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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par

Five real ideas you need to get Success from failures

The list looks so small. But these are so powerful. I hate that expression! And everyone seems to use it. Got fired from your job? It is what it is. Lost your savings? It is what it is. Put on 50 pounds? It is what it is.

failure

Never use It is what it is

  1. Stop saying “it is what it is.” The expression is a mantra for losers. 
  2. Be honest with yourself. Halfway through the tryout, you thought it wasn’t going well, so you stopped pushing yourself, didn’t you? 
  3. “Tryouts didn’t go well because you didn’t try your hardest. You gave up mentally, so you gave up physically.” 
  4. When you fail, figure out why ?
  5. It’s my fault. It’s always my fault—thats way I am in control.
Adopted from www.success.com

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