tree = 6 
for i in range(6):
    print(" " * tree , end = '*' )
    print('*'*2*i)
    tree -=1 

      *
     ***
    *****
   *******
  *********
 ***********

tree = 10 
z = tree + 2 
x = 1 
for n in range(0,tree):
    for n in range(0,z):
        print(' ', end = '')
    for n in range(0,x):
        print('* ', end = '')
    x= x + 1
    z = z -1 
    print()

            * 
           * * 
          * * * 
         * * * * 
        * * * * * 
       * * * * * * 
      * * * * * * * 
     * * * * * * * * 
    * * * * * * * * * 
   * * * * * * * * * * 

import math 
for n in range(0,9):
    i = math.floor(n/2)
    s = math.floor((9-n)/2)
    if (n/2).is_integer():
        tree_even = ' '*s + '*'*i + ' ' + '*'*i  + ' '*s
        print(tree_even)
    else: 
        tree_odd = ' '*s + '*'*n + ' '*s 
        print(tree_odd)
print('   !!   ')

         
    *    
   * *   
   ***   
  ** **  
  *****  
 *** *** 
 ******* 
**** ****
   !!   

i = 0 
star = '*'
tree_size = 12
trunk = 3 

while i < tree_size: 
    print(star.center(40))
    star += '**' 
    i += 1 

while i < (tree_size + trunk):
    print('***'.center(40))
    i = i +1 
    

                   *                    
                  ***                   
                 *****                  
                *******                 
               *********                
              ***********               
             *************              
            ***************             
           *****************            
          *******************           
         *********************          
        ***********************         
                  ***                   
                  ***                   
                  ***                   

rows = 5 
for i in range(rows+1):
    for j in range(i):
        print(i, end = '')
    print('')

1
22
333
4444
55555

list_number = [1,2,3,4,5,6,7,8,9]
i = 10 
for number in range(9):
    print((int((i-1)/2))*'+', str(number)*number + (int((i+1)/2))*'+')
    i -= 1

++++ +++++
++++ 1+++++
+++ 22++++
+++ 333++++
++ 4444+++
++ 55555+++
+ 666666++
+ 7777777++
 88888888+

Continue : skips to the next iteration of the loop

fruits = ['bananas', 'apples', 'cherry']
for x in fruits:
    if x == 'bananas':
       continue
    print(x)

apples
cherry

i = 0 
while i < 6: 
    i = i + 1
    if i == 3:
        continue
    print(i)

1
2
4
5
6

Break stops the Loop

fruits = ['bananas', 'apples', 'cherry']

for x in fruits:
    print(x)
    if x == 'apples': 
        break
    

bananas
apples

i = 1 
while i < 6:
    print(i)
    if i == 3:
        break
    i += 1

1
2
3

Pass empty for loop

mylist = []
for x in [1,2,3]:
  pass

mylist

[]

mylist = []
for x in [1,2,3]:
    break
    mylist.append(x)



mylist

[]

Nested For loops

persons = ['Jon', 'Pete', 'Mary']
food = ['Japanese', 'Mexican', 'French']

for person in persons:
    for restaurant in food:
        print(person + ' eats in ' + restaurant)
        
        

Jon eats in Japanese
Jon eats in Mexican
Jon eats in French
Pete eats in Japanese
Pete eats in Mexican
Pete eats in French
Mary eats in Japanese
Mary eats in Mexican
Mary eats in French

Nested if statements

num = float(input('Enter a number:'))

if (type(num) != int):
    print('Not a number please try again')
    
if num >= 0: 
    if num == 0: 
        print('Number is zero')
    else: 
        print('Positive')
if num < 0: 
    print('Number is negative')

Enter a number:14
Not a number please try again

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-47-e3c5748c4fac> in <module>
      4     print('Not a number please try again')
      5 
----> 6 if num >= 0:
      7     if num == 0:
      8         print('Number is zero')

TypeError: '>=' not supported between instances of 'str' and 'int'

float('14')

14.0

FUNctions

def print_greeting():
    print('Hello students!')
    
print_greeting()

Hello students!

def print_date(month, day , year):
    joined = str(year) + '/' + str(month) +'/' + str(day)
    print(joined)

print_date(8,13,2021)

2021/8/13

def averages(values):
    if len(values) == 0: 
        return None
    return(sum(values)/len(values))

averages([5,6,7,10])

7.0

averages('hello')

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-63-624559abe31e> in <module>
----> 1 averages('hello')

<ipython-input-61-97af3c6a5d68> in averages(values)
      2     if len(values) == 0:
      3         return None
----> 4     return(sum(values)/len(values))

TypeError: unsupported operand type(s) for +: 'int' and 'str'

def birthday_func(month, date):
    print('My birthday is on the month of : '+ str(month))
    print('My birthday is on the : ' + str(date))
    

birthday_func(7,14)

My birthday is on the month of : 7
My birthday is on the : 14

birthday_func('July', 14)

My birthday is on the month of : July
My birthday is on the : 14

birthday_func('July', '14')

My birthday is on the month of : July
My birthday is on the : 14

birthday_func(7, '14')

