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""" A multi-line comment describes your code to someone who is reading it. """ Example: """ This program will ask the user for two numbers. Then it will add the numbers and print the final value. """ number_one = int(input("Enter a number: ")) number_two = int(input("Enter a second number: ")) print("Sum: " + str(number_one + number_two)) # Use single line comments to clarify parts of code. Example: # This program adds 1 and 2 added = 1 + 2 print(added)

# Make a variable to store text name = "Zach" # Create variables that are numbers num_one = 3 num_two = 4 sum = num_one + num_two # We can also assign multiple variables at once num_one, num_two = 3, 4 # The value of a variable can be changed after it has been # created num_one = num_one + 1

print("Hello world") print(2 + 2) print(10)

`str()`

function. The strings are concatenated with a plus symbol. print("The mean is " + str(my_list.mean()) + " .")

+ Addition - Subtraction * Multiplication / Division % Modulus (Remainder) () Parentheses (For order of operations) # Examples z = x + y w = x * y # Division a = 5.0 / 2 # Returns 2.5 b = 5.0 // 2 # Returns 2.0 c = 5/2 # Returns 2.5 d = 5 // 2 # Returns 2 # Increment (add one) x += 1 # Decrement (subtract one) x -= 1 # Absolute value absolute_value = abs(x) abs_val = abs(-5) # Returns 5 # Square root import math square_root = math.sqrt(x) # Raising to a power power = math.pow(x, y) # Calculates x^y # Rounding rounded_num = round(2.675, 2) # Returns 2.68

# Random integer between (and including) low and high import random random_num = random.randint(low, high) random_element = random.choice(string) # Example: # Returns random number within and including 0 and 10. random_num = random.randint(0,10) # Random element in a string random_element = random.choice('abcdefghij')

x == y # is x equal to y x != y # is x not equal to y x > y # is x greater than y x >= y # is x greater than or equal to y x < y # is x less than y x <= y # is x less than or equal to y # Comparison operators in if statements if x == y: print("x and y are equal") if x > 5: print("x is greater than 5.")

# And Operator and_expression = x and y # Or Operator or_expression = x or y # You can combine many booleans! boolean_expression = x and (y or z)

def name_of_your_function(): # Code that will run when you make a call to # this function. # Example: # Teach the computer to add two numbers num_one = 1 num_two = 2 def add_numbers(): sum = num_one + num_two

# We add a return statement in order to use the value of the # sum variable num_one = 1 num_two = 2 def add_numbers(): sum = num_one + num_two return sum

# Call the add_numbers() function once # The computer will return a value of 3 add_numbers() # Call the add_numbers() function 3 times and print the output # The output will be the number 3 printed on 3 separate lines print(add_numbers()) print(add_numbers()) print(add_numbers())

# In this program, parameters are used to give two numbers def add_numbers(num_one, num_two): sum = num_one + num_two return sum # We call the function with values inside the parentheses # This program will print ‘7’ print(add_numbers(3, 4)) # If we have a list with the same number of parameters, we # can use the items to assign arguments using an asterisk my_list = [3, 4] print(add_numbers(*my_list))

# Create an empty list my_list = [] # Create a list with any number of items my_list = [item1, item2, item3] # Example: number_list = [1, 2, 4] # A list can have any type my_list = [integer, string, boolean] # Example: a_list = ["hello", 4, True]

# Access an element in a list a_list = [“hello”, 4, True] first_element = a_list[0] # Returns "hello" # Set an element in a list a_list = [“hello”, 4, True] a_list[0] = 9 # Changes a_list to be [9, 4, True] # Looping over a list # Prints each item on a separate line (9, then 4, then True) a_list = [9, 4, True] for item in a_list: print(item) # Length of a list a_list = [9, 4, True] a_list_length = len(a_list) # Returns 3 # Creates a list based on first operation # This will create a list with numbers 0 to 4 a_list = [x for x in range(5)] # This will create a list with multiples of 2 from 0 to 8 list_of_multiples = [2*x for x in range(5)]

# Creates a Series using a list scores = pd.Series([96, 88, 89, 90]) # Creates a Series using a list AND specifying the indices ingredients = pd.Series(["6 ounces", "1 cup", "2 large", "1 cup"], index=["Coffee", "Milk", "Eggs", "Sugar"]) # Creates a series using a Python dictonary. # The key becomes the index. s = {"Los Angeles Dodgers": 2020, "New York Yankees": 2009, "Boston Red Sox": 2018, "Chicago Cubs": 2016, "San Francisco Giants": 2014, "Colorado Rockies": None} world_series = pd.Series(s)

