01 - Introduction to Python

                                                ^/^/^/^/^/^/^/^/^/^/^/^/  Python Basics  ^\^\^\^\^\^\^\^\^\^\^\^\
                 
The Python Interface 
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# Example, do not modify!
print(5 / 8)

# Print the sum of 7 and 10
print(7+10)


Any comments?
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# Division
print(5 / 8)

# Addition
print(7 + 10)


Python as a calculator
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# Addition, subtraction
print(5 + 5)
print(5 - 5)

# Multiplication, division, modulo, and exponentiation
print(3 * 5)
print(10 / 2)
print(18 % 7)
print(4 ** 2)

# How much is your $100 worth after 7 years?
print(100*1.1**7)


Variable Assignment
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# Create a variable savings
savings = 100

# Print out savings
print(savings)


Calculations with variables
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# Create a variable savings
savings = 100

# Create a variable growth_multiplier
growth_multiplier=1.1

# Calculate result
result=savings*growth_multiplier**7

# Print out result
print(result)


Other variable types
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# Create a variable desc
desc = "compound interest"

# Create a variable profitable
profitable = True


Operations with other types
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savings = 100
growth_multiplier = 1.1
desc = "compound interest"

# Assign product of growth_multiplier and savings to year1
year1 = savings*growth_multiplier

# Print the type of year1
print(type(year1))

# Assign sum of desc and desc to doubledesc
doubledesc = desc+desc

# Print out doubledesc
print(doubledesc)


Type conversion
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# Definition of savings and result
savings = 100
result = 100 * 1.10 ** 7

# Fix the printout
print("I started with $" + str(savings) + " and now have $" + str(result) + ". Awesome!")

# Definition of pi_string
pi_string = "3.1415926"

# Convert pi_string into float: pi_float
pi_float = float(pi_string)



                                                ^/^/^/^/^/^/^/^/^/^/^/^/  Python Lists  ^\^\^\^\^\^\^\^\^\^\^\^\
                      
Create a list
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# area variables (in square meters)
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50

# Create list areas
areas = [hall,kit,liv,bed,bath]

# Print areas
print(areas)


Create list with different types
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# area variables (in square meters)
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50

# Adapt list areas
areas = ["hallway",hall,"kitchen", kit, "living room", liv,"bedroom", bed, "bathroom", bath]

# Print areas
print(areas)


List of lists
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# area variables (in square meters)
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50

# house information as list of lists
house = [["hallway", hall],
         ["kitchen", kit],
         ["living room", liv],
         ["bedroom",bed],
         ["bathroom",bath]]

# Print out house
print(house)


Subset and conquer
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# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Print out second element from areas
print(areas[1])

# Print out last element from areas
print(areas[-1])

# Print out the area of the living room
print(areas[5])


Subset and calculate
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# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Sum of kitchen and bedroom area: eat_sleep_area
eat_sleep_area = areas[3]+areas[-3]

# Print the variable eat_sleep_area
print(eat_sleep_area)


Slicing and dicing
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# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Use slicing to create downstairs
downstairs = areas[0:6]

# Use slicing to create upstairs
upstairs = areas[6:10]

# Print out downstairs and upstairs
print(downstairs)
print(upstairs)


Replace list elements
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# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Correct the bathroom area
areas[-1]=10.50

# Change "living room" to "chill zone"
areas[4]="chill zone"


Extend a list
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# Create the areas list and make some changes
areas = ["hallway", 11.25, "kitchen", 18.0, "chill zone", 20.0,
         "bedroom", 10.75, "bathroom", 10.50]

# Add poolhouse data to areas, new list is areas_1
areas_1 = areas + ["poolhouse", 24.5]

# Add garage data to areas_1, new list is areas_2
areas_2 = areas_1 + ["garage", 15.45]


Inner workings of lists
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# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Create areas_copy
areas_copy = list(areas)

# Change areas_copy
areas_copy[0] = 5.0

# Print areas
print(areas)


                                                ^/^/^/^/^/^/^/^/^/^/^/^/  Functions and Packages  ^\^\^\^\^\^\^\^\^\^\^\^\


Familiar functions
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# Create variables var1 and var2
var1 = [1, 2, 3, 4]
var2 = True

# Print out type of var1
print(type(var1))

# Print out length of var1
print(len(var1))

# Convert var2 to an integer: out2
out2 = int(var2)


String Methods
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# string to experiment with: place
place = "poolhouse"

# Use upper() on place: place_up
place_up = place.upper()

# Print out place and place_up
print(place)
print(place_up)

# Print out the number of o's in place
print(place.count('o'))


List Methods
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# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Print out the index of the element 20.0
print(areas.index(20.0))

# Print out how often 9.50 appears in areas
print(areas.count(9.50))


List Methods (2)
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# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Use append twice to add poolhouse and garage size
areas.append(24.5)
areas.append(15.45)


# Print out areas
print(areas)

# Reverse the orders of the elements in areas
areas.reverse()

# Print out areas
print(areas)


Import package
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# Definition of radius
r = 0.43

# Import the math package
import math 

# Calculate C
C = 2*r*math.pi

# Calculate A
A = r*r*math.pi

# Build printout
print("Circumference: " + str(C))
print("Area: " + str(A))


Selective import
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# Definition of radius
r = 192500

