Organizing & Extracting Data from a Large Array

[PaulaS]

Hello there,

Let's say I have the following array:
(what interests me in the array is the first column)

1 5 9
1 4 9
1 8 9
1 9 6
2 5 6
2 4 8
2 4 7
2 6 9
2 3 0
2 0 4
3 5 8
3 4 8
3 9 8
4 8 5
4 8 9
4 8 5
4 3 9
4 8 7
5 8 9
5 8 9
5 4 8
5 7 8
5 6 2
... and so on

The first columns starts with either 1 or 2 or 3 or 4 or 5.

The thing that I want to do is to extract all the rows that start with 1, separately along with all the rows that start with 2, and so on ...

So in the end, according to the above example, I'll have 5 'sub'arrays:
The first is for the columns that start with 1.
The second is for the columns that start with 2.
and so on ...

So this is it.

HOWEVER, my array has 12332 rows and I will have more than 20 'subarrays'. Plus, I want to name each one differently, since later on I'll have to call each subarray on its own.

How can I do it?

I hope I made it as clear as possible.

 

[kreil]

A = [1 5 9; 1 4 9; 1 8 9; 1 9 6; 2 5 6;2 4 8 ;2 4 7 ;2 6 9;2 3 0 ;2 0 4;3 5 8;3 4 8;3 9 8;4 8 5;4 8 9;4 8 5;4 3 9 ;4 8 7;5 8 9;5 8 9;5 4 8;5 7 8;5 6 2];
A(A(:,1)==1,:)

ans =

     1     5     9
     1     4     9
     1     8     9
     1     9     6

If your numbers range from 1-20, then you'll just need a loop. You can store the results in a cell array for use later:

for i = 1:20
C{i}= A(A(:,1)==i,:);
end

When you want to retrieve a matrix with a column that starts with a certain number from the cell array, call it from C using curly brackets:

C{3}

ans =

     3     5     8
     3     4     8
     3     9     8

If you use regular parentheses it will just give some info about the size:

C(3)

ans = 

    [3x3 double]

 

[PaulaS]

I tried it out. But I have 12,332 rows and these rows will contain 74 subarrays (I said earlier more than 20, it turned out 74 subarrays)

I tried out your code, the first array was right, however the second one I got the following:
'Empty matrix: 0-by-7'

(0 by 7 since my original array has 7 columns)

 

[kreil]

That means that there are no entries that start with that number. For example, your example array above ranges 1:5 in the first column. If I loop i=1:20 and call C{6}:

C{6}

ans =

   Empty matrix: 0-by-3

It comes up empty because the call it is evaluating is: A(A(:,1)==6,:). The inside part, A(:,1)==6, evaluates to 0 everywhere since nothing starts with a 6. Then this is passed as a logical index into A( ,:), but since it is all zeros, it returns the empty array (the colon gives the second dimension nonzero extent).You can test whether a cell in your cell array is empty by using isempty(), or just indexing with parentheses:

isempty(C{7})

ans =

     1

C(7)

ans = 

    [0x3 double]

 

[alphy]

As a scientist, organizing and extracting data from a large array can be a daunting task. However, it is an important step in data analysis and can provide valuable insights.

One approach to organizing and extracting data from a large array is to use a programming language such as Python or R. These languages have built-in functions and libraries that can help with data manipulation and organization.

To start, you can use a for loop to iterate through the array and extract the rows that start with each number (1, 2, 3, etc.). This can be done by using conditional statements to check the first element of each row. You can then store these extracted rows in separate arrays or data frames, each with a unique name.

For example, in Python, you can use the Pandas library to read in the array as a data frame and then use the iloc function to extract specific rows based on their index. You can also use the loc function to extract rows based on a specific condition, such as the first element starting with a certain number. The extracted rows can then be stored in separate data frames with unique names using the assignment operator (=).

Another approach is to use regular expressions to search for patterns within the array. This can be helpful if the array has a specific structure or format. You can use the re library in Python to perform regular expression operations and extract the desired data.

In addition, it may be helpful to use descriptive names for the subarrays to make it easier to call and work with them later on. This can be done by using a dictionary or a list to store the subarrays and assigning a name to each one.

Overall, organizing and extracting data from a large array requires some programming skills and knowledge of data manipulation techniques. However, once the data is organized, it can be analyzed more efficiently and provide valuable insights.