NumPy Init & Python Review

A Crash Python Review & Initialization at NumPy lib — #PySeries#Episode 05

Hi, let's init our study focused on Python For Data Science: Meet NumPy!

Here we going to initialize with a crash review in Python, like functions, Maps, Lambdas, Filters, Strings, dictionaries, Lists, Tuple Unpacking, and more…

Then we head up to NUMPY, A Three-Dimensional Blue Cube with the Letter N Highlighted.

NumPy is The fundamental Package for Scientific Computing with Python!

Be very Welcome o/

You’ll find all the code down on my Colab Project Repo in Colab or GitHub page!

PYTHON REVIEW

Functions:

A function can return data as a result.

Creating a Function: in Python, a function is defined using this keyword: def.

#Python Functions:

def times2(var):
  return var*2

Here how to use above function times2:

times2(5)

10

Another example: here you see how to use Docstring to document your code:

def square(num):
  '''
  THIS IS A DOCSTRING,
  CAN GO MULTIPLY LINES
  THIS FUNCTION SQUARES A NUMBER
  '''
  return num**2

Here is how to use square(num):

# Hit Shift-Tab to bring up the signature and docstring of the class:

output = square(2)
output

4

MAP

seq = [1,2,3,4,5]

Cast your Map object to a list:

# Mapping each item in the list seq to times2 function and cast to a anonymous list:

list(map(times2, seq))

[2, 4, 6, 8, 10]

LAMBDA

(for more info see this post)

HowTo To Get A Lambda Expression just from times2 funtion?

Here is the original function:

def times2(var):
  return var*2

The times2 function can be written in one line:

Rewritting it in one line:

def times2(var):return var*2

Now, get rid of the def keyword:

times2(var):return var*2

Now, to get a lambda expression just get rid of the name of the function (typing lambda in its place — cuz lambda is an anonymous function);

lambda(var):return var*2

And get rid of the parenteses adding space in front (cleaning the code:);

lambda var:return var*2

And finally, get rid of return word (its function is assumed by the colon ), like this :)

lambda var:var*2

It Reads: lambda takes var and returns var*2.

Just in one line. Simple Like That! Awesome!

This is not usual for lambda, but it is a feasible use (saving lambda times2 to var t2):

t2 = lambda var:var*2

t2(5)

10

Now use Map plus Lambda together:

# Using Map function plus Lambda function (what it is build for!):

map(lambda var:var*2, seq)

<map at 0x7f0640b37b38>

# Casting Map to a List:

list(map(lambda var:var*2, seq))

[2, 4, 6, 8, 10]

FILTER

filter(lambda var:var%2==0, seq)

<filter at 0x7f0640b37d30>

# Filters out the even number from seq list casted to a list:

list(filter(lambda var:var%2==0, seq))

[2, 4]

Another example:

seq=['soup','dog','salad','cat','greed']

# Filters out 's' init words using lambda expression:

filter(lambda word:word[0]=='s', seq)

<filter at 0x7f06402bb080>

list(filter(lambda word:word[0]=='s', seq))

['soup', 'salad']

STRING

s = 'hello my name is j3'

Upper function:

s.upper()

'HELLO MY NAME IS J3'

Lower function:

s.lower()

'hello my name is j3'

Split Function:

my_string_list = s.split()

my_string_list

['hello', 'my', 'name', 'is', 'j3']

Tweet use:

tweet = "Go Sports!#Sports"

tweet.split('#')

['Go Sports!', 'Sports']

tweet.split('#')[0]

'Go Sports!'

More Split Function:

tweet.split('#')[1]

'Sports'

Creating a function to count dog occurrences:

# Number of time the word ‘dog’ occurs in the string:

def countDog(phrase):
  count = 0
  for word in phrase.lower().split():
    if word == 'dog':
      count += 1
  return count

Use countDog function:

countDog('This dog runs faster than the other dog dude!')

2

DICTIONARY

d = {'key1':1, 'key2':2}

Keys:

d.keys()

dict_keys(['key1', 'key2'])

Items:

d.items()

dict_items([('key1', 1), ('key2', 2)])

Values:

d.values()

dict_values([1, 2])

LIST

lst = [1,2,3]

Pop & Append Methods:

item = lst.pop()

item

3

lst

[1, 2]

lst.append(3)

lst

[1, 2, 3]

IN word

'x' in ['x','y', 'z']

True

'w' in ['x', 'y', 'z']

False

TUPLE UNPACKING

Python tuples are immutable means that they can not be modified in the whole program.

Packing and Unpacking a Tuple: In Python, there is a very powerful tuple assignment feature that assigns the right-hand side of values into the left-hand side. In another way, it is called the unpacking of a tuple of values into a variable. In packing, we put values into a new tuple while in unpacking we extract those values into a single variable.

x = [(1,2),(3,4),(5,6)]

x[0][0]

1

x[0][1]

2

Using for loop:

for item in x:
  print(item)

(1, 2)
(3, 4)
(5, 6)

Unpacking:

for (a,b) in x:
  print(a)

1
3
5

Or just like this:

for a,b in x:
  print(a+1, b+1)

2 3
4 5
6 7

NUMPY

The reason it is so important for Data Science with Python is that a lot of all the libraries in the Python Ecosystem rely on NUMPY as one of their main building blocks (Jose Portilla — Python For Data Science course).