My birthday is on the month of : 7
My birthday is on the : 14

def birthday_func(month, date):
    assert month == str
    print('My birthday is on the month of : '+ str(month))
    print('My birthday is on the : ' + str(date))
    

birthday_func(7, '14')

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-71-4ddaebe8be08> in <module>
----> 1 birthday_func(7, '14')

<ipython-input-70-3b4dd0ccc843> in birthday_func(month, date)
      1 def birthday_func(month, date):
----> 2     assert month == str
      3     print('My birthday is on the month of : '+ str(month))
      4     print('My birthday is on the : ' + str(date))
      5 

AssertionError: 

def first_negative(values):
    for v in values:
        if v < 0:
            return v

print(first_negative([5, 6,-1, 9 , 10]))
print(first_negative([]))

-1
None

def first_negative(values):
    assert values != []
    for v in values:
        if v < 0:
            return v
first_negative([5, 6,-1, 9 , 10])
first_negative([])

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-77-c6bb682b4d5f> in <module>
      5             return v
      6 first_negative([5, 6,-1, 9 , 10])
----> 7 first_negative([])

<ipython-input-77-c6bb682b4d5f> in first_negative(values)
      1 def first_negative(values):
----> 2     assert values != []
      3     for v in values:
      4         if v < 0:
      5             return v

AssertionError: 

mylist = [] 
def first_negative(values):
    for v in values:
        if v >= 0:
            print('I am in this if statement block')
            continue
        else:
            mylist.append(v)
            break
            
    if len(mylist) == 1 : 
        return mylist
    else:
        return'There are no negatives'
    
        

first_negative([1,2,3,4])

I am in this if statement block
I am in this if statement block
I am in this if statement block
I am in this if statement block

'There are no negatives'

mylist

[-5]

The point of (No) returns

def func_returns(): 
    print('hello')
    print('hello')
    print('hello')
    print('hello')
func_returns()

hello
hello
hello
hello

def func_returns(a): 
    if(a <= 5):
        return('hello')
    if(a >= 10):
        return('bye')

func_returns()

Local/Global variables

b = 10
def math_ploy(x1):
    y = x1 + b 
    print(y)
    return y

print(math_ploy(5))
print(y)

15
15

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-115-f115e8ead91e> in <module>
      6 
      7 print(math_ploy(5))
----> 8 print(y)

NameError: name 'y' is not defined

var = 5
var2 =4

def func_type1():
    return 'hello'

def func_type2(var):
    print(var)

def func_type3(cat = var, dog = var2):
    print(cat -1)
    print(dog -1)

func_type1()

'hello'

func_type2(5)

5

func_type3(cat=10, dog = 6)

9
5

def a():
    return 5

def b(y):
    print('this is my nested function')
    
y = a()
print(y)
b(y)

5
this is my nested function

IMPORTS

import math 
print('pi is ', math.pi)

pi is  3.141592653589793

math.cos(math.pi)

-1.0

from math import cos, pi
print(cos(pi))

-1.0

math.cos(math.pi)

-1.0

sin()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-8-25560f9ce61b> in <module>
----> 1 sin()

NameError: name 'sin' is not defined

import math as m
print(m.cos(m.pi))

-1.0

from math import cos, pi

sin(pi)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-9-1de0726db8ae> in <module>
      1 from math import cos, pi
      2 
----> 3 sin(pi)

NameError: name 'sin' is not defined

sin(pi)

1.2246467991473532e-16

NUMPY

Arrays and more arrays

Vectors

import numpy as np

a = np.array([1,2,3,4,5])
print(a)
type(a)

[1 2 3 4 5]

numpy.ndarray

a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
type(a)

numpy.ndarray

#np.arange(start, stop, step)

np.arange(1, 11, 1)

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

#np.linspace(start, stop, n_elements)
start = 0 
stop = 100 
n_elements = 201
x = np.linspace(start, stop, n_elements)
print(x)
len(x)

[  0.    0.5   1.    1.5   2.    2.5   3.    3.5   4.    4.5   5.    5.5
   6.    6.5   7.    7.5   8.    8.5   9.    9.5  10.   10.5  11.   11.5
  12.   12.5  13.   13.5  14.   14.5  15.   15.5  16.   16.5  17.   17.5
  18.   18.5  19.   19.5  20.   20.5  21.   21.5  22.   22.5  23.   23.5
  24.   24.5  25.   25.5  26.   26.5  27.   27.5  28.   28.5  29.   29.5
  30.   30.5  31.   31.5  32.   32.5  33.   33.5  34.   34.5  35.   35.5
  36.   36.5  37.   37.5  38.   38.5  39.   39.5  40.   40.5  41.   41.5
  42.   42.5  43.   43.5  44.   44.5  45.   45.5  46.   46.5  47.   47.5
  48.   48.5  49.   49.5  50.   50.5  51.   51.5  52.   52.5  53.   53.5
  54.   54.5  55.   55.5  56.   56.5  57.   57.5  58.   58.5  59.   59.5
  60.   60.5  61.   61.5  62.   62.5  63.   63.5  64.   64.5  65.   65.5
  66.   66.5  67.   67.5  68.   68.5  69.   69.5  70.   70.5  71.   71.5
  72.   72.5  73.   73.5  74.   74.5  75.   75.5  76.   76.5  77.   77.5
  78.   78.5  79.   79.5  80.   80.5  81.   81.5  82.   82.5  83.   83.5
  84.   84.5  85.   85.5  86.   86.5  87.   87.5  88.   88.5  89.   89.5
  90.   90.5  91.   91.5  92.   92.5  93.   93.5  94.   94.5  95.   95.5
  96.   96.5  97.   97.5  98.   98.5  99.   99.5 100. ]

