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Handling Complex Datasets

if, elif, and else statements

Python has if, elif, and else statements to control the flow of data by running code only under certain conditions.

Conditions are tested top-to-bottom in the following syntax:

if condition1:
   <indented code block>
elif condition2:
   <indented code block> 
else:
   <indented code block>

As soon as one condition is True, Python runs the corresponding indented code block and skips any remaining statements. If no conditions are True, the else code block is run.

# Assign a letter grade given a test score
test_score = 87
if test_score >= 90:
    grade = "A"
elif test_score >= 80:
    grade = "B"
elif test_score >= 70:
    grade = "C"
else:
    grade = "F"
print("Letter Grade:", grade)
# Output: B

Python Iterables

Python iterables are variables that can be fed one piece at a time into iteration processes like loops.

Examples of iterables include:

  • Lists
  • Strings (one letter at a time)
  • Dictionaries
  • pandas objects like Series and DataFrames

On the other hand, variables like Booleans aren’t iterables: a Boolean is True or False, and has no smaller component pieces.

Python for Loops

Python for loops run the same code on each item of a list (or other iterable) in order.

The generic syntax for a for loops is

for temporary_variable in iterable:
    <indented code block>


# For loop that prints numbers times 2
numbers = [1,2,3]
for num in numbers:
  print(num*2)
# Output: 2 4 6

Python while Loops

Python while loops are used to run a code block repetitively until a certain condition is met.

The generic syntax is

while condition:
    <indented code block>

As long as the condition is True, the while loop will keep running the indented code block. If the condition will never become False, a while loop will repeat infinitely (or until the computer crashes!)

# print even numbers from 0 to 10
number = 0
while number < 12:
    print(number)
    number += 2
# Output: 0 2 4 6 8 10

Break and Continue

The break and continue keywords are used to control the execution of code in loops:

  • break ends the loop early
  • continue skips the current iteration of the loop and moves on to the next item

For example, in the code snippet we can search for 3 in numbers by using

  • continue to skip iterations where num != 3
  • break to end the loop if num == 3
# Using continue and break in a for loop
numbers = [1, 2, 3, 4, 5]
for num in numbers:
    if num != 3:
        continue
    elif num == 3:
        print("Found number 3!")
        break
# Output: Found number 3!

range()

The Python range() function generates sequences of numbers like 1,2,3,4,5. The syntax is range(start,stop,step) where

  • start is the first value in the sequence
  • stop is one number beyond the last number
  • step is the number of values to skip at regular intervals or step size

The start and step are optional. The syntax range(stop) generates the sequence of all numbers starting at 0 and stopping before stop. The function list() converts a range to a standard Python list.

# Generate the sequence of numbers 0,1,2
range(3)


# Generate the sequence of numbers 1,2,3,4,5 
range(1,6)


# Generate the even numbers 0,2,4,6
range(0,8,2)

List Comprehension

A list comprehension generates a new list from an iterable, using for loop syntax.

The general syntax for a list comprehension is 

[<calculation with temporary_variable> for temporary_variable in iterable]


# Convert a list of penny costs to dollars
pennies = [1000, 250, 55, 175, 804]
dollars = [amount/100 for amount in pennies]
print(dollars)
# Output: [10.0, 2.5, 0.55, 1.75, 8.04]

Vertical Concatenation

Multiple pandas DataFrames can be combined or stacked along the vertical index axis through a process called vertical concatenation using the syntax

pd.concat(list_of_dataframes)

If the DataFrames have different columns, pandas will insert NaN values to compensate.

Here are the two input DataFrames from the code snippet, and the final output of the concatention:

XY
A B
100 A
200 B
300 A
A B C
400 A True
500 B False
df
A B C
100 A NaN
200 B NaN
300 A NaN
400 A True
500 B False 
X = pd.DataFrame({'A': [100,200,300],
                  'B': ["A", "B", "A"]})


Y = pd.DataFrame({'A': [400, 500],
                  'B': ["A","B"],
                  'C': [True, False]})


df = pd.concat([X, Y])
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