Notes on Python

# Create list areas
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50
areas = [hall,kit,liv,bed,bath]

# Create list with different types
areas = ["hallway",hall, "kitchen",kit, "living room", liv, "bedroom",bed, "bathroom", bath]

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

x = ["a", "b", "c", "d"]
# Subset and conquer
x[1]
x[-3] # same result!

# Subset and calculate
print(x[1] + x[3])
> bd

# Slicing and dicing. (The start index will be included, while the end index is not)
my_list[start:end]

x[1:3]  
> ['b', 'c']
x[:2]  
> ['a', 'b']
x[2:]  
> ['c', 'd']
x[:]  
> ["a", "b", "c", "d"]

# Subsetting lists of lists
x = [["a", "b", "c"],
     ["d", "e", "f"],
     ["g", "h", "i"]]
x[2][0]
> 'g'
x[2][:2]
> ['g', 'h']

x = ["a", "b", "c", "d"]
# Replace list elements
x[1] = "r"
x[2:] = ["s", "t"]

# Extend a list
y = x + ["e", "f"]

# Delete list elements
del(x[1])

# 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) # Areas dosen't change.But if use areas_copy = areas, when change areas_copy, areas change as well.

# Familiar functions
print()
type()

# switch between data types
str()
int()
bool()
float()

# Help!
help(max)
?max

place = "poolhouse"
place_up=place.upper()
> POOLHOUSE
print(place.count('o')) # Print out the number of o's in place (3)

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))

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

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

# Import package
## Import the math package
import math
r = 0.43 # Definition of radius
C = 2*math.pi*r # Calculate C=2πr
A = math.pi*r**2 # Calculate A=πr^2

# Selective import
## Import radians function of math package
from math import radians
r = 192500 # Definition of radius
dist=r*radians(12) # Travel distance of Moon over 12 degrees. Store in dist.

# Import the numpy package as np
import numpy as np

# Create a numpy array
baseball = [180, 215, 210, 210, 188, 176, 209, 200]
np_baseball=np.array(baseball)

# 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

# Boolean numpy arrays
x = [4 , 9 , 6, 3, 1]
import numpy as np
y = np.array(x)
high = y > 5
> array([False,  True,  True, False, False])
y[high]  
> array([9, 6])

# type coercion. (Numpy arrays cannot contain elements with different types. If you try to build such a list, some of the elements' types are changed to end up with a homogeneous list.)
np.array([True, 1, 2]) + np.array([3, 4, False])
np.array([4, 3, 0]) + np.array([0, 2, 2]) # same result!

# 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(type(np_baseball))
> <class 'numpy.ndarray'>  
print(np_baseball.shape)
> (4, 2)

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

## Print out the 50th row of np_baseball
print(np_baseball[49,:])

## 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[123,0])

# numpy was able to perform all calculations element-wise.
import numpy as np
np_mat = np.array([[1, 2],
                   [3, 4],
                   [5, 6]])
np_mat * 2
>  array([[ 2,  4],
>         [ 6,  8],
>         [10, 12]])  
np_mat + np.array([10, 10])  
>array([[11, 12],
>      [13, 14],
>      [15, 16]])

import numpy as np
x = [1, 4, 8, 10, 12]
np.mean(x) # 7.0
np.median(x) # 8.0
np.std(x)
np.corrcoef(x,y)

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Make a line plot: year on the x-axis, pop on the y-axis
plt.plot(year,pop)

# Display the plot with plt.show()
plt.show()

# Make a line plot, gdp_cap on the x-axis, life_exp on the y-axis
plt.plot(gdp_cap,life_exp)

# Display the plot
plt.show()

import matplotlib.pyplot as plt

# Make a scatter plot, gdp_cap on the x-axis, life_exp on the y-axis
plt.scatter(gdp_cap, life_exp)

# Put the x-axis on a logarithmic scale
plt.xscale('log')

# Display the plot
plt.show()

# Create histogram of life_exp data(Bin=10 by defualt)
plt.hist(life_exp)

# Display histogram and clean up plot
plt.show()
plt.clf()

# Build histogram with 5 bins
plt.hist(life_exp,bins=5)

# Show and clean up plot
plt.show()
plt.clf()

# A dictionary is constructed that maps continents onto colors
dict = {
    'Asia':'red',
    'Europe':'green',
    'Africa':'blue',
    'Americas':'yellow',
    'Oceania':'black'
}
gm["colour"] = gm["continent"].map(colourDict)

# Scatter plot assingning size(s), colour(c) and transparency(alpha)
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c = col, alpha = 0.8)

# Labels
plt.xscale('log')
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')

# Ticks
plt.xticks([1000,10000,100000], ['1k','10k','100k'])

# Add the words "India" and "China" in the plot.
plt.text(1550, 71, 'India')
plt.text(5700, 80, 'China')

# Add grid() call
plt.grid(True)

# Show the plot
plt.show()

# Definition of countries and capital
countries = ['spain', 'france', 'germany', 'norway']
capitals = ['madrid', 'paris', 'berlin', 'oslo']

