I'm quite new to Python and have a simple question (I think). I have an array with hourly data for an entire month. I want to make a new array with only data at a specific time every day (at 00:00 UTC). How do I do this?
This is what the array looks like:
Thank you for your help!
I would go about it by first making a boolean mask representing which times satisfied that condition, and then selecting values based on that mask. If your data is truly hourly this could be done using the following:
mask = q_era5_feb.time.dt.hour == 0
result = q_era5_feb.sel(time=mask)
Related
I have done an operation subtracting two datetime columns to find out the duration between those two dates. I am very new to python also new to the site, I want to know how to work on dtype('<m8[ns]'), because the resultant output comes out as the following:
ar = pd.to_datetime(['12/31/2015 23:55','1/1/2016 2:47'])
print(ar[1]-ar[0])
this would give the OUT as :
0 days 02:52:00
How do I use this data type to perform operations, such as how do i find out if this data is less than 1 day or one hour?
your help is much appreciated.
I have the following dataframe called df1 that contains data for a number of regions in the column NUTS_ID:
The index, called Date has all the days of 2010. That is, for each code in NUTS_ID I have a day of 2010 (all days of the year for AT1, AT2and so on). I created a list containing the dates corresponding to non-workdays and I want to add a column that with 0 for non-workdays and 1 for workdays.
For this, I simply used a for loop that checks day by day if it's in the workday list I created:
for day in df1.index:
if day not in workdays_list:
df1.loc[day,'Workday'] = 0 # Assigning 0 to to non-workdays
else:
df1.loc[day,'Workday'] = 1 # Assigning 1 to workdays
This works well enough if the dataset is not big. But with some of the datasets I'm processing this takes a very long time. I would like to ask for ideas in order to do the process faster and more efficient. Thank you in advance for your input.
EDIT: One of the things I have thought is that maybe a groupby could be helpful, but I don't know if that is correct.
You can use np.where with isin to check if your Date (i.e. your index) is in the list you created:
import numpy as np
df1['Workday'] = np.where(df1.index.isin(workdays_list),1,0)
I can't reproduce your dataset, but something along those lines should work.
can you please suggest me an easy way to convert time periods to the corresponding indexes?
I have a function that picks entries from data frames based on numerical indexes (from 10th to 20th row) that I can not change. At the same time my data frame has time indexes and I have picked parts of it based on timestamps. How to convert those timestamps to the corresponding numerical indexes?
Thanks a lot
Alex
Adding some examples:
small_df.index[1]
Out[894]: Timestamp('2019-02-08 07:53:33.360000')
small_df.index[10]
Out[895]: Timestamp('2019-02-08 07:54:00.149000') # instead of time stamps.
These are the time period I want to pick from a second data frame that has time indexing as well. But I want to do that with numerical indexing
That means then
1. Find which numerical indexes correspond to the time period above
Based on the comment above this might be quite close on what I need:
start=second_dataframe.index.get_loc(pd.Timestamp(small_df.index[1]))
end=second_dataframe.index.get_loc(pd.Timestamp(small_df.index[10]))
picked_rows= second_dataframe[start:end]
Is there a better way to do that?
I believe you need Index.get_loc if need position:
small_df.index.get_loc(pd.Timestamp('2019-02-08 07:53:33.360000'))
1
EDIT: If values always matched, is possible get timestamp form first and extract second rows by DataFrame.loc:
start = small_df.index[1]
end = small_df.index[10]
picked_rows = second_dataframe.loc[start:end]
OrL
start=pd.Timestamp(small_df.index[1])
end=pd.Timestamp(small_df.index[10])
picked_rows = second_dataframe.loc[start:end]
I have two dataframes with different lengths(df,df1). They share one similar label "collo_number". I want to search the second dataframe for every collo_number in the first data frame. Problem is that the second date frame contains multiple rows for different dates for every collo_nummer. So i want to sum these dates and add this in a new column in the first database.
I now use a loop but it is rather slow and has to perform this operation for al 7 days in a week. Is there a way to get a better performance? I tried multiple solutions but keep getting the error that i cannot use the equal sign for two databases with different lenghts. Help would really be appreciated! Here is an example of what is working but with a rather bad performance.
df5=[df1.loc[(df1.index == nasa) & (df1.afleverdag == x1) & (df1.ind_init_actie=="N"), "aantal_colli"].sum() for nasa in df.collonr]
Your description is a bit vague (hence my comment). First what you good do is to select the rows of the dataframe that you want to search:
dftmp = df1[(df1.afleverdag==x1) & (df1.ind_init_actie=='N')]
so that you don't do this for every item in the loop.