201

x_5 = np.where(x %5 == 0)
print(x_5)
type(x_5)
#will return the indeces where in the x array it is divisible by 5

(array([  0,  10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120,
       130, 140, 150, 160, 170, 180, 190, 200]),)

tuple

x[x_5]

array([  0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,  40.,  45.,  50.,
        55.,  60.,  65.,  70.,  75.,  80.,  85.,  90.,  95., 100.])

start =0 
end = 100
n_elem = 501

x = np.linspace(start, end, n_elem)
print('x', x)
y = (.1*x)**2 - (5*x) + 3 
print('y', y)

np.savetxt('my_text_file.txt', np.transpose([x,y]))

x [  0.    0.2   0.4   0.6   0.8   1.    1.2   1.4   1.6   1.8   2.    2.2
   2.4   2.6   2.8   3.    3.2   3.4   3.6   3.8   4.    4.2   4.4   4.6
   4.8   5.    5.2   5.4   5.6   5.8   6.    6.2   6.4   6.6   6.8   7.
   7.2   7.4   7.6   7.8   8.    8.2   8.4   8.6   8.8   9.    9.2   9.4
   9.6   9.8  10.   10.2  10.4  10.6  10.8  11.   11.2  11.4  11.6  11.8
  12.   12.2  12.4  12.6  12.8  13.   13.2  13.4  13.6  13.8  14.   14.2
  14.4  14.6  14.8  15.   15.2  15.4  15.6  15.8  16.   16.2  16.4  16.6
  16.8  17.   17.2  17.4  17.6  17.8  18.   18.2  18.4  18.6  18.8  19.
  19.2  19.4  19.6  19.8  20.   20.2  20.4  20.6  20.8  21.   21.2  21.4
  21.6  21.8  22.   22.2  22.4  22.6  22.8  23.   23.2  23.4  23.6  23.8
  24.   24.2  24.4  24.6  24.8  25.   25.2  25.4  25.6  25.8  26.   26.2
  26.4  26.6  26.8  27.   27.2  27.4  27.6  27.8  28.   28.2  28.4  28.6
  28.8  29.   29.2  29.4  29.6  29.8  30.   30.2  30.4  30.6  30.8  31.
  31.2  31.4  31.6  31.8  32.   32.2  32.4  32.6  32.8  33.   33.2  33.4
  33.6  33.8  34.   34.2  34.4  34.6  34.8  35.   35.2  35.4  35.6  35.8
  36.   36.2  36.4  36.6  36.8  37.   37.2  37.4  37.6  37.8  38.   38.2
  38.4  38.6  38.8  39.   39.2  39.4  39.6  39.8  40.   40.2  40.4  40.6
  40.8  41.   41.2  41.4  41.6  41.8  42.   42.2  42.4  42.6  42.8  43.
  43.2  43.4  43.6  43.8  44.   44.2  44.4  44.6  44.8  45.   45.2  45.4
  45.6  45.8  46.   46.2  46.4  46.6  46.8  47.   47.2  47.4  47.6  47.8
  48.   48.2  48.4  48.6  48.8  49.   49.2  49.4  49.6  49.8  50.   50.2
  50.4  50.6  50.8  51.   51.2  51.4  51.6  51.8  52.   52.2  52.4  52.6
  52.8  53.   53.2  53.4  53.6  53.8  54.   54.2  54.4  54.6  54.8  55.
  55.2  55.4  55.6  55.8  56.   56.2  56.4  56.6  56.8  57.   57.2  57.4
  57.6  57.8  58.   58.2  58.4  58.6  58.8  59.   59.2  59.4  59.6  59.8
  60.   60.2  60.4  60.6  60.8  61.   61.2  61.4  61.6  61.8  62.   62.2
  62.4  62.6  62.8  63.   63.2  63.4  63.6  63.8  64.   64.2  64.4  64.6
  64.8  65.   65.2  65.4  65.6  65.8  66.   66.2  66.4  66.6  66.8  67.
  67.2  67.4  67.6  67.8  68.   68.2  68.4  68.6  68.8  69.   69.2  69.4
  69.6  69.8  70.   70.2  70.4  70.6  70.8  71.   71.2  71.4  71.6  71.8
  72.   72.2  72.4  72.6  72.8  73.   73.2  73.4  73.6  73.8  74.   74.2
  74.4  74.6  74.8  75.   75.2  75.4  75.6  75.8  76.   76.2  76.4  76.6
  76.8  77.   77.2  77.4  77.6  77.8  78.   78.2  78.4  78.6  78.8  79.
  79.2  79.4  79.6  79.8  80.   80.2  80.4  80.6  80.8  81.   81.2  81.4
  81.6  81.8  82.   82.2  82.4  82.6  82.8  83.   83.2  83.4  83.6  83.8
  84.   84.2  84.4  84.6  84.8  85.   85.2  85.4  85.6  85.8  86.   86.2
  86.4  86.6  86.8  87.   87.2  87.4  87.6  87.8  88.   88.2  88.4  88.6
  88.8  89.   89.2  89.4  89.6  89.8  90.   90.2  90.4  90.6  90.8  91.
