How do I convert daily data to weekly in Python?

Apply separate aggregation methods to related variables in a timetable while maintaining consistency between aggregated results when converting from a daily to a weekly periodicity. You can use convert2weekly to aggregate both intra-daily data and aggregated daily data. These methods result in equivalent weekly aggregates.

Table of Contents Show

How do you convert daily data to weekly data?
How to group daily data into weeks in pandas?
How do you convert monthly data to weekly data in Python?
How do I get the day of the week from a date column in Python?

Load a timetable (TT) of simulated stock price data and corresponding logarithmic returns. The data stored in TT is recorded at various times throughout the day on New York Stock Exchange (NYSE) business days from January 1, 2018, to December 31,2020. The timetable TT also includes NYSE business calendar awareness. If your timetable does not account for nonbusiness days (weekends, holidays, and market closures), add business calendar awareness by using

            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

2 first.

load('SimulatedStock.mat','TT');
head(TT)

            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

Use

            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

3 to aggregate intra-daily prices and returns to daily periodicity. To maintain consistency between prices and returns, for any given trading day, aggregate prices by reporting the last recorded price with

            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

4 and aggregate returns by summing all logarithmic returns with

            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

TT1 = convert2daily(TT,'Aggregation',["lastvalue" "sum"]);
head(TT1)

       Time        Price     Log_Return
    ___________    ______    __________

    02-Jan-2018    101.37     0.013607 
    03-Jan-2018    100.12    -0.012408 
    04-Jan-2018    106.76     0.064214 
    05-Jan-2018    112.78     0.054856 
    08-Jan-2018    119.07     0.054273 
    09-Jan-2018    119.46      0.00327 
    10-Jan-2018    124.44     0.040842 
    11-Jan-2018    125.63    0.0095174

Use convert2weekly to aggregate the data to a weekly periodicity and compare the results of two different aggregation approaches. The first approach computes weekly results by aggregating the daily aggregates and the second approach computes weekly results by directly aggregating the original intra-daily data.

tt1 = convert2weekly(TT1,'Aggregation',["lastvalue" "sum"]);   % Daily to weekly
tt2 = convert2weekly(TT ,'Aggregation',["lastvalue" "sum"]);   % Intra-daily to weekly

head(tt1)

       Time        Price     Log_Return
    ___________    ______    __________

    05-Jan-2018    112.78      0.12027 
    12-Jan-2018    125.93      0.11029 
    19-Jan-2018    117.67    -0.067842 
    26-Jan-2018     118.8    0.0095573 
    02-Feb-2018    120.85     0.017109 
    09-Feb-2018    123.68     0.023147 
    16-Feb-2018    124.33    0.0052417 
    23-Feb-2018    127.09     0.021956

       Time        Price     Log_Return
    ___________    ______    __________

    05-Jan-2018    112.78      0.12027 
    12-Jan-2018    125.93      0.11029 
    19-Jan-2018    117.67    -0.067842 
    26-Jan-2018     118.8    0.0095573 
    02-Feb-2018    120.85     0.017109 
    09-Feb-2018    123.68     0.023147 
    16-Feb-2018    124.33    0.0052417 
    23-Feb-2018    127.09     0.021956

Notice that the results of the two approaches are the same and that convert2weekly reports on Fridays by default. For weeks in which Friday is not an NYSE trading day, the function reports results on the previous business day. In addition, you can use the convert2weekly optional name-value pair argument

            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

9' to specify a different day of the week that ends business weeks.

How do you convert daily data to weekly data?

Click a cell in the date column of the pivot table that Excel created in the spreadsheet. Right-click and select "Group," then "Days." Enter "7" in the "Number of days" box to group by week. Click "OK" and verify that you have correctly converted daily data to weekly data.

How to group daily data into weeks in pandas?

For example, you can specify Freq=W-MON if you'd like each week to start the day after Monday (i.e. Tuesday) instead. Here's how to interpret the output: The max sales on an individual day during the week starting the day after 1/2/2022 was 9.

How do you convert monthly data to weekly data in Python?

you can use df. resample() with appropriate arguments.

How do I get the day of the week from a date column in Python?

We can use the weekday() method of a datetime. date object to determine if the given date is a weekday or weekend. Note: The weekday() method returns the day of the week as an integer, where Monday is 0 and Sunday is 6. For example, the date(2022, 05, 02) is a Monday.