2002 in name_of_series # Returns True or False "mouse" in name_of_series # Returns True or False

# Returns all statistics at one time df.describe() # Or return each measure separately df.mean() df.median() df.mode() df.min() df.max() df.count()

# Returns the variance and the standard deviation df.var() df.std() # Find the range using the max and min values max = people_named_anna.max() min = people_named_anna.min() range = max - min # Find the interquartile range using the first and third # quartile values Q1 = people_named_anna.quantile(0.25) Q3 = people_named_anna.quantile(0.75) IQR = Q3 - Q1

a_dictionary = {key1:value1, key2:value2} # Example: # This dictionary keeps a farm's animal count my_farm = {pigs:2, cows:4} # Creates an empty dictionary a_dictionary = {} # Inserts a key-value pair a_dictionary[key] = value my_farm["horses"] = 1 # The farm now has one horse # Gets a value for a key my_dict[key] # Will return the key my_farm["pigs"] # Will return 2, the value of "pigs" # Using the 'in' keyword my_dict = {"a": 1, "b": 2} print("a" in my_dict) # Returns True print("z" in my_dict) # Returns False print(2 in my_dict) # Returns False, 2 is not a key # Iterating through a dictionary for key in my_dict: print("key: " + str(key)) print("value: " + str(my_dict[key]))

# Creates a DataFrame using a Python dictonary. data = {"mammal": ["African Elephant", "Bottlenose Dolphin", "Cheetah", "Domestic Cat"], "life_span": [70, 25, 14, 16] } mammals = pd.DataFrame(data)

# Returns the data type of each column df.dtypes # Returns the number of rows and columns as (rows, columns) df.shape # Returns summary statistics about each column df.describe() # Returns summary statistics, rounding to one decimal round((df.describe()), 1)

`iloc`

) selects rows and columns by their index location or address in the table.# Returns rows from index location 0 to 1 # and columns from index location 3 to 6 df.iloc[0:2, 3:7]

`loc`

) selects rows and columns by their label or name in the table. # Returns rows with the index 8 through 12 # and columns named "country" and "score" df.loc[8:12, ["country","score"]]

`loc`

to filter for specific values, etc.# Returns only rows with a score higher than 7 # and only the score column df.loc[df.score > 7, ["score"]])

import matplotlib.pyplot as plt

df.plot(kind="box") plt.show()

df["column"].plot(kind="box") plt.show()

df[["column1", "column2"]].plot(kind="box") plt.show()

df.boxplot(column=["column1", "column2"]) plt.show()

import matplotlib.pyplot as plt

df["column1"].plot(kind="hist", title="Histogram") plt.show()

plt.hist(df[["column1", "column2"]]) plt.show()

df.hist(column=["column1", "column2"]) plt.show()

import matplotlib.pyplot as plt

# Groups by a specific column and sums up the total df1 = df.groupby("column1").sum() # Plots using the sums and another column df1.plot.pie(y="column2", labels=df1.index) plt.show()

# Specify the colors used colors = ["lightcoral", "lightskyblue", "gold"] # Set the middle section to "explode" explode = [0, 0.1, 0] # Plot the pie chart using the data frame # Organize it by a specific column # Set a start angle for the text # Display percentages df.plot.pie(y="column", colors=colors, explode=explode, startangle=45, autopct="%1.1f%%") # Move the legend to the best location plt.legend(loc="upper right") plt.show()

import matplotlib.pyplot as plt

df.plot(kind="scatter", x="column1", y="column2") plt.show()

# Sets a color and size (s) for the points df.plot(kind="scatter", x="column1", y="column2", color="orange", s = 10) plt.show()

import matplotlib.pyplot as plt

# Set x1 and y1 x = df.age.loc[df.sex == "f"] y = df.height.loc[df.sex == "f"] # Set x2 and y2 x2 = df.age.loc[df.sex == "m"] y2 = df.height.loc[df.sex == "m"] # Plot and customize each line plt.plot(x1, y1) plt.plot(x2, y2) plt.show()