  91.2  91.4  91.6  91.8  92.   92.2  92.4  92.6  92.8  93.   93.2  93.4
  93.6  93.8  94.   94.2  94.4  94.6  94.8  95.   95.2  95.4  95.6  95.8
  96.   96.2  96.4  96.6  96.8  97.   97.2  97.4  97.6  97.8  98.   98.2
  98.4  98.6  98.8  99.   99.2  99.4  99.6  99.8 100. ]
y [ 3.000000e+00  2.000400e+00  1.001600e+00  3.600000e-03 -9.936000e-01
 -1.990000e+00 -2.985600e+00 -3.980400e+00 -4.974400e+00 -5.967600e+00
 -6.960000e+00 -7.951600e+00 -8.942400e+00 -9.932400e+00 -1.092160e+01
 -1.191000e+01 -1.289760e+01 -1.388440e+01 -1.487040e+01 -1.585560e+01
 -1.684000e+01 -1.782360e+01 -1.880640e+01 -1.978840e+01 -2.076960e+01
 -2.175000e+01 -2.272960e+01 -2.370840e+01 -2.468640e+01 -2.566360e+01
 -2.664000e+01 -2.761560e+01 -2.859040e+01 -2.956440e+01 -3.053760e+01
 -3.151000e+01 -3.248160e+01 -3.345240e+01 -3.442240e+01 -3.539160e+01
 -3.636000e+01 -3.732760e+01 -3.829440e+01 -3.926040e+01 -4.022560e+01
 -4.119000e+01 -4.215360e+01 -4.311640e+01 -4.407840e+01 -4.503960e+01
 -4.600000e+01 -4.695960e+01 -4.791840e+01 -4.887640e+01 -4.983360e+01
 -5.079000e+01 -5.174560e+01 -5.270040e+01 -5.365440e+01 -5.460760e+01
 -5.556000e+01 -5.651160e+01 -5.746240e+01 -5.841240e+01 -5.936160e+01
 -6.031000e+01 -6.125760e+01 -6.220440e+01 -6.315040e+01 -6.409560e+01
 -6.504000e+01 -6.598360e+01 -6.692640e+01 -6.786840e+01 -6.880960e+01
 -6.975000e+01 -7.068960e+01 -7.162840e+01 -7.256640e+01 -7.350360e+01
 -7.444000e+01 -7.537560e+01 -7.631040e+01 -7.724440e+01 -7.817760e+01
 -7.911000e+01 -8.004160e+01 -8.097240e+01 -8.190240e+01 -8.283160e+01
 -8.376000e+01 -8.468760e+01 -8.561440e+01 -8.654040e+01 -8.746560e+01
 -8.839000e+01 -8.931360e+01 -9.023640e+01 -9.115840e+01 -9.207960e+01
 -9.300000e+01 -9.391960e+01 -9.483840e+01 -9.575640e+01 -9.667360e+01
 -9.759000e+01 -9.850560e+01 -9.942040e+01 -1.003344e+02 -1.012476e+02
 -1.021600e+02 -1.030716e+02 -1.039824e+02 -1.048924e+02 -1.058016e+02
 -1.067100e+02 -1.076176e+02 -1.085244e+02 -1.094304e+02 -1.103356e+02
 -1.112400e+02 -1.121436e+02 -1.130464e+02 -1.139484e+02 -1.148496e+02
 -1.157500e+02 -1.166496e+02 -1.175484e+02 -1.184464e+02 -1.193436e+02
 -1.202400e+02 -1.211356e+02 -1.220304e+02 -1.229244e+02 -1.238176e+02
 -1.247100e+02 -1.256016e+02 -1.264924e+02 -1.273824e+02 -1.282716e+02
 -1.291600e+02 -1.300476e+02 -1.309344e+02 -1.318204e+02 -1.327056e+02
 -1.335900e+02 -1.344736e+02 -1.353564e+02 -1.362384e+02 -1.371196e+02
 -1.380000e+02 -1.388796e+02 -1.397584e+02 -1.406364e+02 -1.415136e+02
 -1.423900e+02 -1.432656e+02 -1.441404e+02 -1.450144e+02 -1.458876e+02
 -1.467600e+02 -1.476316e+02 -1.485024e+02 -1.493724e+02 -1.502416e+02
 -1.511100e+02 -1.519776e+02 -1.528444e+02 -1.537104e+02 -1.545756e+02
 -1.554400e+02 -1.563036e+02 -1.571664e+02 -1.580284e+02 -1.588896e+02
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 -3.939900e+02 -3.945936e+02 -3.951964e+02 -3.957984e+02 -3.963996e+02
 -3.970000e+02]

pwd

'/Users/suarezc/Desktop/Bootcamp_2021/Untitled Folder'

data = np.loadtxt('../my_text_file.txt')
#data = np.loadtxt('..\my_text_file.txt') # For windows path 


print(data)

[[ 0.000000e+00  3.000000e+00]
 [ 2.000000e-01  2.000400e+00]
 [ 4.000000e-01  1.001600e+00]
 ...
 [ 9.960000e+01 -3.957984e+02]
 [ 9.980000e+01 -3.963996e+02]
 [ 1.000000e+02 -3.970000e+02]]

data[0]

array([0., 3.])

