numpy basics Flashcards
import the numpy library
import numpy as np
in a 2-D array, what are the axes labels for row and column?
rows - axis 0
cols - axis 1
in a 3-D array what are the axes numbered as for rows, columns and array?
rows - axis 0
cols - axis 1
array - axis 2
create an array in numpy
a = np.array([2,3,5])
create a 2-D array in numpy
two_d = np.array([(3,3,6),(1,4,7)])
create a 3-D array in numpy
c = np.array([[(3,4),(1,3)],[(25,2),(1,55)]])
create an array of zeroes as integers with a length of 5
x = np.zeros(5, dtype=int)
create a 2-d array of zeroes as integers, with dimensions of 5 rows by 3 columns
x = np.zeros((5,3),dtype=int)
create an array of even numbers from 2 -> 46 using numpy
a = np.arange(2,47,2)
create an array of 10 evenly spaced values between 0 and 64 inclusive
a = np.linspace(0,65,10)
create a 3 x 5 matrix (3 rows, 5 columns) filled with the number 10
a = np.full((3,5),10)
create an identity matrix measuring 3x3
np.eye(3)
create a 2x8 matrix (row x col) filled with random numbers
np.random.random((2,8))
see row x col dimensions of an array
a.shape
see if 1-d, 2-d, 3-d, etc of an array
a.ndim
see data type of an array
a.dtype.name
convert array to int
a = a.astype(int)
get square root of an array get log of an array get exponentiation of array get sin of an array get cos of an array
b = np.sqrt(a) b = np.log(a) b = np.exp(a) b = np.sin(a) b = np.cos(a)
get the dot product of transposed matrices
a.dot(b)
get sum of an array get min of an array get max of rows get cumulative sum get mean of array get median get standard deviation copy an array sort an array
a.sum()
a.min()
a.max(axis=0)
a.cumsum()
a.mean()
a.median()
np.std(a)
b = a.copy()
a.sort()
get correlation coefficient between two arrays
np.corrcoef(a,b)
transpose and array - what does it mean and how do you do it?
diagonal flip of a matrix…
cols => rows
rows = > cols
b = np.transpose(a)
flatten a multi-dimensional array
a.ravel()
reshape an array, but don’t change data
a.reshape(2,3)
concatenate 2 arrays
c = np.concatenate((a,b), axis=0)
stack 2-D array (row-wise)
c = np.vstack(a,b)
stack 2-D array (col-wise)
c = np.hstack(a,b)
split 2-D array every other row
c = np.vsplit(b, n/2) where n == row length
split 2-D array horizontally by every other column
c = np.hsplit(b, n/2) where n == col length
