Read Allen Downey's Think Python for getting up to speed with Python 2.7 and computer science topics. It's completely available online, or you can buy a physical copy if you would like.
For quick and easy interactive practice with Python, many people enjoy Codecademy's Python track. There's also Learn Python The Hard Way and The Python Tutorial.
###Q1. Lists & Tuples
How are Python lists and tuples similar and different? Which will work as keys in dictionaries? Why?
Tuples, you can't add/delete objects from it. Lists, you can add/delete/amend/whatever-you-want. Both sequences of elements. For syntax, tuples use parens, lists use brackets. Tuples are much faster for searching. Tuples great for storing contants.
Dictionary keys must be immutable––Tuples are immutable. Lists are disqualified as they can be added/amended/deleted
###Q2. Lists & Sets
How are Python lists and sets similar and different? Give examples of using both. How does performance compare between lists and sets for finding an element. Why?
In sets, there are no duplicate elements. Sets are much faster. In lists, elements can be overwritten, changed, assigned. As there are no duplicate elements, sets are faster. At times (like in 05b), I use sets to find unique values
list_array = ['apple', 'pear', 'orange']
set = {'dali', 'nihilist', 'surrealism'}
###Q3. Lambda Function
Describe Python's lambda. What is it, and what is it used for? Give at least one example, including an example of using a lambda in the key argument to sorted.
lambda allow you to create small anonymous functions...great when when the function is simple and you don't want to write a formal one with all the syntax and such. Especially true when working row/col calcs in Pandas
Lambda example––stripping whitespace from column names in a dataframe(used in 05b-python)
df.rename(columns=lambda x: x.strip())
###Q4. List Comprehension, Map & Filter
Explain list comprehensions. Give examples and show equivalents with map and filter. How do their capabilities compare? Also demonstrate set comprehensions and dictionary comprehensions.
List comprehension are a way to iterate over items in a list. You can also use lambda for this task--especially works well with pandas
# List Comprehension: Count how many p items in large sample >= .18
over_18_count = len([p for p in large_sample if p>= .18])
Maps applies a function to items in a list Works particularly well with lambda
# Map Example
# Square every item in the items array. Myself, I prefer numpy which allows you easy array calcs(below)
items = [1, 2, 3, 4, 5]
list(map((lambda x: x**2), items))
# Numpy example (more elegant, imo)
a = np.array([2,3,4])*2) = [4,6,8]
###Complete the following problems by editing the files below:
###Q5. Datetime
Use Python to compute days between start and stop date.
a.
from datetime import datetime
date_format = '%m-%d-%Y'
date_start = '01-02-2013'
date_stop = '07-28-2015'
def delta_calc(date_start, date_stop, date_format):
a = datetime.strptime(date_start, date_format)
b = datetime.strptime(date_stop, date_format)
delta = b-a
return delta
delta = delta_calc(date_start,date_stop,date_format)
print("{0} days have passed".format(delta.days))
937 days have passed
b.
date_start = '12312013'
date_stop = '05282015'
date_format = "%m%d%Y"
delta = delta_calc(date_start,date_stop,date_format)
print("{0} days have passed".format(delta.days))
513 days have passed
c.
date_start = '15-Jan-1994'
date_stop = '14-Jul-2015'
date_format = '%d-%b-%Y'
delta = datetime.strptime(date_stop, date_format) - datetime.strptime(date_start, date_format)
print("{0} days have passed".format(delta.days))
7,850 days have passed
Place code in this file: q5_datetime.py
Done. I also coded this in Jupyter. Here is the Gist.
###Q6. Strings Edit the 7 functions in q6_strings.py
Ok
###Q7. Lists Edit the 5 functions in q7_lists.py
Ok
###Q8. Parsing Edit the 3 functions in q8_parsing.py