# From string in countries and capitals, create dictionary europe
europe = { 'spain':'madrid', 'france': 'paris', 'germany':'berlin', 'norway':'oslo'}

# Print out the keys in europe
print(europe.keys())

# Print out value that belongs to key 'norway'
print(europe['norway'])

# Add italy to europe
europe['italy'] = 'rome'

# Print out italy in europe, output should be true or false
print('italy' in europe)

# Add poland to europe
europe['poland'] = 'warsaw'

# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw',
          'australia':'vienna' }

# Update capital of germany, bonn to berlin
europe['germany'] = 'berlin'

# Remove australia
del(europe['australia'])

# Dictionary of dictionaries(嵌套)
europe = { 'spain': { 'capital':'madrid', 'population':46.77 },
           'france': { 'capital':'paris', 'population':66.03 },
           'germany': { 'capital':'berlin', 'population':80.62 },
           'norway': { 'capital':'oslo', 'population':5.084 } }

# Print out the capital of France
print(europe['france']['capital'])

# Create sub-dictionary data
data = {'capital':'rome', 'population':59.83 }

# Add data to europe under key 'italy'
europe['italy']=data

# Print europe
print(europe)

# Pre-defined lists
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]

# Import pandas as pd
import pandas as pd

# Create dictionary my_dict with three key:value pairs: my_dict
my_dict = {'country': names,'drives_right': dr,'cars_per_cap':cpc}

# Build a DataFrame cars from my_dict: cars
cars = pd.DataFrame(my_dict)

# Definition of row_labels
row_labels = ['US', 'AUS', 'JPN', 'IN', 'RU', 'MOR', 'EG']

# Specify row labels of cars
cars.index = row_labels

# Import the cars.csv data: cars
# 括号里的文件名称要打引号
cars = pd.read_csv('cars.csv')

# The first column is used as row labels
cars = pd.read_csv('cars.csv',index_col=0)

# Print out country column as Pandas Series
print(cars['country'])

# Print out country column as Pandas DataFrame
print(cars[['country']])

# Print out DataFrame with country and drives_right columns
print(cars[['country','drives_right']])

# Print out first 3 observations
print(cars[0:4])

# Print out fourth, fifth and sixth observation
print(cars[3:6])

# Each pair of commands here gives the same result.
cars.loc['RU']
cars.iloc[4]

cars.loc[['RU']]
cars.iloc[[4]]

cars.loc[['RU', 'AUS']]
cars.iloc[[4, 1]]

# Print out observation for Japan as a Series
print(cars.loc['JPN'])
print(cars.iloc[4])

# Print out observations for Australia and Egypt as DataFrame
print(cars.loc[['AUS','EG']])
print(cars.iloc[[1,6]])

# Each pair of commands here gives the same result.
cars.loc['IN', 'cars_per_cap']
cars.iloc[3, 0]

cars.loc[['IN', 'RU'], 'cars_per_cap']
cars.iloc[[3, 4], 0]

cars.loc[['IN', 'RU'], ['cars_per_cap', 'country']]
cars.iloc[[3, 4], [0, 1]]

# Each pair of commands here gives the same result.
cars.loc[:, 'country']
cars.iloc[:, 1]

cars.loc[:, ['country','drives_right']]
cars.iloc[:, [1, 2]]

# Equality
True == False # return False
-5 * 15 != 75 # return True
"pyscript" == "PyScript" # return False
True == 1 # return True

# Greater and less than
x = -3 * 6
x >= -10 # return False

y = "test"
"test"<=y # return True
True > False # return True

# Compare arrays(element-wise)
## Create arrays
import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50])
your_house = np.array([14.0, 24.0, 14.25, 9.0])

## my_house greater than or equal to 18
print(my_house >= 18)
> [ True  True False False]

## my_house less than your_house
print(my_house <= your_house)
> [ True  True False False]

# Define variables
my_kitchen = 18.0
your_kitchen = 14.0

# my_kitchen bigger than 10 and smaller than 18?
print(my_kitchen > 10 and my_kitchen < 18)

# my_kitchen smaller than 14 or bigger than 17?
print(my_kitchen < 14 or my_kitchen > 17)

# Double my_kitchen smaller than triple your_kitchen?
print(my_kitchen * 2 < your_kitchen * 3)

# 记得冒号！！
# if
area = 14.0
if area > 15 :
    print("big place!")

# Add else
if area > 15 :
    print("big place!")
else :
    print("pretty small." )

# Customize further: elif
if area > 15 :
    print("big place!")
elif area > 10 :
    print("medium size, nice!")
else :
    print("pretty small.")