Second, use .groupby.
newseries = dftmp['aantal_colli'].groupby(dftmp.index).sum()
newseries = newseries.ix[df.collonr.unique()]
This post is quiet long and I will be very grateful to everybody who reads it until the end. :)
I am experimenting execution python code issues and would like to know if you have a better way of doing what I want to.
I explain my problem brifely. I have plenty solar panels measurements. Each one of them is done each 3 minutes. Unfortunately, some measurements can fail. The goal is to compare the time in order to keep only the values that have been measured in the same minutes and then retrieve them. A GUI is also included in my software, so each time the user changes the panels to compare, the calculation has to be done again. To do so, I have implemented 2 parts, the first one creates a vector of true or false for each panel for each minute, and the second compare the previous vector and keep only the common measures.
All the datas are contained in the pandas df energiesDatas. The relevant columns are:
name: contains the name of the panel (length 1)
date: contains the day of the measurement (length 1)
list_time: contains a list of all time of measurement of a day (length N)
list_energy_prod : contains the corresponding measures (length N)
The first part loop over all possible minutes from beginning to end of measurements. If a measure has been done, add True, otherwise add False.
self.ListCompare2=pd.DataFrame()
for n in self.NameList:#loop over all my solar panels
m=self.energiesDatas[self.energiesDatas['Name']==n]#all datas
#table_date contains all the possible date from the 1st measure, with interval of 1 min.
table_list=[1 for i in range(len(table_date))]
pointerDate=0 #pointer to the current value of time
#all the measures of a given day are transform into a str of hour-minutes
DateString=[b.strftime('%H-%M') for b in m['list_time'].iloc[pointerDate] ]
#some test
changeDate=0
count=0
#store the current pointed date
m_date=m['Date'].iloc[pointerDate]
#for all possible time
for curr_date in table_date:
#if considered date is bigger, move pointer to next day
while curr_date.date()>m_date:
pointerDate+=1
changeDate=1
m_date=m['Date'].iloc[pointerDate]
#if the day is changed, recalculate the measures of this new day
if changeDate:
DateString=[b.strftime('%H-%M') for b in m['list_time'].iloc[pointerDate] ]
changeDate=0
#check if a measure has been done at the considered time
table_list[count]=curr_date.strftime('%H-%M') in DateString
count+=1
#add to a dataframe
self.ListCompare2[n]=table_list
l2=self.ListCompare2
The second part is the following: given a "ListOfName" of modules to compare, check if they have been measured in the same time and only keep the values measure in the same minute.
ListToKeep=self.ListCompare2[ListOfName[0]]#take list of True or False done before
for i in ListOfName[1:]#for each other panels, check if True too
ListToKeep=ListToKeep&self.ListCompare2[i]
for i in ListOfName:#for each module, recover values
tmp=self.energiesDatas[self.energiesDatas['Name']==i]
count=0
#loop over value we want to keep (also energy produced and the interval of time)
for j,k,l,m,n in zip(tmp['list_time'],tmp['Date'],tmp['list_energy_prod'],tmp['list_energy_rec'],tmp['list_interval']):
#calculation of the index
delta_day=(k-self.dt.date()).days*(18*60)
#if the value of ListToKeep corresponding to the index is True, we keep the value
tmp['list_energy_prod'].iloc[count]=[ l[index] for index,a in enumerate(j) if ListToKeep.iloc[delta_day+(a.hour-4)*60+a.minute]==True]
tmp['list_energy_rec'].iloc[count]=[ m[index] for index,a in enumerate(j) if ListToKeep.iloc[delta_day+(a.hour-4)*60+a.minute]==True]
tmp['list_interval'].iloc[count]=[ n[index] for index,a in enumerate(j) if ListToKeep.iloc[delta_day+(a.hour-4)*60+a.minute]==True]
count+=1
self.store_compare=self.store_compare.append(tmp)
Actually, this part is the one that takes a very long time.
My question is: Is there a way to save time, using build-in function or anything.
Thank you very much
Kilian
The answer of chris-sc sloved my problem:
I believe your data structure isn't appropriate for your problem. Especially the list in fields of a DataFrame, they make loops or apply almost unavoidable. Could you in principle re-structure the data? (For example one df per solar panel with columns date, time, energy)