#All rows, first column
data[:,0] 

array([  0. ,   0.2,   0.4,   0.6,   0.8,   1. ,   1.2,   1.4,   1.6,
         1.8,   2. ,   2.2,   2.4,   2.6,   2.8,   3. ,   3.2,   3.4,
         3.6,   3.8,   4. ,   4.2,   4.4,   4.6,   4.8,   5. ,   5.2,
         5.4,   5.6,   5.8,   6. ,   6.2,   6.4,   6.6,   6.8,   7. ,
         7.2,   7.4,   7.6,   7.8,   8. ,   8.2,   8.4,   8.6,   8.8,
         9. ,   9.2,   9.4,   9.6,   9.8,  10. ,  10.2,  10.4,  10.6,
        10.8,  11. ,  11.2,  11.4,  11.6,  11.8,  12. ,  12.2,  12.4,
        12.6,  12.8,  13. ,  13.2,  13.4,  13.6,  13.8,  14. ,  14.2,
        14.4,  14.6,  14.8,  15. ,  15.2,  15.4,  15.6,  15.8,  16. ,
        16.2,  16.4,  16.6,  16.8,  17. ,  17.2,  17.4,  17.6,  17.8,
        18. ,  18.2,  18.4,  18.6,  18.8,  19. ,  19.2,  19.4,  19.6,
        19.8,  20. ,  20.2,  20.4,  20.6,  20.8,  21. ,  21.2,  21.4,
        21.6,  21.8,  22. ,  22.2,  22.4,  22.6,  22.8,  23. ,  23.2,
        23.4,  23.6,  23.8,  24. ,  24.2,  24.4,  24.6,  24.8,  25. ,
        25.2,  25.4,  25.6,  25.8,  26. ,  26.2,  26.4,  26.6,  26.8,
        27. ,  27.2,  27.4,  27.6,  27.8,  28. ,  28.2,  28.4,  28.6,
        28.8,  29. ,  29.2,  29.4,  29.6,  29.8,  30. ,  30.2,  30.4,
        30.6,  30.8,  31. ,  31.2,  31.4,  31.6,  31.8,  32. ,  32.2,
        32.4,  32.6,  32.8,  33. ,  33.2,  33.4,  33.6,  33.8,  34. ,
        34.2,  34.4,  34.6,  34.8,  35. ,  35.2,  35.4,  35.6,  35.8,
        36. ,  36.2,  36.4,  36.6,  36.8,  37. ,  37.2,  37.4,  37.6,
        37.8,  38. ,  38.2,  38.4,  38.6,  38.8,  39. ,  39.2,  39.4,
        39.6,  39.8,  40. ,  40.2,  40.4,  40.6,  40.8,  41. ,  41.2,
        41.4,  41.6,  41.8,  42. ,  42.2,  42.4,  42.6,  42.8,  43. ,
        43.2,  43.4,  43.6,  43.8,  44. ,  44.2,  44.4,  44.6,  44.8,
        45. ,  45.2,  45.4,  45.6,  45.8,  46. ,  46.2,  46.4,  46.6,
        46.8,  47. ,  47.2,  47.4,  47.6,  47.8,  48. ,  48.2,  48.4,
        48.6,  48.8,  49. ,  49.2,  49.4,  49.6,  49.8,  50. ,  50.2,
        50.4,  50.6,  50.8,  51. ,  51.2,  51.4,  51.6,  51.8,  52. ,
        52.2,  52.4,  52.6,  52.8,  53. ,  53.2,  53.4,  53.6,  53.8,
        54. ,  54.2,  54.4,  54.6,  54.8,  55. ,  55.2,  55.4,  55.6,
        55.8,  56. ,  56.2,  56.4,  56.6,  56.8,  57. ,  57.2,  57.4,
        57.6,  57.8,  58. ,  58.2,  58.4,  58.6,  58.8,  59. ,  59.2,
        59.4,  59.6,  59.8,  60. ,  60.2,  60.4,  60.6,  60.8,  61. ,
        61.2,  61.4,  61.6,  61.8,  62. ,  62.2,  62.4,  62.6,  62.8,
        63. ,  63.2,  63.4,  63.6,  63.8,  64. ,  64.2,  64.4,  64.6,
        64.8,  65. ,  65.2,  65.4,  65.6,  65.8,  66. ,  66.2,  66.4,
        66.6,  66.8,  67. ,  67.2,  67.4,  67.6,  67.8,  68. ,  68.2,
        68.4,  68.6,  68.8,  69. ,  69.2,  69.4,  69.6,  69.8,  70. ,
        70.2,  70.4,  70.6,  70.8,  71. ,  71.2,  71.4,  71.6,  71.8,
        72. ,  72.2,  72.4,  72.6,  72.8,  73. ,  73.2,  73.4,  73.6,
        73.8,  74. ,  74.2,  74.4,  74.6,  74.8,  75. ,  75.2,  75.4,
        75.6,  75.8,  76. ,  76.2,  76.4,  76.6,  76.8,  77. ,  77.2,
        77.4,  77.6,  77.8,  78. ,  78.2,  78.4,  78.6,  78.8,  79. ,
        79.2,  79.4,  79.6,  79.8,  80. ,  80.2,  80.4,  80.6,  80.8,
        81. ,  81.2,  81.4,  81.6,  81.8,  82. ,  82.2,  82.4,  82.6,
        82.8,  83. ,  83.2,  83.4,  83.6,  83.8,  84. ,  84.2,  84.4,
        84.6,  84.8,  85. ,  85.2,  85.4,  85.6,  85.8,  86. ,  86.2,
        86.4,  86.6,  86.8,  87. ,  87.2,  87.4,  87.6,  87.8,  88. ,
        88.2,  88.4,  88.6,  88.8,  89. ,  89.2,  89.4,  89.6,  89.8,
        90. ,  90.2,  90.4,  90.6,  90.8,  91. ,  91.2,  91.4,  91.6,
        91.8,  92. ,  92.2,  92.4,  92.6,  92.8,  93. ,  93.2,  93.4,
        93.6,  93.8,  94. ,  94.2,  94.4,  94.6,  94.8,  95. ,  95.2,
        95.4,  95.6,  95.8,  96. ,  96.2,  96.4,  96.6,  96.8,  97. ,
        97.2,  97.4,  97.6,  97.8,  98. ,  98.2,  98.4,  98.6,  98.8,
        99. ,  99.2,  99.4,  99.6,  99.8, 100. ])

