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How to write a pandas dataframe with a datetime variable name

By : Doug Smith
Date : October 14 2020, 02:23 PM
should help you out I am scheduling a python script to run everyday,which outputs a dataframe. I wish to save the dataframe everyday with that day's date. For example for today it would be '12/11/18.csv' , This should work:
code :
today = datetime.datetime.today().strftime('%d-%m-%Y')

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Python - Iterrow through pandas dataframe and assign and conditionally update datetime variable

By : Rohan Sul
Date : March 29 2020, 07:55 AM
I hope this helps . Every time through the loop you are writing over the variable index before checking the inequality, so
code :
if index >= row['Time']:
RecentTime = [] # Store list of most recent time for each row
priortime = None
# Loop through 
for index, row in data.iterrows():
    currenttime = row['Time']
    if priortime is None:
        priortime = currenttime

    if priortime > currenttime: # If prior time is greater than current row
        currenttime = priortime

    priortime = currenttime    

Binning Pandas Dataframe by custom and variable length datetime ranges

By : S Dawa
Date : March 29 2020, 07:55 AM
I hope this helps . If you have the start and end times of each period, then you really don't need to create a range. You can just use logic with the datetime objects. Should be easy to generalize to more and more tests if you have that.
code :
import pandas as pd

start_t1 = pd.to_datetime('2017-10-14 00:20:00')
stop_t1 = pd.to_datetime('2017-10-14 00:33:15')
start_t2 = pd.to_datetime('2017-10-14 00:49:15')
stop_t2 = pd.to_datetime('2017-10-14 01:15:15')

df.loc[(df.Timestamp > start_t1) & (df.Timestamp < stop_t1), 'Test'] = 'Test_1'
df.loc[(df.Timestamp > start_t2) & (df.Timestamp < stop_t2), 'Test'] = 'Test_2'

  DeviceID  Quant Result2  QuantResult1           Timestamp    Test
0      15D         387403          7903 2017-10-14 00:28:00  Test_1
1      15D            786       3429734 2017-10-14 00:29:10  Test_1
2      15D            546       2320923 2017-10-14 00:31:15  Test_1
3      15D         435869           232 2017-10-14 00:50:05  Test_2
4      15D             12      34032984 2017-10-14 01:10:07  Test_2

Write value (datetime) in pandas dataframe under diff condition

By : Anshul
Date : March 29 2020, 07:55 AM
I wish this helpful for you You are pretty close! Use groupby on 'groupped_measurement' column you created.
code :
df['groupped_measurement'] = df['value'].diff().fillna(1).eq(1).cumsum().where(df['value'].ne(0))

result = (df.join(df.groupby('groupped_measurement')['time']
                 , on='groupped_measurement')
label_start_end = df['value'].diff().fillna(1, downcast='infer')

df['Start_time'] = df['time'].where(label_start_end.eq(1)).ffill().where(df['value'].eq(1),0)

df['End_time'] = df['time'].where(label_start_end.eq(-1)).bfill().where(df['value'].eq(1),0)
label_start_end = df['value'].diff().fillna(1, downcast='infer')

mask = df['value'].eq(1)

df['Start_time'] = df['time'].where(label_start_end.eq(1)).ffill().where(mask)

df['End_time'] = df['time'].where(label_start_end.eq(-1)).bfill().where(mask)

Converting all the datetime columns of a variable Pandas DataFrame into a different timezone

By : user3703622
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I have to deal with a Pandas DataFrame that can be composed by different datetime columns (they can vary, I can have 0..N datetime cols). I know that each datetime column timezone is UTC and I need to convert their data to another timezone. If I do as follows: , You can loop over the columns which have datetime type:
code :
for col,dtyp in df.dtypes.iteritems():
    if dtyp == 'datetime64[ns]':
        df[col] = df[col].dt.tz_localize('UTC').dt.tz_convert('my_timezone') 

How can I combine datetime.date and datetime.time columns in pandas dataframe?

By : andrefboliveira
Date : March 29 2020, 07:55 AM
Any of those help The issue here is that both date and time are already in datetime format. Try
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