# Import radians function of math package
from math import radians

# Travel distance of Moon over 12 degrees. Store in dist.
dist = r*radians(12)

# Print out dist
print(dist)


                                                ^/^/^/^/^/^/^/^/^/^/^/^/  NumPy  ^\^\^\^\^\^\^\^\^\^\^\^\


Your First NumPy Array
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# Create list baseball
baseball = [180, 215, 210, 210, 188, 176, 209, 200]

# Import the numpy package as np
import numpy as np

# Create a numpy array from baseball: np_baseball
np_baseball = np.array(baseball) 

# Print out type of np_baseball
print(type(np_baseball))


Baseball players' height
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# height is available as a regular list

# Import numpy
import numpy as np

# Create a numpy array from height_in: np_height_in
np_height_in = np.array(height_in)

# Print out np_height_in
print(np_height_in)

# Convert np_height_in to m: np_height_m
np_height_m = np_height_in*0.0254

# Print np_height_m
print(np_height_m)


Baseball player's BMI
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# height and weight are available as regular lists

# Import numpy
import numpy as np

# Create array from height_in with metric units: np_height_m
np_height_m = np.array(height_in) * 0.0254

# Create array from weight_lb with metric units: np_weight_kg
np_weight_kg = np.array(weight_lb)*0.453592

# Calculate the BMI: bmi
bmi = np_weight_kg / np_height_m **2

# Print out bmi
print(bmi)


Lightweight baseball players
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# height and weight are available as a regular lists

# Import numpy
import numpy as np

# Calculate the BMI: bmi
np_height_m = np.array(height_in) * 0.0254
np_weight_kg = np.array(weight_lb) * 0.453592
bmi = np_weight_kg / np_height_m ** 2

# Create the light array
light = np.array(bmi)<21

# Print out light
print(light)

# Print out BMIs of all baseball players whose BMI is below 21
print(bmi[light])


Subsetting NumPy Arrays
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# height and weight are available as a regular lists

# Import numpy
import numpy as np

# Store weight and height lists as numpy arrays
np_weight_lb = np.array(weight_lb)
np_height_in = np.array(height_in)

# Print out the weight at index 50
print(np_weight_lb[50])

# Print out sub-array of np_height_in: index 100 up to and including index 110
print(np_height_in[100:111])


Your First 2D NumPy Array
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# Create baseball, a list of lists
baseball = [[180, 78.4],
            [215, 102.7],
            [210, 98.5],
            [188, 75.2]]

# Import numpy
import numpy as np

# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the type of np_baseball
print(type(np_baseball))

# Print out the shape of np_baseball
print(np_baseball.shape)


Baseball data in 2D form
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# baseball is available as a regular list of lists

# Import numpy package
import numpy as np

# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the shape of np_baseball
print(np_baseball.shape)


Subsetting 2D NumPy Arrays
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# baseball is available as a regular list of lists

# Import numpy package
import numpy as np

# Create np_baseball (2 cols)
np_baseball = np.array(baseball)

# Print out the 50th row of np_baseball
print(np_baseball[:50,:])

# Select the entire second column of np_baseball: np_weight_lb
np_weight_lb=np_baseball[:,1]

# Print out height of 124th player
print(np_baseball[:,1])


2D Arithmetic
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# baseball is available as a regular list of lists
# updated is available as 2D numpy array

# Import numpy package
import numpy as np

# Create np_baseball (3 cols)
np_baseball = np.array(baseball)

# Print out addition of np_baseball and updated
print(np_baseball+updated)

# Create numpy array: conversion
conversion = np.array([0.0254, 0.453592, 1])

# Print out product of np_baseball and conversion
print(np_baseball*conversion)


Average versus median
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# np_baseball is available

# Import numpy
import numpy as np

# Create np_height_in from np_baseball
np_height_in = np.array(np_baseball[:,0])

# Print out the mean of np_height_in
print(np.mean(np_height_in))

# Print out the median of np_height_in
print(np.median(np_height_in))


Explore the baseball data
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# np_baseball is available

# Import numpy
import numpy as np

# Print mean height (first column)
avg = np.mean(np_baseball[:,0])
print("Average: " + str(avg))

# Print median height. Replace 'None'
med = np.median(np_baseball[:,0])
print("Median: " + str(med))

# Print out the standard deviation on height. Replace 'None'
stddev = np.std(np_baseball[:,0])
print("Standard Deviation: " + str(stddev))

# Print out correlation between first and second column. Replace 'None'
corr = np.corrcoef(np_baseball[:,0],np_baseball[:,1])
print("Correlation: " + str(corr))


Blend it all together
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# heights and positions are available as lists

# Import numpy
import numpy as np

# Convert positions and heights to numpy arrays: np_positions, np_heights
np_heights = np.array(heights)
np_positions = np.array(positions)


# Heights of the goalkeepers: gk_heights
gh_heights = np_heights[np_positions == 'GK']

# Heights of the other players: other_heights
other_heights = np_heights[np_positions != 'GK']

# Print out the median height of goalkeepers. Replace 'None'
print("Median height of goalkeepers: " + str(np.median(gh_heights)))

# Print out the median height of other players. Replace 'None'
print("Median height of other players: " + str(np.median(other_heights)))