#All rows, second column
data[:,1]

array([ 3.000000e+00,  2.000400e+00,  1.001600e+00,  3.600000e-03,
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       -8.942400e+00, -9.932400e+00, -1.092160e+01, -1.191000e+01,
       -1.289760e+01, -1.388440e+01, -1.487040e+01, -1.585560e+01,
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       -3.636000e+01, -3.732760e+01, -3.829440e+01, -3.926040e+01,
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       -4.407840e+01, -4.503960e+01, -4.600000e+01, -4.695960e+01,
       -4.791840e+01, -4.887640e+01, -4.983360e+01, -5.079000e+01,
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       -5.556000e+01, -5.651160e+01, -5.746240e+01, -5.841240e+01,
       -5.936160e+01, -6.031000e+01, -6.125760e+01, -6.220440e+01,
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       -7.068960e+01, -7.162840e+01, -7.256640e+01, -7.350360e+01,
       -7.444000e+01, -7.537560e+01, -7.631040e+01, -7.724440e+01,
       -7.817760e+01, -7.911000e+01, -8.004160e+01, -8.097240e+01,
       -8.190240e+01, -8.283160e+01, -8.376000e+01, -8.468760e+01,
       -8.561440e+01, -8.654040e+01, -8.746560e+01, -8.839000e+01,
       -8.931360e+01, -9.023640e+01, -9.115840e+01, -9.207960e+01,
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       -1.397584e+02, -1.406364e+02, -1.415136e+02, -1.423900e+02,
       -1.432656e+02, -1.441404e+02, -1.450144e+02, -1.458876e+02,
       -1.467600e+02, -1.476316e+02, -1.485024e+02, -1.493724e+02,
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       -1.776336e+02, -1.784764e+02, -1.793184e+02, -1.801596e+02,
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       -1.843536e+02, -1.851900e+02, -1.860256e+02, -1.868604e+02,
       -1.876944e+02, -1.885276e+02, -1.893600e+02, -1.901916e+02,
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       -1.976400e+02, -1.984636e+02, -1.992864e+02, -2.001084e+02,
       -2.009296e+02, -2.017500e+02, -2.025696e+02, -2.033884e+02,
       -2.042064e+02, -2.050236e+02, -2.058400e+02, -2.066556e+02,
       -2.074704e+02, -2.082844e+02, -2.090976e+02, -2.099100e+02,
       -2.107216e+02, -2.115324e+02, -2.123424e+02, -2.131516e+02,
       -2.139600e+02, -2.147676e+02, -2.155744e+02, -2.163804e+02,
       -2.171856e+02, -2.179900e+02, -2.187936e+02, -2.195964e+02,
       -2.203984e+02, -2.211996e+02, -2.220000e+02, -2.227996e+02,
       -2.235984e+02, -2.243964e+02, -2.251936e+02, -2.259900e+02,
       -2.267856e+02, -2.275804e+02, -2.283744e+02, -2.291676e+02,
       -2.299600e+02, -2.307516e+02, -2.315424e+02, -2.323324e+02,
       -2.331216e+02, -2.339100e+02, -2.346976e+02, -2.354844e+02,
       -2.362704e+02, -2.370556e+02, -2.378400e+02, -2.386236e+02,
       -2.394064e+02, -2.401884e+02, -2.409696e+02, -2.417500e+02,
       -2.425296e+02, -2.433084e+02, -2.440864e+02, -2.448636e+02,
       -2.456400e+02, -2.464156e+02, -2.471904e+02, -2.479644e+02,
       -2.487376e+02, -2.495100e+02, -2.502816e+02, -2.510524e+02,
       -2.518224e+02, -2.525916e+02, -2.533600e+02, -2.541276e+02,
       -2.548944e+02, -2.556604e+02, -2.564256e+02, -2.571900e+02,
       -2.579536e+02, -2.587164e+02, -2.594784e+02, -2.602396e+02,
       -2.610000e+02, -2.617596e+02, -2.625184e+02, -2.632764e+02,