# Add labels plt.xlabel("Age") plt.ylabel("Height") plt.title("Height of School Children") # Add a legend plt.legend(["Females", "Males"])

import matplotlib.pyplot as plt

# Set color, width, and edgecolor of bars plt.bar(x=df.column1, height=df.column2, width=1, edgecolor="black", color="#EA638C") plt.show()

# Add labels and a title plt.xlabel("Month") plt.ylabel("Temperature (°F)") plt.title("Average GA Temps", fontsize=22) # Adjust grid and rotation of x ticks plt.grid(False) plt.xticks(rotation=45)

# Set the width of the bar bar_width = 0.4 # Plot first dataset plt.bar(x=df.column1, height=df.column2, width=bar_width, color="#EA638C") # Plot second data set. # Add the bar width to the x value so that the bars # do not overlap plt.bar(x=df.column3 + bar_width, height=df.column2, width=bar_width, color="#190E4F") # Add a legend plt.legend(["First Column", "Second Column "]) plt.show()

import matplotlib.pyplot as plt from scipy.stats import norm

`scipy`

in the requirements.txt file# Set data to be the values in a specific column data = df.column # Plot the histogram (w/density) plt.hist(data, bins=10, density=True) plt.show()

# Determine the mean, median and std mean = data.mean() median = data.median() std = data.std() # Set up min and max of the x-axis using the mean and standard deviation xmin = mean - 3 * std xmax = mean + 3 * std # Define the x-axis values x = range(int(xmin), int(xmax)) # "Norm" the y-axis values based on the x-axis values, the mean and the std y = norm.pdf(x, mean, std) # Plot the graph using the x and the y values plt.plot(x, y, color="orange", linewidth=2) plt.show()

pdf = norm.pdf(x_value, mean, std) print(pdf)

cdf = norm.cdf(x_value, mean, std) print(cdf)

more_than_cdf = 1 - norm.cdf(x_value, mean, std) print(more_than_cdf)

import numpy as np import matplotlib.pyplot as plt

# Set the x and y values x = column1 y = column2 # Determine and display the correlation correlation = y.corr(x) print(correlation)

`[ m, b ]`

# Determine the model equation model = np.polyfit(x, y, 1) print(model)

# Create the predict function predict = np.poly1d(model) # Use the predict function value = 60 prediction = predict(value) print(prediction)

# Determine the min and max values of the x-axis print(df.wait_time.min()) print(df.wait_time.max()) # Create the line of best fit # range is based on the min and max values determined above x_lin_reg = range(min, max) y_lin_reg = predict(x_lin_reg) plt.plot(x_lin_reg, y_lin_reg) plt.show()

# If the input is a string. name = input("What is your name? ") # If the input needs to be used as a number include # the term 'int' or 'float' num_one = int(input("Enter a number: ")) num_two = int(input("Enter a second number: ")) num_three = float(input("Enter a third number: "))

if BOOLEAN_EXPRESSION: print("This executes if BOOLEAN_EXPRESSION is True") # Example: # This will only print if the user enters a negative number number = int(input("Enter a number: ")) if number < 0: print(str(number) + " is negative!")

if condition_1: print("This executes if condition_1 evaluates to True") elif condition_2: print("This executes if condition_2 evaluates to True") else: print("This executes if no prior conditions are True") # Example: # This program will print that the color is secondary color == "purple" if color == "red" or color == "blue" or color == "yellow": print("Primary color.") elif color == "green" or color == "orange" or color == "purple": print("Secondary color.") else: print("Not a primary or secondary color.")

# This for loop will print "hello" 5 times for i in range(5): print("hello") # This for loop will print out even numbers 1 through 10 for number in range(2, 11, 2): print(i) # This code executes on each item in my_list # This loop will print 1, then 5, then 10, then 15 my_list = [1, 5, 10, 15] for item in my_list: print(item)

# This program will run as long as the variable 'number' is greater than 0 # Countdown from from 10 to 0 number = 10 while number >= 0: print(number) number -= 1 # You can also use user input to control a while loop # This code will continue running while the user answers ‘Yes’ continue = input("Continue code?: ") while continue == "Yes": continue = input("Continue code?: ")

