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Data Science & Artificial Intelligence
To Fight For Something That Matters
Author: Chris Albon
# Load library import numpy as np# Create vector vector = np . array ([ , , , , , ])# Create matrix matrix = np . array ([[ , , ], [ , , ], [ , , ]])# Tranpose vector vector . Tarray([1, 2, 3, 4, 5, 6])# Transpose matrix matrix . Tarray([[1, 4, 7], [2, 5, 8], [3, 6, 9]])# Load library import numpy as np# Create row vector vector = np . array ([ , , , , , ])# Select second element vector [ ]2# Create matrix matrix = np . array ([[ , , ], [ , , ], [ , , ]])# Select second row, second column matrix [ , ]5# Create matrix tensor = np . array ([ [[[ , ], [ , ]], [[ , ], [ , ]]], [[[ , ], [ , ]], [[ , ], [ , ]]] ])# Select second element of each of the three dimensions tensor [ , , ]array([4, 4])# Load library import numpy as np# Create a 4x3 matrix matrix = np . array ([[ , , ], [ , , ], [ , , ], [ , , ]])# Reshape matrix into 2x6 matrix matrix . reshape ( , )array([[ 1, 2, 3, 4, 5, 6], [ 7, 8, 9, 10, 11, 12]])# Load library import numpy as np# Create matrix matrix = np . array ([[ , ], [ , ]])# Calculate inverse of matrix np . linalg . inv ( matrix )array([[-1.66666667, 1.33333333], [ 0.66666667, -0.33333333]])# Load library import numpy as np# Create matrix matrix = np . array ([[ , , ], [ , , ], [ , , ]])# Return diagonal elements matrix . diagonal ()array([1, 5, 9])# Calculate the tracre of the matrix matrix . diagonal () . sum ()15# Load library import numpy as np# Create matrix matrix = np . array ([[ , , ], [ , , ], [ , , ]])# Flatten matrix matrix . flatten ()array([1, 2, 3, 4, 5, 6, 7, 8, 9])# Load library import numpy as np# Create matrix matrix = np . array ([[ , , ], [ , , ], [ , , ]])# Return matrix rank np . linalg . matrix_rank ( matrix )2# Load library import numpy as np# Create matrix matrix = np . array ([[ , , ], [ , , ], [ , , ]])# Return maximum element np . max ( matrix )9# Return minimum element np . min ( matrix )1# Find the maximum element in each column np . max ( matrix , axis = )array([7, 8, 9])# Find the maximum element in each row np . max ( matrix , axis = )array([3, 6, 9])# Load library import numpy as np# Create matrix matrix = np . array ([[ , , , ], [ , , , ], [ , , , ]])# View number of rows and columns matrix . shape(3, 4)# View number of elements (rows * columns) matrix . size12# View number of dimensions matrix . ndim2# Load library import numpy as np# Create a vector as a row vector_row = np . array ([ , , ])# Create a vector as a column vector_column = np . array ([[ ], [ ], [ ]])# Load libraries import numpy as np from scipy import sparse# Create a matrix matrix = np . array ([[ , ], [ , ], [ , ]])# Create compressed sparse row (CSR) matrix matrix_sparse = sparse . csr_matrix ( matrix )Note: There are many types of sparse matrices. In the example above we use CSR but the type we use should reflect our use case.
# Load library import numpy as np# Create matrix matrix = np . array ([[ , ], [ , ]])Note NumPys mat data structure is less flexible for our purposes and should be avoided.
# Load library from sklearn.feature_extraction import DictVectorizer# Our dictionary of data data_dict = [{ 'Red' : , 'Blue' : }, { 'Red' : , 'Blue' : }, { 'Red' : , 'Yellow' : }, { 'Red' : , 'Yellow' : }]# Create DictVectorizer object dictvectorizer = DictVectorizer ( sparse = False ) # Convert dictionary into feature matrix features = dictvectorizer . fit_transform ( data_dict ) # View feature matrix featuresarray([[ 4., 2., 0.], [ 3., 4., 0.], [ 0., 1., 2.], [ 0., 2., 2.]])# View feature matrix column names dictvectorizer . get_feature_names ()['Blue', 'Red', 'Yellow']# Load library import numpy as np# Create matrix matrix = np . array ([[ , , ], [ , , ], [ , , ]])Font size:
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