# Driving right
## Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

## Extract drives_right column as Series: dr
dr = cars['drives_right'] # panda series of boolen
> US      True
> AUS    False
> JPN    False
> IN     False
> RU      True
> MOR     True
> EG      True
> Name: drives_right, dtype: bool

## Use dr to subset cars: sel
sel = cars[dr] # drives_right=True的国家的各项信息

## or in one line
sel = cars[cars['drives_right']]

# Cars per capita
## Create car_maniac: observations that have a cars_per_cap over 500
cpc=cars['cars_per_cap']
many_cars=cpc>500  # panda series of boolen
car_maniac = cars[many_cars]

## Create medium: observations with cars_per_cap between 100 and 500
cpc = cars['cars_per_cap']
between = np.logical_and(cpc > 100, cpc < 500)
medium=cars[between]

# Initialize offset
offset = 8

# Code the while loop
while offset != 0 :
    print("correcting...")
    offset = offset - 1
    print(offset)

# Add conditionals
## Initialize offset
offset = -6

## Code the while loop
while offset != 0 :
    print("correcting...")
    if offset > 0 :
      offset = offset - 1
    else :
      offset = offset + 1
    print(offset)

areas = [11.25, 18.0, 20.0, 10.75, 9.50]
for area in areas:
    print(area)
# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Change for loop to use enumerate() and update print()
for index,a in enumerate(areas) :
    print('room' + str(index) + ':' + str(a))

# Change for loop to use enumerate() and update print()
## 需要索引的时候用
for index,a in enumerate(areas) :
    print('room' + str(index) + ':' + str(a))

# house list of lists
house = [["hallway", 11.25],
         ["kitchen", 18.0],
         ["living room", 20.0],
         ["bedroom", 10.75],
         ["bathroom", 9.50]]

# Build a for loop from scratch
## 注意空格！！
for [x,y] in house:
    print('the ' + x + ' is ' + str(y) +' sqm')

# Use method items()
# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 'austria':'vienna' }

# Iterate over europe
for x, y in europe.items() :
    print("the capital of " + x + " is " + y)

# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Iterate over rows of cars
for label,row in cars.iterrows() :
    print(label)
    print(row)

# select variables from the Pandas Series using square brackets
for lab, row in cars.iterrows() :
    print(lab + ': ' + str(row['cars_per_cap']))
> US: 809
> AUS: 731
> JPN: 588
> IN: 18
> RU: 200
> MOR: 70
> EG: 45

# Code for loop that adds COUNTRY column that contains a uppercase version of the country names in the "country" column.
for lab, row in cars.iterrows() :
    cars.loc[lab, "COUNTRY"] = row['country'].upper()

# .apply(str.upper) give the same result
cars["COUNTRY"] = cars["country"].apply(str.upper)

# Import numpy as np
import numpy as np

# Set the seed
np.random.seed(123)

# Generate and print random float
print(np.random.rand())

# Starting step
step = 50

# Roll the dice
dice = np.random.randint(1,7)

# Finish the control construct
if dice <= 2 :
    step = step - 1
elif dice <= 5 :
    step = step + 1
else :
    step = step + np.random.randint(1,7)

# Print out dice and step
print(dice)
> 3
print(step)
> 51

# Initialize random_walk
random_walk = [0]

# Finish the for loop
for x in range(100) :
    # Set step: last element in random_walk
    # 基于上一步确定走到哪里
    step = random_walk[-1]

    # Roll the dice
    dice = np.random.randint(1,7)

    # Determine next step
    if dice <= 2:
         # Replace below: use max to make sure step can't go below 0
        step = max(0, step - 1)
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    # append next_step to random_walk
    random_walk.append(step)

# Print random_walk
print(random_walk)

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Plot random_walk
plt.plot(random_walk)

# Show the plot
plt.show()

# Initialize all_walks (don't change this line)
all_walks = []

# Simulate random walk 10 times
for i in range(10) :

    # Code from before
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)

        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
         # Implement clumsiness.You're a bit clumsy and you have a 0.1% chance of falling down
        if np.random.rand() <= 0.001 :
            step = 0
        random_walk.append(step)

    # Append random_walk to all_walks
    all_walks.append(random_walk)

# Print all_walks
print(all_walks)

# Convert all_walks to Numpy array: np_aw
np_aw = np.array(all_walks)

# Plot np_aw and show
plt.plot(np_aw)
plt.show()

# Clear the figure
plt.clf()

# Transpose np_aw: np_aw_t
np_aw_t=np.transpose(np_aw)

# Plot np_aw_t and show
plt.plot(np_aw_t)
plt.show()

# Select last row from np_aw_t: ends
ends = np_aw_t[-1,:]

# Plot histogram of ends, display plot
plt.hist(ends)
plt.show()

#  To calculate the chance that this end point is greater than or equal to 60
np.mean(ends > 30) # gives you the fraction of simulations that ended higher than step 30.
> 0.784