# Prints a character at a specific index my_string = "hello!" print(my_string[0]) # print("h") print(my_string[5]) # print("!") # Prints all the characters after the specific index my_string = "hello world!" print(my_string[1:]) # print("ello world!") print(my_string[6:]) # prints("world!") # Prints all the characters before the specific index my_string = "hello world!" print(my_string[:6]) # print("hello") print(my_string[:1]) # print("h") # Prints all the characters between the specific indices my_string = "hello world!" print(my_string[1:6]) # print("ello") print(my_string[4:7]) # print("o w") # Iterates through every character in the string # Will print one letter of the string on each line in order my_string = "Turtle" for c in my_string: print(c) # Completes commands if the string is found inside the given string my_string = "hello world!" if "world" in my_string: print("world") # Concatenation my_string = "Tracy the" print(my_string + " turtle") # print(“Tracy the turtle”) # Splits the string into a list of letters my_string = "Tracy" my_list = list(my_string) # my_list = ['T’, ‘r’, ‘a’, ‘c’, ‘y’] # Using enumerate will print the index number followed by a colon and the # word at that index for each word in the list my_string = "Tracy is a turtle" for index, word in enumerate(my_string.split()): print(str(index) + ": " + word)

# upper: To make a string all uppercase my_string = "Hello" my_string = my_string.upper() # returns "HELLO" # lower: To make a string all lowercase my_string = "Hello" my_string = my_string.lower() # returns "hello" # isupper: Returns True if a string is all uppercase letters and False otherwise my_string = "HELLO" print(my_string.isupper()) # returns True # islower: Returns True if a string is all lowercase letters and False otherwise my_string = "Hello" print(my_string.islower()) # returns False # swapcase: Returns a string where each letter is the opposite case from original my_string = "PyThOn" my_string = my_string.swapcase() # returns "pYtHoN" # strip: Returns a copy of the string without any whitespace at beginning or end my_string = " hi there " my_string = my_string.strip() # returns "hi there" # find: Returns the lowest index in the string where substring is found # Returns -1 if substring is not found my_string = "eggplant" index = my_string.find("plant") # returns 3 index = my_string.find("Tracy") # returns -1 # split: Splits the string into a list of words at whitespace my_string = "Tracy is a turtle" my_list = my_string.split() # Returns ['Tracy', 'is', 'a', 'turtle']

df.set_index("column")

df.set_index("column", inplace=True)

df.reset_index(inplace=True)

df["new_column"] = [1, 2, 3, 4, 5, 6]

df["new_column"] = function(df["column1"], df["column2"])

# Import the data df = pd.read_csv (r"data.csv") # Remove max columns limitation and show all columns. pd.set_option("display.max_columns", None)

# Drop unnecessary columns df = df.drop(["column1", "column2"], axis=1)

df.isnull().sum()

# Drop rows that contain missing values df.dropna() # Drop columns that contain missing values df.dropna(axis=1)

# Fill in with a specific value df.fillna(0, inplace=True) # Fill in with the number in the row behind it. df.fillna(method='bfill') # Fill in with the number in the column before it. df.fillna(method='ffill', axis=1)

df.duplicated().sum()

df.loc[df.duplicated()]

df.drop_duplicates(inplace=True)

# Change the data type to a specific data type df.column.astype(data_type) # Change the data type to a float pd.to_numeric(df.column)

# Groups and returns the count df.groupby("value_to_group_by").column.count() # Groups and returns the maximum value in two columns df.groupby("value_to_group_by")[["column1", "column2"]].max() # Groups and returns the min, max and sum of the # values in a column df.groupby("value_to_group_by").column.agg([min, max, sum]) # Groups and returns the sorted list of values in a column df.groupby("value_to_group_by").column.agg([sorted]

# Sort values (increasing/ascending) df.sort_values(by="sorting_value") # Sorts one column of values (decreasing/descending) and # then by another (increasing/ascending) df.sort_values(by=["sort1", "sort2"], ascending=[False, True])

# Concatenating two datasets: # add second data set on as new rows # use the reset_index function to renumber the rows combined_df = pd.concat([df1, df2]).reset_index() # Merging/Joining two datasets: # Merge everything from both data sets pd.merge(df1, df2, on="name", how="outer") # Merge only values that exist in BOTH data sets pd.merge(df1, df2, on="name", how="inner") # Keep everything in the first data set and # merge in matching values from the second pd.merge(df1, df2, on="name", how="left") # Keep everything in the second data set and # merge in matching values from the first pd.merge(df1, df2, on="name", how="right")