}').format(currentday) + ' ' + ('{:02}').format(currenthour)
value = dfaverage.loc[dfaverage.index.strftime('%m-%d %H') == match_timestamp].mean()
df.at[count, 'AtmPressurekPa'] = value


Now, I want to iterate through each element of this empty dataseries, find corresponding day, month, and hour values from the main dataframe (df), average it, and assign to this time series.



Later on, I will use the dfaverage timeseries to fill the missing values in the df timeseries.










share|improve this question















I have an hourly dataseries in pandas for about 10 years. Sometimes the data is missing for 2-3 months. I want to fill the missing periods. The process I thought of it is as follows.




  1. Create one year hourly timeseries from the available data that is
    calculated by average values for each day and hour.

  2. Fill the missing values from this average time series.

  3. For example if 2009/01/28 1:00pm is missing, it will locate 01/28 1:00pm from the timeseries calculated in the first step and fill it.


I tried searching a lot, but I could not accomplish this task.



Any help will be appreciated.



Edit:
This is my attempt so far. I am still testing it. It takes a long time though.



for count in dfaverage.index:
currentday = count.day
currentmonth = count.month
currenthour = count.hour
match_timestamp=('{:02}').format(currentmonth) + '-' + ('{:02}').format(currentday) + ' ' + ('{:02}').format(currenthour)
#print(match_timestamp)
value = df.loc[df.index.strftime('%m-%d %H') == match_timestamp].mean()
dfaverage.loc[count]['value'] = value

for count in df.index:
if math.isnan(df.loc[count]):
currentday = count.day
currentmonth = count.month
currenthour = count.hour
match_timestamp=('{:02}').format(currentmonth) + '-' + ('{:02}').format(currentday) + ' ' + ('{:02}').format(currenthour)
value = dfaverage.loc[dfaverage.index.strftime('%m-%d %H') == match_timestamp].mean()
df.at[count, 'AtmPressurekPa'] = value


Now, I want to iterate through each element of this empty dataseries, find corresponding day, month, and hour values from the main dataframe (df), average it, and assign to this time series.



Later on, I will use the dfaverage timeseries to fill the missing values in the df timeseries.







python-3.x pandas






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edited Nov 13 at 19:40

























asked Nov 12 at 6:48









Anurag Mishra

166




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  • 2




    Where's your attempt? Please put the code which you tried.
    – Mayank Porwal
    Nov 12 at 6:50






  • 1




    Hi. Please take the time to read this post on how to provide a great pandas example as well as how to provide a minimal, complete, and verifiable example and revise your question accordingly. These tips on how to ask a good question may also be useful.
    – jezrael
    Nov 12 at 7:39














  • 2




    Where's your attempt? Please put the code which you tried.
    – Mayank Porwal
    Nov 12 at 6:50






  • 1




    Hi. Please take the time to read this post on how to provide a great pandas example as well as how to provide a minimal, complete, and verifiable example and revise your question accordingly. These tips on how to ask a good question may also be useful.
    – jezrael
    Nov 12 at 7:39








2




2




Where's your attempt? Please put the code which you tried.
– Mayank Porwal
Nov 12 at 6:50




Where's your attempt? Please put the code which you tried.
– Mayank Porwal
Nov 12 at 6:50




1




1




Hi. Please take the time to read this post on how to provide a great pandas example as well as how to provide a minimal, complete, and verifiable example and revise your question accordingly. These tips on how to ask a good question may also be useful.
– jezrael
Nov 12 at 7:39




Hi. Please take the time to read this post on how to provide a great pandas example as well as how to provide a minimal, complete, and verifiable example and revise your question accordingly. These tips on how to ask a good question may also be useful.
– jezrael
Nov 12 at 7:39

















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