How I can load multiple csv files from class type data into one big dataset?

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Magsud Hasanov 2022-5-17

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回答： Rishav 2023-10-11

Hello, I have a class type data called "mSWW", it has different properties (as per below code). It contains many .csv files with data from smart meters for electric load forecast. This class was generated with Matlab code, that sorted different properties into several scalar vectors (fName, fYear, fMonth and etc.) In the folder this files are sorted by day (17 days in total). For each day, the smart meters did measurements each minute. So there is a lot of csv files for each day (around 1440 for each day).

classdef MeasureSWW
    properties
        % File Path Information
        path        % Main File Path
        fName       % All File Names
        fFullPath   % All File Names (Full Path)
        % File Date Number Information
        fYear       % File Year Identifier
        fMonth      % File Month Identifier
        fDay        % File Day Identifier
        fHour       % File Hour Identifier
        fMinute     % File Minute Identifier
        fSecond     % File Second Identifier
        % Start and End Date Information
        START_DATE  % Start Date Information
        END_DATE    % End Date Information
    end

My goal is

1)to make a big .csv file (for Machine Learning purposes) out of this files.

2) make 17 .csv files for each day.

Any suggestions on how it is better done? Please share your experience.

Right now, what I do is I used datastore() function to read some data. Here is my code so far:

%Loading files from mSWW class
%For creating mSWW, the LoadMeasurements.m was used (with some adjustments)
load mSWW.mat
ds = tabularTextDatastore(mSWW.fFullPath(1:1439, 1),'FileExtensions','.csv')
% creating a timestamp (readable time for Matlab) - datetime type value 
% from Year, Month, Day, Hour, Minute and Second
timeStamp = datetime(mSWW.fYear(1:1439, 1),mSWW.fMonth(1:1439, 1), ...
    mSWW.fDay(1:1439, 1),mSWW.fHour(1:1439, 1),mSWW.fMinute(1:1439, 1),mSWW.fSecond(1:1439, 1))
%Creating a big dataset (raw version)
all_days = readall(ds)
% data for day 1 (example)
day1 = all_days(1:1439,:)
day1.Properties.Description

My problem is that with this approach I can't cover all data in one big file and concate it with timestamp. Because the size of timestamp is 1439x1 and size of all_days is 109363x12.

Thank you very much in advance!

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Answer 1

Rishav 2023-10-11

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Hi Magsud,

I understand that you want to create a big csv file and create 17 csv files for each day.

Create a Big CSV File

Use tabularTextDatastore to read all the data from the CSV files into a single table, which will be your big dataset.
Create timestamps for all the measurements. It's important to make sure that the timestamps match the order of the measurements.
Add the timestamps as a new variable (column) in your table.
Finally, save the table as a single CSV file for machine learning.

Here is how you can do it:

% Assuming mSWW is loaded already
ds = tabularTextDatastore(mSWW.fFullPath, 'FileExtensions', '.csv');
% Read all data into a table
all_data = readall(ds);
% Create timestamps based on your time components
timeStamp = datetime(mSWW.fYear, mSWW.fMonth, mSWW.fDay, ...
    mSWW.fHour, mSWW.fMinute, mSWW.fSecond);
% Add the timestamps to the table
all_data.Timestamp = timeStamp;
% Save the combined table as a big CSV file
writetable(all_data, 'big_data.csv');

Create Daily CSV Files

To create separate CSV files for each of the 17 days, you can use a loop to filter the data for each day and save it as a separate CSV file. Here is how you can do it:

unique_dates = unique(dateshift(all_data.Timestamp, 'start', 'day'));
for i = 1:length(unique_dates)
    date_filter = all_data.Timestamp >= unique_dates(i) & ...
                  all_data.Timestamp < unique_dates(i+1);
    daily_data = all_data(date_filter, :);
    
    % Save daily data as CSV
    filename = sprintf('day_%02d.csv', i);
    writetable(daily_data, filename);
end