       -2.640336e+02, -2.647900e+02, -2.655456e+02, -2.663004e+02,
       -2.670544e+02, -2.678076e+02, -2.685600e+02, -2.693116e+02,
       -2.700624e+02, -2.708124e+02, -2.715616e+02, -2.723100e+02,
       -2.730576e+02, -2.738044e+02, -2.745504e+02, -2.752956e+02,
       -2.760400e+02, -2.767836e+02, -2.775264e+02, -2.782684e+02,
       -2.790096e+02, -2.797500e+02, -2.804896e+02, -2.812284e+02,
       -2.819664e+02, -2.827036e+02, -2.834400e+02, -2.841756e+02,
       -2.849104e+02, -2.856444e+02, -2.863776e+02, -2.871100e+02,
       -2.878416e+02, -2.885724e+02, -2.893024e+02, -2.900316e+02,
       -2.907600e+02, -2.914876e+02, -2.922144e+02, -2.929404e+02,
       -2.936656e+02, -2.943900e+02, -2.951136e+02, -2.958364e+02,
       -2.965584e+02, -2.972796e+02, -2.980000e+02, -2.987196e+02,
       -2.994384e+02, -3.001564e+02, -3.008736e+02, -3.015900e+02,
       -3.023056e+02, -3.030204e+02, -3.037344e+02, -3.044476e+02,
       -3.051600e+02, -3.058716e+02, -3.065824e+02, -3.072924e+02,
       -3.080016e+02, -3.087100e+02, -3.094176e+02, -3.101244e+02,
       -3.108304e+02, -3.115356e+02, -3.122400e+02, -3.129436e+02,
       -3.136464e+02, -3.143484e+02, -3.150496e+02, -3.157500e+02,
       -3.164496e+02, -3.171484e+02, -3.178464e+02, -3.185436e+02,
       -3.192400e+02, -3.199356e+02, -3.206304e+02, -3.213244e+02,
       -3.220176e+02, -3.227100e+02, -3.234016e+02, -3.240924e+02,
       -3.247824e+02, -3.254716e+02, -3.261600e+02, -3.268476e+02,
       -3.275344e+02, -3.282204e+02, -3.289056e+02, -3.295900e+02,
       -3.302736e+02, -3.309564e+02, -3.316384e+02, -3.323196e+02,
       -3.330000e+02, -3.336796e+02, -3.343584e+02, -3.350364e+02,
       -3.357136e+02, -3.363900e+02, -3.370656e+02, -3.377404e+02,
       -3.384144e+02, -3.390876e+02, -3.397600e+02, -3.404316e+02,
       -3.411024e+02, -3.417724e+02, -3.424416e+02, -3.431100e+02,
       -3.437776e+02, -3.444444e+02, -3.451104e+02, -3.457756e+02,
       -3.464400e+02, -3.471036e+02, -3.477664e+02, -3.484284e+02,
       -3.490896e+02, -3.497500e+02, -3.504096e+02, -3.510684e+02,
       -3.517264e+02, -3.523836e+02, -3.530400e+02, -3.536956e+02,
       -3.543504e+02, -3.550044e+02, -3.556576e+02, -3.563100e+02,
       -3.569616e+02, -3.576124e+02, -3.582624e+02, -3.589116e+02,
       -3.595600e+02, -3.602076e+02, -3.608544e+02, -3.615004e+02,
       -3.621456e+02, -3.627900e+02, -3.634336e+02, -3.640764e+02,
       -3.647184e+02, -3.653596e+02, -3.660000e+02, -3.666396e+02,
       -3.672784e+02, -3.679164e+02, -3.685536e+02, -3.691900e+02,
       -3.698256e+02, -3.704604e+02, -3.710944e+02, -3.717276e+02,
       -3.723600e+02, -3.729916e+02, -3.736224e+02, -3.742524e+02,
       -3.748816e+02, -3.755100e+02, -3.761376e+02, -3.767644e+02,
       -3.773904e+02, -3.780156e+02, -3.786400e+02, -3.792636e+02,
       -3.798864e+02, -3.805084e+02, -3.811296e+02, -3.817500e+02,
       -3.823696e+02, -3.829884e+02, -3.836064e+02, -3.842236e+02,
       -3.848400e+02, -3.854556e+02, -3.860704e+02, -3.866844e+02,
       -3.872976e+02, -3.879100e+02, -3.885216e+02, -3.891324e+02,
       -3.897424e+02, -3.903516e+02, -3.909600e+02, -3.915676e+02,
       -3.921744e+02, -3.927804e+02, -3.933856e+02, -3.939900e+02,
       -3.945936e+02, -3.951964e+02, -3.957984e+02, -3.963996e+02,
       -3.970000e+02])

