title: numpy array vs list of lists?
body: <p>EDIT: I have changed <code>np.array</code> to <code>np.arange</code> to better explain what my problem is.</p>
  
  <p>Being new to Python, I still struggle a lot with data structures. This seems like it should be a very obvious (and obviously duplicate) question, yet I can't understand enough of other explanations to make it work. </p>
  
  <p>I need to create a list of lists (and / or arrays, I'll explain this in a minute), all with different lengths and ranges. I set up my initial arrays in the following way, but I still don't understand how to create the 'partotal' array. I know the syntax is wrong but still don't understand why.</p>
  
  <pre><code>import numpy as np
  
  par1 = np.arange([10,80,1])
  par2 = np.arange([50,120,1])
  par3 = np.arange([0,40,1])
  par4 = np.arange([0,30,1])
  
  partotal = np.array([par1][par2][par3][par4])
  </code></pre>
  
  <p>The second problem, as you may have guessed, is that I have no idea whether I should be using numpy arrays, list of lists, pandas, or something else entirely. Since all my arrays are of different lengths, I find it hard to understand how to put things together or remove them again. </p>
  
  <p>EDIT: The purpose of <code>partotal</code> is to create a set of starting positions for another function (see below)</p>
  
  <pre><code>inputnumber = 200
  
  def positions(partotal, inputnumber):
      return np.random.uniform(partotal, size=(inputnumber, len(partotal)))
  </code></pre>
  
  <p>I know this must sound very basic but as a beginner I find it confusing and difficult. Most answers focus on syntax and don't help develop true insight. If someone can take some time to explain the very obvious I would appreciate it.</p>
  
  <p>FINAL EDIT: The answer was very simple when I understood my own problem. I won't delete the rest of the post, for the sake of others who might need to follow my thought process. </p>
  
  <pre><code>par = 3
  par1 = np.random.uniform(10,80,size=par)
  par2 = np.random.uniform(5,120,size=par)
  par3 = np.random.uniform(0,40,size=par)
  
  allpar = np.array([par1,par2,par3])
  </code></pre>