Week 8 - NumPy (Part II) - 2D NumPy Arrays, Basic Statistics

Liaw Bei Le · May 16, 2021

Learning   Python   NumPy   Practices

2D NumPy Arrays:

  1. type(np_name)
  2. 2D NumPy Array
  3. np_name.shape( )
  4. Subsetting using index positions

Basic Statistics:

  1. np.mean( )
  2. np.median( )
  3. np.corrcoef( )
  4. np.std( )
  5. Generate data using np.random.normal( ) and np.column_stack( )

there’s np.sum( ) , np.sort( ) and more…

Note: All values in arrays need to be the same data type to have a homogeneous array.

What I learnt:

2D NumPy Arrays:

Example: we start with having 2 separate arrays

import numpy as np
np_height=np.array([1.73, 1.68, 1.71, 1.89, 1.79])
np_weight=np.array([65.0, 59.2, 63.4, 88.5, 65.23])
  • type( ) shows type of data. Here ndarray means N-dimensional array. 
    import numpy as np
type(np_height)
type(np_weight)

    Output:

    numpy.ndarray
  • 2D NumPy Array 
    np_2d=np.array([1.73, 1.68, 1.71, 1.89, 1.79],[65.0, 59.2, 63.4, 88.5, 65.23])

    Output:

    array([[1.73, 1.68, 1.71, 1.89, 1.79],[65.0, 59.2, 63.4, 88.5, 65.23]])
  • .shape shows number of rows and columns 
    np_2d.shape

    Output:

    '''shows us 2 rows 5 columns in the 2D NumPy array'''
(2,5)
  • Subsetting using index positions to generate values from specified rows and columns
    • item in index one of this 2d array is the first (nested) array 
        np_2d[0]

      Output:

        array([1.73, 1.68, 1.71, 1.89, 1.79])
    • indicate row index, then column index, to generate one value 
        '''get the item in row index 0 and column index 2'''
  np_2d[0][2]

        '''get the item in row index 0 and column index 2'''
  np_2d[0,2]

      Output:

        1.71
    • we can indicate row and column ranges and generate (2d) arrays as output 
        '''get the items in both rows and column index 1 and 2'''
  np_2d[:,1:3]

      Output:

        # we get a 2d array with 2 rows and 2 columns as the output
  array([[1.68, 1.71],[59.2, 63.4]])

        '''get the items in second rows and all columns'''
  np_2d[1,:]

      Output:

        # we get a 1d array with 1 rows as the output
  array([65.0, 59.2, 63.4, 88.5, 65.23])

Basic Statistics

  • np.mean( ) gets the average value from selected data 
    np.mean(np_city[:,0])

    Output:

    1.7472

  • np.median( ) gets the median value from selected data

  • np.corrcoef( ) for correlation coefficient to see relationship between fields 
    np.corrcoef(np_city[:,0],np_city[:,1])
  • np.std( ) to find standard deviation 
    np.std(np_city[:,0])
  • Generate data using np.random.normal(distribution mean, distribution standard deviation, no. of samples) and np.column_stack( ) 
    height=np.round(np.random.normal(1.75,0.20,5000),2)
weight=np.round(np.random.normal(60.32,15,5000),2)
np_city=np.column_stack((height,weight))

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