a = np.empty(4)
a.fill(5.5)
a

array([5.5, 5.5, 5.5, 5.5])

np.arange(5)

array([0, 1, 2, 3, 4])

b = np.random.normal(10,3,5)
b

array([ 5.21504731,  9.33161611,  7.82804446, 13.43441166, 14.88559031])

mask = b > 9

mask

array([False,  True, False,  True,  True])

b[mask]

array([ 9.33161611, 13.43441166, 14.88559031])

b[mask] = 0

b

array([5.21504731, 0.        , 7.82804446, 0.        , 0.        ])

c = np.random.normal(10,3, (2,4))
c

array([[12.13497155, 11.88190856,  6.23317578, 12.41081666],
       [16.14427092, 10.70031063, 10.39910172,  9.10741824]])

c.shape

(2, 4)

c.sum()

89.01197405930381

c.sum(axis=0)

array([28.27924247, 22.58221919, 16.6322775 , 21.5182349 ])

c

array([[12.13497155, 11.88190856,  6.23317578, 12.41081666],
       [16.14427092, 10.70031063, 10.39910172,  9.10741824]])

c[0,:]

array([12.13497155, 11.88190856,  6.23317578, 12.41081666])

c[1,:]

array([16.14427092, 10.70031063, 10.39910172,  9.10741824])

c[:,0]

array([12.13497155, 16.14427092])

c.flatten()

array([12.13497155, 11.88190856,  6.23317578, 12.41081666, 16.14427092,
       10.70031063, 10.39910172,  9.10741824])

np.array2string(np.array(['hello', 'lunch']))

"['hello' 'lunch']"

PANDAS

DataFrames

import pandas as pd

data = pd.read_csv('../Python/gapminder_data/data/gapminder_gdp_oceania.csv')

data.describe()

	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
count	2.000000	2.000000	2.000000	2.000000	2.00000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000	2.000000
mean	10298.085650	11598.522455	12696.452430	14495.021790	16417.33338	17283.957605	18554.709840	20448.040160	20894.045885	24024.175170	26938.778040	29810.188275
std	365.560078	917.644806	677.727301	43.986086	525.09198	1485.263517	1304.328377	2037.668013	3578.979883	4205.533703	5301.853680	6540.991104
min	10039.595640	10949.649590	12217.226860	14463.918930	16046.03728	16233.717700	17632.410400	19007.191290	18363.324940	21050.413770	23189.801350	25185.009110
25%	10168.840645	11274.086022	12456.839645	14479.470360	16231.68533	16758.837652	18093.560120	19727.615725	19628.685412	22537.294470	25064.289695	27497.598692
50%	10298.085650	11598.522455	12696.452430	14495.021790	16417.33338	17283.957605	18554.709840	20448.040160	20894.045885	24024.175170	26938.778040	29810.188275
75%	10427.330655	11922.958888	12936.065215	14510.573220	16602.98143	17809.077558	19015.859560	21168.464595	22159.406358	25511.055870	28813.266385	32122.777858
max	10556.575660	12247.395320	13175.678000	14526.124650	16788.62948	18334.197510	19477.009280	21888.889030	23424.766830	26997.936570	30687.754730	34435.367440

data

	country	gdpPercap_1952	gdpPercap_1957	gdpPercap_1962	gdpPercap_1967	gdpPercap_1972	gdpPercap_1977	gdpPercap_1982	gdpPercap_1987	gdpPercap_1992	gdpPercap_1997	gdpPercap_2002	gdpPercap_2007
0	Australia	10039.59564	10949.64959	12217.22686	14526.12465	16788.62948	18334.19751	19477.00928	21888.88903	23424.76683	26997.93657	30687.75473	34435.36744
1	New Zealand	10556.57566	12247.39532	13175.67800	14463.91893	16046.03728	16233.71770	17632.41040	19007.19129	18363.32494	21050.41377	23189.80135	25185.00911

c

array([[12.13497155, 11.88190856,  6.23317578, 12.41081666],
       [16.14427092, 10.70031063, 10.39910172,  9.10741824]])

df_c = pd.DataFrame(c)

df_c.describe()

	0	1	2	3
count	2.000000	2.000000	2.000000	2.000000
mean	14.139621	11.291110	8.316139	10.759117
std	2.835003	0.835516	2.945754	2.335855
min	12.134972	10.700311	6.233176	9.107418
25%	13.137296	10.995710	7.274657	9.933268
50%	14.139621	11.291110	8.316139	10.759117
75%	15.141946	11.586509	9.357620	11.584967
max	16.144271	11.881909	10.399102	12.410817