NUMPY is also incredibly fast as it has bound to C libraries.

Installation:

conda install numpy (Anaconda) or pip install numpy

ARRAYS (Vectors & Matrices) in NUMPY

-> Always cast a list into NUMPY array

# Vectors:

my_list=[1,2,3]

import numpy as np 

vec = np.array(my_list)

# 1 bracket mean 1-dimensional array ([]):

vec

array([1, 2, 3])

# Matrices:

my_mat = [[1,2,3],[4,5,6],[7,8,9]]

import numpy as np

mat=np.array(my_mat)

# 2 brackets mean 2-dimensional array ([[]]):

mat
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

ARANGE in NUMPY

# All the way up to 10, but not including 10 -> return AN NUMPY ARRAY sequence of 10 digits counting from 0 to 9

# np.arange(start, stop):

np.arange(0,10)

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

The third argument is the step:

# Third argument: step size you may want

# np.arange(start, stop, step):

np.arange(0,10,2)

array([0, 2, 4, 6, 8])

ZEROES & ONES & EYE(Identity matrix) in NUMPY

np.zeros(3)

array([0., 0., 0.])

np.zeros((5,5))

array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])

Ones:

np.zeros(3)

array([1., 1., 1., 1.])

np.ones((5,5))

array([[1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.]])

Identity Matrix:

np.eye(4)

array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]])

LINSPACE (SPACED EVENLY) in NUMPY

# From 0, to 5 returning 10 digits spaced evenly:

np.linspace(0,5,10)

array([0.        , 0.55555556, 1.11111111, 1.66666667, 2.22222222,
       2.77777778, 3.33333333, 3.88888889, 4.44444444, 5.        ])

PLEASE DON’T CONFUSE linspace WITH arange:

arange: will take in the third argument as the STEPS SIZE (you want)

linspace: will take in the third argument as the NUMBER OF POINTS (evenly separated)

RANDOM NUMBER in NUMPY

# RAND differs from ARANGE because RAND returns random numbers:

np.random.rand(5)

array([0.08930558, 0.21911281, 0.20612461, 0.51468239, 0.11276573])

N means Normal Distribution:

# n means Normal Distribution (return values around the Mean in gaussian distribution):

np.random.randn(2)

array([0.44034175, 1.1805967 ])

Without the Third Parameter:

# Without the third parameter, it will return one digit from 1(included) to 100 (not included):

np.random.randint(1,100)

84

With the Third Parameter:

# The third parameter specify the number of digits you want to return:

np.random.randint(1,100,10)

array([25, 60, 41, 48, 76, 99, 86, 62, 74, 86])

Difference between ARANGE and RANDINT:

ARANGE: returns sequence of digits

RANDINT: returns randomly selected digits

#Vectors:

arr = np.arange(25)

arr

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24])

#Matrices:

randarr = np.random.randint(0,50,10)

randarr

array([ 2, 11, 26, 19, 13, 37, 35, 36, 18, 24])

RESHAPE in NUMPY

# Get the and Numpy array and reshape it as you want;

Here reshaping arr (1_dimentional) as a matrix of 5x5 (2_dimentional):

reshapped_arr = arr.reshape(5,5)

reshapped_arr

array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])

MAX & MIN in NUMPY

reshapped_arr.min()

0

reshapped_arr.max()

24

ATTRIBUTES SHAPE & DTYPE in NUMPY

(there are no Parentheses cuz this is an attribute, remember?)

reshapped_arr.shape

(5, 5)

reshapped_arr.dtype

dtype('int64')

To import use this syntax:

from numpy.random import randint

That’s it!

print("Thank you for reading this page! Please favorite it, IF YOU LIKE:)")

Thank you for reading this page! Please favorite it, IF YOU LIKE:)

At this point in time, I just want to say THANK YOU for sticking along with this Python Series so far.

It’s been a long way but I’m hoping you have learned a lot!

There is much more to come... Stay tuned!

Bye and till the next #PySeries Episode o/

Fun Little Quiz

A BONUS FOR NUMPY EPISODE;)

01Make this Matriz a:

02 How would you index these parts of the matrix b? Create Matrices c,d, and e :-)

Solutions here:

👉Colab Google link:)

👉GitHub Repo link:)

👉Files from Google Drive:)

👆More .ipynb

Credits & References:

Jose Portilla — Python for Data Science and Machine Learning Bootcamp — Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

Python NumPy Tutorial for Beginners — https://youtu.be/QUT1VHiLmmI by freeCodeCamp.org

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review: Aug/2021 (text improvements — better lambda explaination)